import tensorflow as tf
from tensorflow.keras.layers import Dense, BatchNormalization, Flatten
from tensorflow.keras.callbacks import Callback
from tensorflow.keras.models import Sequential
import tensorflow_model_optimization as tfmot
import numpy as np
import pickle
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector
import os
# os.environ['R_HOME'] = 'C:\Program Files\R\R-4.1.0' #path R installation
itses = importr('itses') # For SparseMAD() as itses()
est_iterative = robjects.r['itses']
class SureMaskedDense(Dense, tfmot.sparsity.keras.PrunableLayer):
def __init__(self, units, **kwargs):
super(SureMaskedDense, self).__init__(units, **kwargs)
self.units = units
self.masks = []
def build(self, input_shape):
super(SureMaskedDense, self).build(input_shape)
for weight in self.get_prunable_weights():
self.masks += [tf.Variable(tf.ones(weight.shape, tf.float32),
trainable=False,
name="kernel_mask")]
def call(self, inputs):
self.mask_weights()
return super(SureMaskedDense, self).call(inputs)
def mask_weights(self):
for mask, weight in zip(self.masks, self.get_prunable_weights()):
weight.assign(tf.squeeze(weight * mask))
def get_prunable_weights(self):
return [self.kernel]
def hard_threshold(weight, mask, sparsity):
weights_r = tf.reshape(weight, [-1]).numpy()
sparsity = sparsity[1].numpy()
upper_percentile = np.percentile(np.abs(weights_r), sparsity*100)
print("Wanted sparsity", sparsity)
print("Upper percentile", upper_percentile)
try:
weights_r = FloatVector(weights_r)
sparsity = FloatVector([sparsity])
current_threshold = est_iterative(weights_r, method="HT", sparsity=sparsity)[0][0]
print("Thresholhold", current_threshold)
old_mask = False
if(upper_percentile < current_threshold):
print("Threshold over percentile. Lowering.")
current_threshold = upper_percentile
else:
print("Using suggest threshold.")
except:
print("Keeping old")
old_mask = True
weight = mask * weight
if not old_mask:
print("Applying new mask")
abs_weight = tf.math.abs(weight)
new_mask = tf.logical_not(tf.math.greater_equal(abs_weight, current_threshold))
mask = tf.cast(1. - mask, tf.bool)
mask = tf.cast(tf.logical_not(tf.math.logical_or(mask, new_mask)), weight.dtype)
print("Percentage zeros", tf.math.reduce_mean(1. - mask).numpy())
print(mask)
weight = weight * mask
return weight, mask
class ShrinkCallback(Callback):
def __init__(self, schedule, gradient_adjusment=False, data = None):
super(ShrinkCallback, self).__init__()
self.schedule = schedule
self.gradient_adjusment = gradient_adjusment
self.steps = 0
self.data = data
def prune(self, epoch, weights, masks, tape=None, loss=None, epsilon=1e-12):
new_masks = []
sparsity = self.schedule(epoch)
for weight, mask in zip(weights, masks):
if self.gradient_adjusment:
grad = tape.gradient(loss, weight)
weight = weight / (tf.math.abs(grad) + epsilon)
new_weight, mask = hard_threshold(weight, mask, sparsity=sparsity)
new_weight = new_weight * (tf.math.abs(grad) + epsilon)
else:
new_weight, mask = hard_threshold(weight, mask, sparsity=sparsity)
weight.assign(new_weight)
new_masks += [mask]
return new_masks
def on_train_begin(self, logs = None):
self.steps = 0
def on_batch_begin(self, batch, logs=None):
self.steps += 1
if self.schedule(self.steps)[0]:
if self.gradient_adjusment:
for batch in self.data.take(1):
x = batch[0]
y = batch[1]
with tf.GradientTape(persistent=True) as tape:
y_pred = self.model(x)
loss = self.model.loss(y, y_pred)
else:
tape = None
loss = None
for layer in self.model.layers:
if hasattr(layer, 'masks'):
layer.mask_weights()
weights = layer.get_prunable_weights()
new_masks = self.prune(self.steps, weights, layer.masks, tape=tape, loss=loss)
for new_mask, old_mask in zip(new_masks, layer.masks):
old_mask.assign(new_mask)
layer.mask_weights()
if self.gradient_adjusment:
del tape
return
class SparsityCallback(Callback):
def get_sparsity(self):
weights_list = []
for layer in self.model.layers:
if isinstance(layer, tf.keras.layers.Wrapper):
layer = layer.layer
if isinstance(layer, tfmot.sparsity.keras.PrunableLayer):
weights = layer.get_prunable_weights()
weights_list += [tf.reshape(weight,-1).numpy() for weight in weights]
weights_list = np.concatenate(weights_list)
print(weights_list)
return np.mean(weights_list == 0)
def on_epoch_end(self, epoch, logs = None):
sparsity = self.get_sparsity()
logs["sparsity"] = sparsity
print("Sparsity at:", sparsity)
def get_data(batch_size, buffer_size = 64):
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
def mapper(x_train, y_train):
x_train = tf.cast(x_train, tf.float32)
y_train = tf.cast(y_train, tf.int32)
x_train = x_train/255.
return x_train, y_train
train_dataset = train_dataset.map(mapper).shuffle(buffer_size).batch(batch_size)
test_dataset = test_dataset.map(mapper).shuffle(buffer_size).batch(batch_size)
return train_dataset, test_dataset
def get_mnist_model(kernel_regularizer = 'l2', batch_norm = True):
if batch_norm:
mnist_model = Sequential([
Flatten(input_shape = (28, 28)),
SureMaskedDense(300, activation = "relu",kernel_regularizer=kernel_regularizer),
BatchNormalization(),
SureMaskedDense(100, activation = "relu",kernel_regularizer=kernel_regularizer),
BatchNormalization(),
SureMaskedDense(10, activation = "softmax",kernel_regularizer=kernel_regularizer)])
else:
mnist_model = Sequential([
Flatten(input_shape = (28, 28)),
SureMaskedDense(64, activation = "tanh",kernel_regularizer=kernel_regularizer),
SureMaskedDense(128, activation = "tanh",kernel_regularizer=kernel_regularizer),
SureMaskedDense(10, activation = "softmax",kernel_regularizer=kernel_regularizer)])
return mnist_model
def compile_model(model):
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
optimizer=tf.keras.optimizers.Adam(0.001),
metrics=['accuracy'])
# Train base
epochs = 200
batch_size = 256
train_dataset, test_dataset = get_data(batch_size)
num_batches = len(list(train_dataset))
end_pruning_epoch = 450
pruning_epoch_frequency = 50
prune_epochs = 500
schedule = tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,
final_sparsity=0.99,
begin_step=1,
end_step= num_batches * end_pruning_epoch,
frequency = num_batches*pruning_epoch_frequency)
def get_base_model(epochs,batch_size, train_dataset, test_dataset, kernel_regularizer, batch_norm, seed = 1234):
tf.random.set_seed(seed)
mnist_model = get_mnist_model(kernel_regularizer = kernel_regularizer, batch_norm = batch_norm)
compile_model(mnist_model)
mnist_model.fit(train_dataset, epochs = epochs, validation_data = test_dataset)
return mnist_model
def iterative_pruning(prune_epcochs, train_dataset, test_dataset, schedule, original_model, seed = 1234):
iterative_mnist_model = tf.keras.models.clone_model(original_model)
compile_model(iterative_mnist_model)
sparsity_callback = SparsityCallback()
shrink_callback = ShrinkCallback(schedule)
tf.random.set_seed(seed)
history_iterative_prune = iterative_mnist_model.fit(train_dataset,
epochs = prune_epcochs,
validation_data = test_dataset,
callbacks = [shrink_callback, sparsity_callback])
return iterative_mnist_model, history_iterative_prune
def apply_pruning_to_dense(layer, pruning_schedule = schedule):
if isinstance(layer, tf.keras.layers.Dense):
return tfmot.sparsity.keras.prune_low_magnitude(layer, pruning_schedule = pruning_schedule)
return layer
def magnitude_pruning(prune_epcochs, train_dataset, test_dataset, schedule, original_model, seed = 1234):
tf.random.set_seed(seed)
magnitude_mnist_model = tf.keras.models.clone_model(
original_model,
clone_function=apply_pruning_to_dense,
)
compile_model(magnitude_mnist_model)
sparsity_callback = SparsityCallback()
callbacks = [
tfmot.sparsity.keras.UpdatePruningStep(),
sparsity_callback
]
history_magnitude_prune = magnitude_mnist_model.fit(train_dataset,
epochs = prune_epcochs,
validation_data = test_dataset,
callbacks = callbacks)
return magnitude_mnist_model, history_magnitude_prune
for j in [1,2,3,4,5]:
l2_base_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, "l2", batch_norm = True, seed = j)
base_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, None, batch_norm = True, seed = j)
l2_base_no_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, "l2", batch_norm = False, seed = j)
base_no_batch = get_base_model(epochs,batch_size, train_dataset, test_dataset, None, batch_norm = False, seed = j)
models = [l2_base_batch, base_batch, l2_base_no_batch, base_no_batch]
iterative_histories = []
iterative_models = []
for model in models:
model, history = iterative_pruning(prune_epochs, train_dataset, test_dataset, schedule, model, seed = j)
iterative_models += [model]
iterative_histories += [history.history]
with open('output/neural-network-pruning/pickle-jar/iterative_histories'+str(j)+'.pickle', 'wb') as file:
pickle.dump(iterative_histories, file)
magnitude_histories = []
magnitude_models = []
for model in models:
model, history = magnitude_pruning(prune_epochs, train_dataset, test_dataset, schedule, model, seed = j)
magnitude_models += [model]
magnitude_histories += [history.history]
with open('output/neural-network-pruning/pickle-jar/magnitude_histories'+str(j)+'.pickle', 'wb') as file:
pickle.dump(magnitude_histories, file)
Epoch 1/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1850 - accuracy: 0.9255 - val_loss: 1.5350 - val_accuracy: 0.9102 Epoch 2/200 235/235 [==============================] - 4s 16ms/step - loss: 0.4388 - accuracy: 0.9595 - val_loss: 0.4665 - val_accuracy: 0.9536 Epoch 3/200 235/235 [==============================] - 4s 15ms/step - loss: 0.3123 - accuracy: 0.9637 - val_loss: 0.3425 - val_accuracy: 0.9459 Epoch 4/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2770 - accuracy: 0.9654 - val_loss: 0.3068 - val_accuracy: 0.9525 Epoch 5/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2603 - accuracy: 0.9667 - val_loss: 0.2895 - val_accuracy: 0.9511 Epoch 6/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2447 - accuracy: 0.9683 - val_loss: 0.2654 - val_accuracy: 0.9594 Epoch 7/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2345 - accuracy: 0.9699 - val_loss: 0.2728 - val_accuracy: 0.9549 Epoch 8/200 235/235 [==============================] - 4s 16ms/step - loss: 0.2302 - accuracy: 0.9691 - val_loss: 0.2528 - val_accuracy: 0.9604 Epoch 9/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2189 - accuracy: 0.9705 - val_loss: 0.2361 - val_accuracy: 0.9655 Epoch 10/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2143 - accuracy: 0.9703 - val_loss: 0.2403 - val_accuracy: 0.9626 Epoch 11/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2095 - accuracy: 0.9713 - val_loss: 0.2408 - val_accuracy: 0.9599 Epoch 12/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2071 - accuracy: 0.9718 - val_loss: 0.2392 - val_accuracy: 0.9593 Epoch 13/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2012 - accuracy: 0.9726 - val_loss: 0.2423 - val_accuracy: 0.9596 Epoch 14/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1952 - accuracy: 0.9732 - val_loss: 0.2257 - val_accuracy: 0.9632 Epoch 15/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1966 - accuracy: 0.9720 - val_loss: 0.2357 - val_accuracy: 0.9592 Epoch 16/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1915 - accuracy: 0.9731 - val_loss: 0.2331 - val_accuracy: 0.9592 Epoch 17/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1931 - accuracy: 0.9715 - val_loss: 0.2196 - val_accuracy: 0.9641 Epoch 18/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1860 - accuracy: 0.9738 - val_loss: 0.2182 - val_accuracy: 0.9624 Epoch 19/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1869 - accuracy: 0.9722 - val_loss: 0.2249 - val_accuracy: 0.9619 Epoch 20/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1829 - accuracy: 0.9731 - val_loss: 0.2106 - val_accuracy: 0.9653 Epoch 21/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1791 - accuracy: 0.9742 - val_loss: 0.2444 - val_accuracy: 0.9530 Epoch 22/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1819 - accuracy: 0.9734 - val_loss: 0.2232 - val_accuracy: 0.9612 Epoch 23/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1799 - accuracy: 0.9740 - val_loss: 0.2191 - val_accuracy: 0.9614 Epoch 24/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1783 - accuracy: 0.9737 - val_loss: 0.2106 - val_accuracy: 0.9643 Epoch 25/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1751 - accuracy: 0.9749 - val_loss: 0.2110 - val_accuracy: 0.9644 Epoch 26/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1744 - accuracy: 0.9746 - val_loss: 0.2312 - val_accuracy: 0.9572 Epoch 27/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1730 - accuracy: 0.9746 - val_loss: 0.2244 - val_accuracy: 0.9615 Epoch 28/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1745 - accuracy: 0.9735 - val_loss: 0.2129 - val_accuracy: 0.9617 Epoch 29/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1732 - accuracy: 0.9739 - val_loss: 0.2239 - val_accuracy: 0.9583 Epoch 30/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1714 - accuracy: 0.9744 - val_loss: 0.2074 - val_accuracy: 0.9644 Epoch 31/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1692 - accuracy: 0.9751 - val_loss: 0.2304 - val_accuracy: 0.9572 Epoch 32/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1702 - accuracy: 0.9746 - val_loss: 0.2422 - val_accuracy: 0.9530 Epoch 33/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1722 - accuracy: 0.9737 - val_loss: 0.2141 - val_accuracy: 0.9623 Epoch 34/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1637 - accuracy: 0.9757 - val_loss: 0.2165 - val_accuracy: 0.9610 Epoch 35/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1676 - accuracy: 0.9745 - val_loss: 0.2252 - val_accuracy: 0.9569 Epoch 36/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1669 - accuracy: 0.9749 - val_loss: 0.2298 - val_accuracy: 0.9597 Epoch 37/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1667 - accuracy: 0.9759 - val_loss: 0.2230 - val_accuracy: 0.9584 Epoch 38/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1636 - accuracy: 0.9753 - val_loss: 0.2309 - val_accuracy: 0.9541 Epoch 39/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1626 - accuracy: 0.9764 - val_loss: 0.2274 - val_accuracy: 0.9569 Epoch 40/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1647 - accuracy: 0.9751 - val_loss: 0.2323 - val_accuracy: 0.9553 Epoch 41/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1636 - accuracy: 0.9754 - val_loss: 0.2395 - val_accuracy: 0.9531 Epoch 42/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1653 - accuracy: 0.9751 - val_loss: 0.2211 - val_accuracy: 0.9616 Epoch 43/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1621 - accuracy: 0.9759 - val_loss: 0.2158 - val_accuracy: 0.9601 Epoch 44/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1603 - accuracy: 0.9768 - val_loss: 0.2527 - val_accuracy: 0.9481 Epoch 45/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1635 - accuracy: 0.9755 - val_loss: 0.2153 - val_accuracy: 0.9621 Epoch 46/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1620 - accuracy: 0.9764 - val_loss: 0.1961 - val_accuracy: 0.9670 Epoch 47/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1629 - accuracy: 0.9753 - val_loss: 0.2121 - val_accuracy: 0.9618 Epoch 48/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1608 - accuracy: 0.9759 - val_loss: 0.2367 - val_accuracy: 0.9557 Epoch 49/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1583 - accuracy: 0.9772 - val_loss: 0.2241 - val_accuracy: 0.9558 Epoch 50/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1582 - accuracy: 0.9767 - val_loss: 0.2063 - val_accuracy: 0.9619 Epoch 51/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1582 - accuracy: 0.9763 - val_loss: 0.2034 - val_accuracy: 0.9636 Epoch 52/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1572 - accuracy: 0.9773 - val_loss: 0.2067 - val_accuracy: 0.9611 Epoch 53/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1602 - accuracy: 0.9756 - val_loss: 0.2121 - val_accuracy: 0.9622 Epoch 54/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1578 - accuracy: 0.9770 - val_loss: 0.2315 - val_accuracy: 0.9571 Epoch 55/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1607 - accuracy: 0.9757 - val_loss: 0.2132 - val_accuracy: 0.9588 Epoch 56/200 235/235 [==============================] - 5s 19ms/step - loss: 0.1584 - accuracy: 0.9761 - val_loss: 0.2275 - val_accuracy: 0.9588 Epoch 57/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1588 - accuracy: 0.9760 - val_loss: 0.1913 - val_accuracy: 0.9672 Epoch 58/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1558 - accuracy: 0.9768 - val_loss: 0.2000 - val_accuracy: 0.9638 Epoch 59/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9773 - val_loss: 0.2128 - val_accuracy: 0.9612 Epoch 60/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1564 - accuracy: 0.9762 - val_loss: 0.2160 - val_accuracy: 0.9602 Epoch 61/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1538 - accuracy: 0.9771 - val_loss: 0.2297 - val_accuracy: 0.9566 Epoch 62/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1571 - accuracy: 0.9765 - val_loss: 0.2174 - val_accuracy: 0.9600 Epoch 63/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1560 - accuracy: 0.9768 - val_loss: 0.2252 - val_accuracy: 0.9568 Epoch 64/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1566 - accuracy: 0.9765 - val_loss: 0.2147 - val_accuracy: 0.9599 Epoch 65/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1546 - accuracy: 0.9766 - val_loss: 0.1942 - val_accuracy: 0.9649 Epoch 66/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1591 - accuracy: 0.9756 - val_loss: 0.1933 - val_accuracy: 0.9668 Epoch 67/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1583 - accuracy: 0.9760 - val_loss: 0.2080 - val_accuracy: 0.9625 Epoch 68/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1552 - accuracy: 0.9771 - val_loss: 0.2337 - val_accuracy: 0.9552 Epoch 69/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1558 - accuracy: 0.9763 - val_loss: 0.2177 - val_accuracy: 0.9578 Epoch 70/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1533 - accuracy: 0.9768 - val_loss: 0.2275 - val_accuracy: 0.9568 Epoch 71/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1533 - accuracy: 0.9773 - val_loss: 0.2447 - val_accuracy: 0.9499 Epoch 72/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1572 - accuracy: 0.9756 - val_loss: 0.2073 - val_accuracy: 0.9604 Epoch 73/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1540 - accuracy: 0.9765 - val_loss: 0.1984 - val_accuracy: 0.9630 Epoch 74/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1542 - accuracy: 0.9762 - val_loss: 0.2074 - val_accuracy: 0.9605 Epoch 75/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1529 - accuracy: 0.9776 - val_loss: 0.1973 - val_accuracy: 0.9636 Epoch 76/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1517 - accuracy: 0.9775 - val_loss: 0.2038 - val_accuracy: 0.9614 Epoch 77/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1522 - accuracy: 0.9778 - val_loss: 0.2000 - val_accuracy: 0.9651 Epoch 78/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1502 - accuracy: 0.9780 - val_loss: 0.2018 - val_accuracy: 0.9637 Epoch 79/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1530 - accuracy: 0.9763 - val_loss: 0.2123 - val_accuracy: 0.9588 Epoch 80/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1512 - accuracy: 0.9772 - val_loss: 0.1935 - val_accuracy: 0.9650 Epoch 81/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1512 - accuracy: 0.9777 - val_loss: 0.1965 - val_accuracy: 0.9651 Epoch 82/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1524 - accuracy: 0.9778 - val_loss: 0.2085 - val_accuracy: 0.9610 Epoch 83/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1547 - accuracy: 0.9766 - val_loss: 0.1900 - val_accuracy: 0.9665 Epoch 84/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1519 - accuracy: 0.9770 - val_loss: 0.2246 - val_accuracy: 0.9571 Epoch 85/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1480 - accuracy: 0.9783 - val_loss: 0.2230 - val_accuracy: 0.9568 Epoch 86/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1555 - accuracy: 0.9760 - val_loss: 0.2225 - val_accuracy: 0.9568 Epoch 87/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1518 - accuracy: 0.9768 - val_loss: 0.1952 - val_accuracy: 0.9649 Epoch 88/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1510 - accuracy: 0.9773 - val_loss: 0.2074 - val_accuracy: 0.9620 Epoch 89/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1480 - accuracy: 0.9779 - val_loss: 0.2077 - val_accuracy: 0.9612 Epoch 90/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1503 - accuracy: 0.9766 - val_loss: 0.2383 - val_accuracy: 0.9535 Epoch 91/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1496 - accuracy: 0.9771 - val_loss: 0.2005 - val_accuracy: 0.9625 Epoch 92/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1489 - accuracy: 0.9771 - val_loss: 0.1898 - val_accuracy: 0.9655 Epoch 93/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9779 - val_loss: 0.2269 - val_accuracy: 0.9557 Epoch 94/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1505 - accuracy: 0.9776 - val_loss: 0.2140 - val_accuracy: 0.9599 Epoch 95/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1504 - accuracy: 0.9771 - val_loss: 0.2064 - val_accuracy: 0.9601 Epoch 96/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1472 - accuracy: 0.9780 - val_loss: 0.2336 - val_accuracy: 0.9541 Epoch 97/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9780 - val_loss: 0.2234 - val_accuracy: 0.9550 Epoch 98/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9784 - val_loss: 0.2542 - val_accuracy: 0.9497 Epoch 99/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1530 - accuracy: 0.9760 - val_loss: 0.2299 - val_accuracy: 0.9537 Epoch 100/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1523 - accuracy: 0.9772 - val_loss: 0.2204 - val_accuracy: 0.9566 Epoch 101/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1512 - accuracy: 0.9774 - val_loss: 0.2180 - val_accuracy: 0.9576 Epoch 102/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1492 - accuracy: 0.9772 - val_loss: 0.1989 - val_accuracy: 0.9642 Epoch 103/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1475 - accuracy: 0.9782 - val_loss: 0.2135 - val_accuracy: 0.9595 Epoch 104/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9777 - val_loss: 0.1986 - val_accuracy: 0.9616 Epoch 105/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1462 - accuracy: 0.9782 - val_loss: 0.1918 - val_accuracy: 0.9644 Epoch 106/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2034 - val_accuracy: 0.9622 Epoch 107/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9778 - val_loss: 0.2156 - val_accuracy: 0.9578 Epoch 108/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1485 - accuracy: 0.9774 - val_loss: 0.2294 - val_accuracy: 0.9558 Epoch 109/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9780 - val_loss: 0.2078 - val_accuracy: 0.9604 Epoch 110/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9785 - val_loss: 0.1991 - val_accuracy: 0.9631 Epoch 111/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9779 - val_loss: 0.1891 - val_accuracy: 0.9662 Epoch 112/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1433 - accuracy: 0.9789 - val_loss: 0.1992 - val_accuracy: 0.9637 Epoch 113/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1500 - accuracy: 0.9766 - val_loss: 0.2103 - val_accuracy: 0.9604 Epoch 114/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1455 - accuracy: 0.9785 - val_loss: 0.2115 - val_accuracy: 0.9611 Epoch 115/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9773 - val_loss: 0.2173 - val_accuracy: 0.9559 Epoch 116/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1473 - accuracy: 0.9775 - val_loss: 0.2280 - val_accuracy: 0.9569 Epoch 117/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9780 - val_loss: 0.2039 - val_accuracy: 0.9600 Epoch 118/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1456 - accuracy: 0.9778 - val_loss: 0.2392 - val_accuracy: 0.9543 Epoch 119/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1460 - accuracy: 0.9786 - val_loss: 0.2284 - val_accuracy: 0.9528 Epoch 120/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1478 - accuracy: 0.9776 - val_loss: 0.2207 - val_accuracy: 0.9581 Epoch 121/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1455 - accuracy: 0.9782 - val_loss: 0.1977 - val_accuracy: 0.9638 Epoch 122/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9776 - val_loss: 0.2137 - val_accuracy: 0.9597 Epoch 123/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9794 - val_loss: 0.2329 - val_accuracy: 0.9525 Epoch 124/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1447 - accuracy: 0.9778 - val_loss: 0.1982 - val_accuracy: 0.9660 Epoch 125/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1408 - accuracy: 0.9794 - val_loss: 0.2049 - val_accuracy: 0.9603 Epoch 126/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1448 - accuracy: 0.9788 - val_loss: 0.1992 - val_accuracy: 0.9625 Epoch 127/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1443 - accuracy: 0.9784 - val_loss: 0.2209 - val_accuracy: 0.9587 Epoch 128/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1444 - accuracy: 0.9786 - val_loss: 0.2193 - val_accuracy: 0.9574 Epoch 129/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1465 - accuracy: 0.9778 - val_loss: 0.1955 - val_accuracy: 0.9670 Epoch 130/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1441 - accuracy: 0.9786 - val_loss: 0.1928 - val_accuracy: 0.9646 Epoch 131/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1464 - accuracy: 0.9780 - val_loss: 0.2059 - val_accuracy: 0.9631 Epoch 132/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1423 - accuracy: 0.9787 - val_loss: 0.2376 - val_accuracy: 0.9503 Epoch 133/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1475 - accuracy: 0.9777 - val_loss: 0.1940 - val_accuracy: 0.9643 Epoch 134/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1457 - accuracy: 0.9780 - val_loss: 0.2448 - val_accuracy: 0.9506 Epoch 135/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1451 - accuracy: 0.9779 - val_loss: 0.2208 - val_accuracy: 0.9560 Epoch 136/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1444 - accuracy: 0.9786 - val_loss: 0.2308 - val_accuracy: 0.9522 Epoch 137/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1459 - accuracy: 0.9782 - val_loss: 0.2108 - val_accuracy: 0.9592 Epoch 138/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1415 - accuracy: 0.9792 - val_loss: 0.2005 - val_accuracy: 0.9629 Epoch 139/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9777 - val_loss: 0.2040 - val_accuracy: 0.9606 Epoch 140/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1461 - accuracy: 0.9777 - val_loss: 0.2290 - val_accuracy: 0.9556 Epoch 141/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1450 - accuracy: 0.9783 - val_loss: 0.1960 - val_accuracy: 0.9664 Epoch 142/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.1830 - val_accuracy: 0.9671 Epoch 143/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1406 - accuracy: 0.9795 - val_loss: 0.2100 - val_accuracy: 0.9609 Epoch 144/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1458 - accuracy: 0.9780 - val_loss: 0.1907 - val_accuracy: 0.9653 Epoch 145/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1444 - accuracy: 0.9781 - val_loss: 0.2211 - val_accuracy: 0.9570 Epoch 146/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1418 - accuracy: 0.9788 - val_loss: 0.2140 - val_accuracy: 0.9589 Epoch 147/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1452 - accuracy: 0.9777 - val_loss: 0.2107 - val_accuracy: 0.9584 Epoch 148/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1450 - accuracy: 0.9786 - val_loss: 0.1987 - val_accuracy: 0.9624 Epoch 149/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1453 - accuracy: 0.9776 - val_loss: 0.2217 - val_accuracy: 0.9565 Epoch 150/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1465 - accuracy: 0.9775 - val_loss: 0.2122 - val_accuracy: 0.9593 Epoch 151/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1433 - accuracy: 0.9787 - val_loss: 0.2099 - val_accuracy: 0.9583 Epoch 152/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1429 - accuracy: 0.9781 - val_loss: 0.2721 - val_accuracy: 0.9404 Epoch 153/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1477 - accuracy: 0.9774 - val_loss: 0.1997 - val_accuracy: 0.9591 Epoch 154/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1398 - accuracy: 0.9796 - val_loss: 0.2000 - val_accuracy: 0.9616 Epoch 155/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1426 - accuracy: 0.9780 - val_loss: 0.2272 - val_accuracy: 0.9545 Epoch 156/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1470 - accuracy: 0.9769 - val_loss: 0.1989 - val_accuracy: 0.9646 Epoch 157/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1419 - accuracy: 0.9789 - val_loss: 0.2099 - val_accuracy: 0.9604 Epoch 158/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9789 - val_loss: 0.2022 - val_accuracy: 0.9625 Epoch 159/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1410 - accuracy: 0.9787 - val_loss: 0.1942 - val_accuracy: 0.9684 Epoch 160/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1460 - accuracy: 0.9782 - val_loss: 0.2074 - val_accuracy: 0.9600 Epoch 161/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1408 - accuracy: 0.9788 - val_loss: 0.2110 - val_accuracy: 0.9608 Epoch 162/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1411 - accuracy: 0.9792 - val_loss: 0.1972 - val_accuracy: 0.9615 Epoch 163/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1449 - accuracy: 0.9780 - val_loss: 0.2182 - val_accuracy: 0.9592 Epoch 164/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1439 - accuracy: 0.9785 - val_loss: 0.2106 - val_accuracy: 0.9578 Epoch 165/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1432 - accuracy: 0.9776 - val_loss: 0.2197 - val_accuracy: 0.9569 Epoch 166/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9789 - val_loss: 0.2058 - val_accuracy: 0.9596 Epoch 167/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1414 - accuracy: 0.9784 - val_loss: 0.1894 - val_accuracy: 0.9662 Epoch 168/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1456 - accuracy: 0.9777 - val_loss: 0.2164 - val_accuracy: 0.9581 Epoch 169/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1422 - accuracy: 0.9781 - val_loss: 0.2028 - val_accuracy: 0.9615 Epoch 170/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1446 - accuracy: 0.9779 - val_loss: 0.2243 - val_accuracy: 0.9567 Epoch 171/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1423 - accuracy: 0.9791 - val_loss: 0.1984 - val_accuracy: 0.9622 Epoch 172/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1423 - accuracy: 0.9779 - val_loss: 0.1883 - val_accuracy: 0.9677 Epoch 173/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9785 - val_loss: 0.1981 - val_accuracy: 0.9654 Epoch 174/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1398 - accuracy: 0.9788 - val_loss: 0.2105 - val_accuracy: 0.9567 Epoch 175/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1401 - accuracy: 0.9791 - val_loss: 0.2116 - val_accuracy: 0.9603 Epoch 176/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9802 - val_loss: 0.2492 - val_accuracy: 0.9471 Epoch 177/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1425 - accuracy: 0.9772 - val_loss: 0.1953 - val_accuracy: 0.9653 Epoch 178/200 235/235 [==============================] - 4s 18ms/step - loss: 0.1428 - accuracy: 0.9780 - val_loss: 0.2331 - val_accuracy: 0.9557 Epoch 179/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1432 - accuracy: 0.9785 - val_loss: 0.2046 - val_accuracy: 0.9626 Epoch 180/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1424 - accuracy: 0.9784 - val_loss: 0.1952 - val_accuracy: 0.9649 Epoch 181/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9784 - val_loss: 0.1815 - val_accuracy: 0.9684 Epoch 182/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2220 - val_accuracy: 0.9568 Epoch 183/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2043 - val_accuracy: 0.9631 Epoch 184/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1392 - accuracy: 0.9789 - val_loss: 0.2293 - val_accuracy: 0.9538 Epoch 185/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1390 - accuracy: 0.9795 - val_loss: 0.2020 - val_accuracy: 0.9614 Epoch 186/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9784 - val_loss: 0.1947 - val_accuracy: 0.9643 Epoch 187/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9785 - val_loss: 0.2100 - val_accuracy: 0.9587 Epoch 188/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1402 - accuracy: 0.9789 - val_loss: 0.1805 - val_accuracy: 0.9680 Epoch 189/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1424 - accuracy: 0.9785 - val_loss: 0.2356 - val_accuracy: 0.9497 Epoch 190/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1419 - accuracy: 0.9782 - val_loss: 0.1840 - val_accuracy: 0.9672 Epoch 191/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1449 - accuracy: 0.9776 - val_loss: 0.2659 - val_accuracy: 0.9428 Epoch 192/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1391 - accuracy: 0.9791 - val_loss: 0.1957 - val_accuracy: 0.9618 Epoch 193/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1397 - accuracy: 0.9785 - val_loss: 0.1848 - val_accuracy: 0.9671 Epoch 194/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1403 - accuracy: 0.9783 - val_loss: 0.2444 - val_accuracy: 0.9485 Epoch 195/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1415 - accuracy: 0.9785 - val_loss: 0.2172 - val_accuracy: 0.9553 Epoch 196/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1428 - accuracy: 0.9778 - val_loss: 0.1856 - val_accuracy: 0.9667 Epoch 197/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9808 - val_loss: 0.2033 - val_accuracy: 0.9627 Epoch 198/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1379 - accuracy: 0.9791 - val_loss: 0.2216 - val_accuracy: 0.9544 Epoch 199/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1408 - accuracy: 0.9791 - val_loss: 0.2036 - val_accuracy: 0.9605 Epoch 200/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1381 - accuracy: 0.9796 - val_loss: 0.1780 - val_accuracy: 0.9682 Epoch 1/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2490 - accuracy: 0.9266 - val_loss: 0.2152 - val_accuracy: 0.9551 Epoch 2/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0887 - accuracy: 0.9741 - val_loss: 0.1027 - val_accuracy: 0.9669 Epoch 3/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0507 - accuracy: 0.9859 - val_loss: 0.0945 - val_accuracy: 0.9689 Epoch 4/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0310 - accuracy: 0.9918 - val_loss: 0.0894 - val_accuracy: 0.9728 Epoch 5/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0198 - accuracy: 0.9953 - val_loss: 0.0912 - val_accuracy: 0.9734 Epoch 6/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0133 - accuracy: 0.9971 - val_loss: 0.0863 - val_accuracy: 0.9759 Epoch 7/200 235/235 [==============================] - 4s 17ms/step - loss: 0.0119 - accuracy: 0.9972 - val_loss: 0.0850 - val_accuracy: 0.9780 Epoch 8/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0116 - accuracy: 0.9969 - val_loss: 0.1076 - val_accuracy: 0.9714 Epoch 9/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0100 - accuracy: 0.9972 - val_loss: 0.1027 - val_accuracy: 0.9744 Epoch 10/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 0.0968 - val_accuracy: 0.9742 Epoch 11/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0092 - accuracy: 0.9972 - val_loss: 0.0997 - val_accuracy: 0.9754 Epoch 12/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0083 - accuracy: 0.9974 - val_loss: 0.1004 - val_accuracy: 0.9751 Epoch 13/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0110 - accuracy: 0.9964 - val_loss: 0.1186 - val_accuracy: 0.9720 Epoch 14/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0071 - accuracy: 0.9980 - val_loss: 0.0933 - val_accuracy: 0.9774 Epoch 15/200 235/235 [==============================] - 4s 17ms/step - loss: 0.0039 - accuracy: 0.9991 - val_loss: 0.0837 - val_accuracy: 0.9804 Epoch 16/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0832 - val_accuracy: 0.9807 Epoch 17/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 0.0793 - val_accuracy: 0.9815 Epoch 18/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0765 - val_accuracy: 0.9823 Epoch 19/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 0.1480 - val_accuracy: 0.9669 Epoch 20/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0206 - accuracy: 0.9933 - val_loss: 0.1313 - val_accuracy: 0.9703 Epoch 21/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0128 - accuracy: 0.9955 - val_loss: 0.0908 - val_accuracy: 0.9787 Epoch 22/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0067 - accuracy: 0.9979 - val_loss: 0.0826 - val_accuracy: 0.9789 Epoch 23/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.0768 - val_accuracy: 0.9815 Epoch 24/200 235/235 [==============================] - 4s 15ms/step - loss: 9.0077e-04 - accuracy: 0.9999 - val_loss: 0.0708 - val_accuracy: 0.9827 Epoch 25/200 235/235 [==============================] - 3s 15ms/step - loss: 5.2012e-04 - accuracy: 0.9999 - val_loss: 0.0698 - val_accuracy: 0.9838 Epoch 26/200 235/235 [==============================] - 4s 15ms/step - loss: 2.9209e-04 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9832 Epoch 27/200 235/235 [==============================] - 4s 15ms/step - loss: 1.8796e-04 - accuracy: 1.0000 - val_loss: 0.0701 - val_accuracy: 0.9834 Epoch 28/200 235/235 [==============================] - 4s 17ms/step - loss: 1.2218e-04 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9837 Epoch 29/200 235/235 [==============================] - 4s 17ms/step - loss: 1.0851e-04 - accuracy: 1.0000 - val_loss: 0.0709 - val_accuracy: 0.9834 Epoch 30/200 235/235 [==============================] - 4s 15ms/step - loss: 1.1920e-04 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9839 Epoch 31/200 235/235 [==============================] - 4s 15ms/step - loss: 7.7386e-05 - accuracy: 1.0000 - val_loss: 0.0711 - val_accuracy: 0.9840 Epoch 32/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1824 - val_accuracy: 0.9625 Epoch 33/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0379 - accuracy: 0.9881 - val_loss: 0.0997 - val_accuracy: 0.9771 Epoch 34/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.0787 - val_accuracy: 0.9801 Epoch 35/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0748 - val_accuracy: 0.9821 Epoch 36/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0711 - val_accuracy: 0.9829 Epoch 37/200 235/235 [==============================] - 4s 15ms/step - loss: 5.5592e-04 - accuracy: 0.9999 - val_loss: 0.0709 - val_accuracy: 0.9829 Epoch 38/200 235/235 [==============================] - 4s 15ms/step - loss: 5.5829e-04 - accuracy: 0.9999 - val_loss: 0.0730 - val_accuracy: 0.9828 Epoch 39/200 235/235 [==============================] - 4s 15ms/step - loss: 2.9081e-04 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9845 Epoch 40/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1149e-04 - accuracy: 1.0000 - val_loss: 0.0719 - val_accuracy: 0.9848 Epoch 41/200 235/235 [==============================] - 4s 15ms/step - loss: 2.5171e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9841 Epoch 42/200 235/235 [==============================] - 4s 15ms/step - loss: 1.6638e-04 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9838 Epoch 43/200 235/235 [==============================] - 4s 15ms/step - loss: 1.2381e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9846 Epoch 44/200 235/235 [==============================] - 4s 16ms/step - loss: 9.4578e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9845 Epoch 45/200 235/235 [==============================] - 4s 16ms/step - loss: 7.8825e-05 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9841 Epoch 46/200 235/235 [==============================] - 4s 15ms/step - loss: 6.1688e-05 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9843 Epoch 47/200 235/235 [==============================] - 4s 15ms/step - loss: 5.6006e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9837 Epoch 48/200 235/235 [==============================] - 4s 15ms/step - loss: 5.5935e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9843 Epoch 49/200 235/235 [==============================] - 4s 15ms/step - loss: 8.0974e-04 - accuracy: 0.9998 - val_loss: 0.1241 - val_accuracy: 0.9767 Epoch 50/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0337 - accuracy: 0.9897 - val_loss: 0.1049 - val_accuracy: 0.9763 Epoch 51/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.0831 - val_accuracy: 0.9812 Epoch 52/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0787 - val_accuracy: 0.9822 Epoch 53/200 235/235 [==============================] - 4s 15ms/step - loss: 7.9964e-04 - accuracy: 0.9999 - val_loss: 0.0780 - val_accuracy: 0.9836 Epoch 54/200 235/235 [==============================] - 4s 15ms/step - loss: 4.2629e-04 - accuracy: 0.9999 - val_loss: 0.0746 - val_accuracy: 0.9835 Epoch 55/200 235/235 [==============================] - 4s 15ms/step - loss: 2.5976e-04 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9837 Epoch 56/200 235/235 [==============================] - 4s 15ms/step - loss: 1.6868e-04 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9839 Epoch 57/200 235/235 [==============================] - 4s 15ms/step - loss: 1.5917e-04 - accuracy: 1.0000 - val_loss: 0.0764 - val_accuracy: 0.9841 Epoch 58/200 235/235 [==============================] - 4s 15ms/step - loss: 1.1439e-04 - accuracy: 1.0000 - val_loss: 0.0763 - val_accuracy: 0.9842 Epoch 59/200 235/235 [==============================] - 4s 15ms/step - loss: 1.0609e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9838 Epoch 60/200 235/235 [==============================] - 4s 15ms/step - loss: 8.9850e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9836 Epoch 61/200 235/235 [==============================] - 4s 15ms/step - loss: 9.2628e-05 - accuracy: 1.0000 - val_loss: 0.0802 - val_accuracy: 0.9829 Epoch 62/200 235/235 [==============================] - 4s 15ms/step - loss: 1.3343e-04 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9827 Epoch 63/200 235/235 [==============================] - 4s 15ms/step - loss: 7.4608e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9831 Epoch 64/200 235/235 [==============================] - 4s 15ms/step - loss: 5.0938e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9828 Epoch 65/200 235/235 [==============================] - 4s 15ms/step - loss: 4.1187e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9829 Epoch 66/200 235/235 [==============================] - 4s 15ms/step - loss: 3.8541e-05 - accuracy: 1.0000 - val_loss: 0.0820 - val_accuracy: 0.9835 Epoch 67/200 235/235 [==============================] - 4s 15ms/step - loss: 3.3755e-05 - accuracy: 1.0000 - val_loss: 0.0824 - val_accuracy: 0.9832 Epoch 68/200 235/235 [==============================] - 4s 15ms/step - loss: 3.1241e-05 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9840 Epoch 69/200 235/235 [==============================] - 4s 15ms/step - loss: 2.6725e-05 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9840 Epoch 70/200 235/235 [==============================] - 4s 15ms/step - loss: 2.6917e-05 - accuracy: 1.0000 - val_loss: 0.0850 - val_accuracy: 0.9837 Epoch 71/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.2888 - val_accuracy: 0.9523 Epoch 72/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0301 - accuracy: 0.9904 - val_loss: 0.1027 - val_accuracy: 0.9784 Epoch 73/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.0820 - val_accuracy: 0.9814 Epoch 74/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.0790 - val_accuracy: 0.9823 Epoch 75/200 235/235 [==============================] - 4s 15ms/step - loss: 6.8739e-04 - accuracy: 0.9999 - val_loss: 0.0786 - val_accuracy: 0.9828 Epoch 76/200 235/235 [==============================] - 4s 15ms/step - loss: 3.2982e-04 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9830 Epoch 77/200 235/235 [==============================] - 4s 16ms/step - loss: 2.3249e-04 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9832 Epoch 78/200 235/235 [==============================] - 4s 15ms/step - loss: 1.6352e-04 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9826 Epoch 79/200 235/235 [==============================] - 4s 15ms/step - loss: 1.4102e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9832 Epoch 80/200 235/235 [==============================] - 3s 14ms/step - loss: 1.4744e-04 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9833 Epoch 81/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3711e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9835 Epoch 82/200 235/235 [==============================] - 3s 15ms/step - loss: 9.2961e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9834 Epoch 83/200 235/235 [==============================] - 3s 15ms/step - loss: 7.8279e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9837 Epoch 84/200 235/235 [==============================] - 3s 14ms/step - loss: 6.1630e-05 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9839 Epoch 85/200 235/235 [==============================] - 3s 15ms/step - loss: 5.3944e-05 - accuracy: 1.0000 - val_loss: 0.0810 - val_accuracy: 0.9840 Epoch 86/200 235/235 [==============================] - 3s 15ms/step - loss: 4.7073e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9839 Epoch 87/200 235/235 [==============================] - 3s 14ms/step - loss: 6.6588e-05 - accuracy: 1.0000 - val_loss: 0.0812 - val_accuracy: 0.9840 Epoch 88/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0061 - accuracy: 0.9981 - val_loss: 0.1814 - val_accuracy: 0.9652 Epoch 89/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0197 - accuracy: 0.9937 - val_loss: 0.0985 - val_accuracy: 0.9778 Epoch 90/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0046 - accuracy: 0.9983 - val_loss: 0.0798 - val_accuracy: 0.9829 Epoch 91/200 235/235 [==============================] - 4s 16ms/step - loss: 9.5145e-04 - accuracy: 0.9998 - val_loss: 0.0747 - val_accuracy: 0.9837 Epoch 92/200 235/235 [==============================] - 4s 16ms/step - loss: 3.2547e-04 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9841 Epoch 93/200 235/235 [==============================] - 3s 15ms/step - loss: 2.0759e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9846 Epoch 94/200 235/235 [==============================] - 3s 14ms/step - loss: 2.1273e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9849 Epoch 95/200 235/235 [==============================] - 3s 14ms/step - loss: 1.6500e-04 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9838 Epoch 96/200 235/235 [==============================] - 3s 15ms/step - loss: 1.8844e-04 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9841 Epoch 97/200 235/235 [==============================] - 4s 15ms/step - loss: 1.4773e-04 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9837 Epoch 98/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0762e-04 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9844 Epoch 99/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5963e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9837 Epoch 100/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3944e-04 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9839 Epoch 101/200 235/235 [==============================] - 3s 15ms/step - loss: 6.3473e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9837 Epoch 102/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1124 - val_accuracy: 0.9775 Epoch 103/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 0.1407 - val_accuracy: 0.9716 Epoch 104/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0979 - val_accuracy: 0.9804 Epoch 105/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.0878 - val_accuracy: 0.9819 Epoch 106/200 235/235 [==============================] - 3s 15ms/step - loss: 8.1914e-04 - accuracy: 0.9998 - val_loss: 0.0843 - val_accuracy: 0.9815 Epoch 107/200 235/235 [==============================] - 3s 14ms/step - loss: 3.3032e-04 - accuracy: 0.9999 - val_loss: 0.0839 - val_accuracy: 0.9828 Epoch 108/200 235/235 [==============================] - 3s 15ms/step - loss: 2.1028e-04 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9837 Epoch 109/200 235/235 [==============================] - 4s 17ms/step - loss: 1.0689e-04 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9840 Epoch 110/200 235/235 [==============================] - 4s 15ms/step - loss: 8.1077e-05 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9843 Epoch 111/200 235/235 [==============================] - 4s 19ms/step - loss: 5.6977e-05 - accuracy: 1.0000 - val_loss: 0.0826 - val_accuracy: 0.9840 Epoch 112/200 235/235 [==============================] - 4s 16ms/step - loss: 5.2850e-05 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9840 Epoch 113/200 235/235 [==============================] - 4s 15ms/step - loss: 3.5655e-04 - accuracy: 0.9999 - val_loss: 0.0849 - val_accuracy: 0.9832 Epoch 114/200 235/235 [==============================] - 4s 18ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1178 - val_accuracy: 0.9794 Epoch 115/200 235/235 [==============================] - 4s 17ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.1190 - val_accuracy: 0.9772 Epoch 116/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.0915 - val_accuracy: 0.9822 Epoch 117/200 235/235 [==============================] - 4s 18ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0907 - val_accuracy: 0.9826 Epoch 118/200 235/235 [==============================] - 4s 18ms/step - loss: 4.1760e-04 - accuracy: 0.9999 - val_loss: 0.0855 - val_accuracy: 0.9836 Epoch 119/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5054e-04 - accuracy: 1.0000 - val_loss: 0.0846 - val_accuracy: 0.9831 Epoch 120/200 235/235 [==============================] - 4s 17ms/step - loss: 8.8659e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9832 Epoch 121/200 235/235 [==============================] - 4s 17ms/step - loss: 5.9028e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9838 Epoch 122/200 235/235 [==============================] - 4s 16ms/step - loss: 4.5024e-05 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 0.9839 Epoch 123/200 235/235 [==============================] - 4s 15ms/step - loss: 4.1556e-05 - accuracy: 1.0000 - val_loss: 0.0838 - val_accuracy: 0.9842 Epoch 124/200 235/235 [==============================] - 4s 15ms/step - loss: 3.6180e-05 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9844 Epoch 125/200 235/235 [==============================] - 4s 15ms/step - loss: 2.8184e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9839 Epoch 126/200 235/235 [==============================] - 4s 16ms/step - loss: 2.6983e-05 - accuracy: 1.0000 - val_loss: 0.0850 - val_accuracy: 0.9838 Epoch 127/200 235/235 [==============================] - 4s 15ms/step - loss: 2.2555e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9837 Epoch 128/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1184e-05 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 0.9836 Epoch 129/200 235/235 [==============================] - 4s 16ms/step - loss: 2.0863e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9841 Epoch 130/200 235/235 [==============================] - 4s 15ms/step - loss: 1.6529e-05 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 0.9840 Epoch 131/200 235/235 [==============================] - 4s 15ms/step - loss: 1.6085e-05 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9845 Epoch 132/200 235/235 [==============================] - 4s 18ms/step - loss: 1.3094e-05 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9840 Epoch 133/200 235/235 [==============================] - 4s 16ms/step - loss: 1.1899e-05 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9843 Epoch 134/200 235/235 [==============================] - 4s 15ms/step - loss: 1.1602e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9844 Epoch 135/200 235/235 [==============================] - 4s 15ms/step - loss: 9.8617e-06 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9842 Epoch 136/200 235/235 [==============================] - 4s 15ms/step - loss: 9.0324e-06 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 0.9840 Epoch 137/200 235/235 [==============================] - 4s 15ms/step - loss: 8.8402e-06 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9841 Epoch 138/200 235/235 [==============================] - 4s 15ms/step - loss: 7.5928e-06 - accuracy: 1.0000 - val_loss: 0.0886 - val_accuracy: 0.9847 Epoch 139/200 235/235 [==============================] - 4s 15ms/step - loss: 6.4924e-06 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9840 Epoch 140/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0088 - accuracy: 0.9977 - val_loss: 0.2212 - val_accuracy: 0.9644 Epoch 141/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0146 - accuracy: 0.9955 - val_loss: 0.0902 - val_accuracy: 0.9814 Epoch 142/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0834 - val_accuracy: 0.9826 Epoch 143/200 235/235 [==============================] - 4s 15ms/step - loss: 3.7214e-04 - accuracy: 0.9999 - val_loss: 0.0813 - val_accuracy: 0.9835 Epoch 144/200 235/235 [==============================] - 4s 15ms/step - loss: 1.4315e-04 - accuracy: 1.0000 - val_loss: 0.0808 - val_accuracy: 0.9838 Epoch 145/200 235/235 [==============================] - 4s 15ms/step - loss: 1.0672e-04 - accuracy: 1.0000 - val_loss: 0.0809 - val_accuracy: 0.9834 Epoch 146/200 235/235 [==============================] - 4s 15ms/step - loss: 1.0374e-04 - accuracy: 1.0000 - val_loss: 0.0826 - val_accuracy: 0.9827 Epoch 147/200 235/235 [==============================] - 4s 15ms/step - loss: 7.3333e-05 - accuracy: 1.0000 - val_loss: 0.0817 - val_accuracy: 0.9838 Epoch 148/200 235/235 [==============================] - 4s 15ms/step - loss: 5.7958e-05 - accuracy: 1.0000 - val_loss: 0.0819 - val_accuracy: 0.9837 Epoch 149/200 235/235 [==============================] - 4s 15ms/step - loss: 5.2431e-05 - accuracy: 1.0000 - val_loss: 0.0821 - val_accuracy: 0.9835 Epoch 150/200 235/235 [==============================] - 4s 15ms/step - loss: 4.4060e-05 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9838 Epoch 151/200 235/235 [==============================] - 4s 15ms/step - loss: 4.2560e-05 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 0.9837 Epoch 152/200 235/235 [==============================] - 4s 15ms/step - loss: 4.5031e-05 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9836 Epoch 153/200 235/235 [==============================] - 4s 15ms/step - loss: 3.1803e-05 - accuracy: 1.0000 - val_loss: 0.0831 - val_accuracy: 0.9841 Epoch 154/200 235/235 [==============================] - 4s 15ms/step - loss: 3.3424e-05 - accuracy: 1.0000 - val_loss: 0.0836 - val_accuracy: 0.9837 Epoch 155/200 235/235 [==============================] - 4s 15ms/step - loss: 5.7255e-04 - accuracy: 0.9999 - val_loss: 0.0916 - val_accuracy: 0.9824 Epoch 156/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1131 - val_accuracy: 0.9797 Epoch 157/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0082 - accuracy: 0.9975 - val_loss: 0.1134 - val_accuracy: 0.9811 Epoch 158/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0025 - accuracy: 0.9991 - val_loss: 0.0973 - val_accuracy: 0.9820 Epoch 159/200 235/235 [==============================] - 4s 15ms/step - loss: 9.4665e-04 - accuracy: 0.9997 - val_loss: 0.0921 - val_accuracy: 0.9838 Epoch 160/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1171e-04 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9841 Epoch 161/200 235/235 [==============================] - 4s 15ms/step - loss: 7.9641e-05 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 0.9840 Epoch 162/200 235/235 [==============================] - 4s 15ms/step - loss: 7.4060e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9842 Epoch 163/200 235/235 [==============================] - 4s 15ms/step - loss: 3.9003e-04 - accuracy: 0.9999 - val_loss: 0.0898 - val_accuracy: 0.9841 Epoch 164/200 235/235 [==============================] - 4s 15ms/step - loss: 2.4947e-04 - accuracy: 0.9999 - val_loss: 0.0881 - val_accuracy: 0.9837 Epoch 165/200 235/235 [==============================] - 4s 15ms/step - loss: 9.7543e-05 - accuracy: 1.0000 - val_loss: 0.0873 - val_accuracy: 0.9847 Epoch 166/200 235/235 [==============================] - 4s 15ms/step - loss: 4.6164e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9843 Epoch 167/200 235/235 [==============================] - 4s 15ms/step - loss: 4.6077e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9847 Epoch 168/200 235/235 [==============================] - 4s 15ms/step - loss: 4.5332e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9846 Epoch 169/200 235/235 [==============================] - 4s 15ms/step - loss: 3.8174e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9842 Epoch 170/200 235/235 [==============================] - 4s 15ms/step - loss: 2.3581e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9840 Epoch 171/200 235/235 [==============================] - 3s 14ms/step - loss: 2.7284e-04 - accuracy: 0.9999 - val_loss: 0.1047 - val_accuracy: 0.9824 Epoch 172/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.1344 - val_accuracy: 0.9775 Epoch 173/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0051 - accuracy: 0.9982 - val_loss: 0.1013 - val_accuracy: 0.9811 Epoch 174/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0938 - val_accuracy: 0.9837 Epoch 175/200 235/235 [==============================] - 4s 15ms/step - loss: 2.9594e-04 - accuracy: 0.9999 - val_loss: 0.0903 - val_accuracy: 0.9841 Epoch 176/200 235/235 [==============================] - 4s 15ms/step - loss: 1.0165e-04 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9844 Epoch 177/200 235/235 [==============================] - 4s 15ms/step - loss: 1.7432e-04 - accuracy: 1.0000 - val_loss: 0.0880 - val_accuracy: 0.9848 Epoch 178/200 235/235 [==============================] - 4s 15ms/step - loss: 4.8831e-04 - accuracy: 0.9998 - val_loss: 0.0940 - val_accuracy: 0.9840 Epoch 179/200 235/235 [==============================] - 4s 15ms/step - loss: 4.4488e-04 - accuracy: 0.9999 - val_loss: 0.0905 - val_accuracy: 0.9848 Epoch 180/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1769e-04 - accuracy: 0.9999 - val_loss: 0.0970 - val_accuracy: 0.9839 Epoch 181/200 235/235 [==============================] - 4s 16ms/step - loss: 2.9174e-04 - accuracy: 0.9999 - val_loss: 0.1058 - val_accuracy: 0.9828 Epoch 182/200 235/235 [==============================] - 4s 15ms/step - loss: 2.2263e-04 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9837 Epoch 183/200 235/235 [==============================] - 4s 15ms/step - loss: 9.7502e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9840 Epoch 184/200 235/235 [==============================] - 4s 15ms/step - loss: 6.8196e-04 - accuracy: 0.9999 - val_loss: 0.0926 - val_accuracy: 0.9844 Epoch 185/200 235/235 [==============================] - 3s 13ms/step - loss: 9.0729e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9854 Epoch 186/200 235/235 [==============================] - 4s 15ms/step - loss: 5.3547e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9846 Epoch 187/200 235/235 [==============================] - 4s 15ms/step - loss: 2.5749e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9850 Epoch 188/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1209 - val_accuracy: 0.9793 Epoch 189/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0063 - accuracy: 0.9982 - val_loss: 0.1085 - val_accuracy: 0.9796 Epoch 190/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1024 - val_accuracy: 0.9829 Epoch 191/200 235/235 [==============================] - 3s 15ms/step - loss: 2.6499e-04 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9826 Epoch 192/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3717e-04 - accuracy: 0.9999 - val_loss: 0.0964 - val_accuracy: 0.9833 Epoch 193/200 235/235 [==============================] - 3s 14ms/step - loss: 6.1717e-05 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9837 Epoch 194/200 235/235 [==============================] - 3s 14ms/step - loss: 4.5216e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9837 Epoch 195/200 235/235 [==============================] - 3s 14ms/step - loss: 3.2486e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9836 Epoch 196/200 235/235 [==============================] - 3s 14ms/step - loss: 2.8874e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9836 Epoch 197/200 235/235 [==============================] - 3s 14ms/step - loss: 1.4463e-04 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9843 Epoch 198/200 235/235 [==============================] - 3s 14ms/step - loss: 4.4490e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9842 Epoch 199/200 235/235 [==============================] - 3s 15ms/step - loss: 2.7753e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9842 Epoch 200/200 235/235 [==============================] - 3s 14ms/step - loss: 1.8208e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9841 Epoch 1/200 235/235 [==============================] - 3s 10ms/step - loss: 1.5573 - accuracy: 0.8554 - val_loss: 0.9210 - val_accuracy: 0.9018 Epoch 2/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8728 - accuracy: 0.8968 - val_loss: 0.8270 - val_accuracy: 0.8996 Epoch 3/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8335 - accuracy: 0.8975 - val_loss: 0.8117 - val_accuracy: 0.8994 Epoch 4/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8236 - accuracy: 0.8975 - val_loss: 0.8050 - val_accuracy: 0.8991 Epoch 5/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8182 - accuracy: 0.8980 - val_loss: 0.8013 - val_accuracy: 0.8991 Epoch 6/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.8982 - val_loss: 0.8000 - val_accuracy: 0.8985 Epoch 7/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8132 - accuracy: 0.8985 - val_loss: 0.7969 - val_accuracy: 0.8995 Epoch 8/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8118 - accuracy: 0.8982 - val_loss: 0.7969 - val_accuracy: 0.8994 Epoch 9/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8107 - accuracy: 0.8989 - val_loss: 0.7948 - val_accuracy: 0.8994 Epoch 10/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8094 - accuracy: 0.8988 - val_loss: 0.7936 - val_accuracy: 0.8997 Epoch 11/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8090 - accuracy: 0.8987 - val_loss: 0.7938 - val_accuracy: 0.8997 Epoch 12/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8085 - accuracy: 0.8985 - val_loss: 0.7933 - val_accuracy: 0.9003 Epoch 13/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8082 - accuracy: 0.8990 - val_loss: 0.7930 - val_accuracy: 0.9002 Epoch 14/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8081 - accuracy: 0.8988 - val_loss: 0.7921 - val_accuracy: 0.9001 Epoch 15/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8079 - accuracy: 0.8990 - val_loss: 0.7907 - val_accuracy: 0.9004 Epoch 16/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8074 - accuracy: 0.8990 - val_loss: 0.7909 - val_accuracy: 0.9003 Epoch 17/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8070 - accuracy: 0.8992 - val_loss: 0.7910 - val_accuracy: 0.9003 Epoch 18/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8068 - accuracy: 0.8992 - val_loss: 0.7896 - val_accuracy: 0.9007 Epoch 19/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8063 - accuracy: 0.8995 - val_loss: 0.7895 - val_accuracy: 0.9012 Epoch 20/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8066 - accuracy: 0.8990 - val_loss: 0.7901 - val_accuracy: 0.9010 Epoch 21/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8063 - accuracy: 0.8990 - val_loss: 0.7888 - val_accuracy: 0.9016 Epoch 22/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8058 - accuracy: 0.8994 - val_loss: 0.7882 - val_accuracy: 0.9012 Epoch 23/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8056 - accuracy: 0.8993 - val_loss: 0.7890 - val_accuracy: 0.9009 Epoch 24/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8057 - accuracy: 0.8991 - val_loss: 0.7888 - val_accuracy: 0.9021 Epoch 25/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8057 - accuracy: 0.8992 - val_loss: 0.7888 - val_accuracy: 0.9017 Epoch 26/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8992 - val_loss: 0.7889 - val_accuracy: 0.9006 Epoch 27/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8994 - val_loss: 0.7890 - val_accuracy: 0.9022 Epoch 28/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8994 - val_loss: 0.7893 - val_accuracy: 0.9008 Epoch 29/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8992 - val_loss: 0.7889 - val_accuracy: 0.9020 Epoch 30/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8997 - val_loss: 0.7883 - val_accuracy: 0.9021 Epoch 31/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8992 - val_loss: 0.7886 - val_accuracy: 0.9013 Epoch 32/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8997 - val_loss: 0.7885 - val_accuracy: 0.9018 Epoch 33/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8995 - val_loss: 0.7883 - val_accuracy: 0.9014 Epoch 34/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8994 - val_loss: 0.7885 - val_accuracy: 0.9011 Epoch 35/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8994 - val_loss: 0.7874 - val_accuracy: 0.9018 Epoch 36/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8998 - val_loss: 0.7884 - val_accuracy: 0.9018 Epoch 37/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8999 - val_loss: 0.7880 - val_accuracy: 0.9016 Epoch 38/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8999 - val_loss: 0.7875 - val_accuracy: 0.9021 Epoch 39/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8998 - val_loss: 0.7873 - val_accuracy: 0.9023 Epoch 40/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8996 - val_loss: 0.7881 - val_accuracy: 0.9027 Epoch 41/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8996 - val_loss: 0.7881 - val_accuracy: 0.9021 Epoch 42/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9000 - val_loss: 0.7885 - val_accuracy: 0.9023 Epoch 43/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8998 - val_loss: 0.7873 - val_accuracy: 0.9029 Epoch 44/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8994 - val_loss: 0.7880 - val_accuracy: 0.9025 Epoch 45/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8999 - val_loss: 0.7865 - val_accuracy: 0.9026 Epoch 46/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9000 - val_loss: 0.7870 - val_accuracy: 0.9022 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9002 - val_loss: 0.7876 - val_accuracy: 0.9030 Epoch 48/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9001 - val_loss: 0.7882 - val_accuracy: 0.9018 Epoch 49/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8997 - val_loss: 0.7871 - val_accuracy: 0.9020 Epoch 50/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9001 - val_loss: 0.7877 - val_accuracy: 0.9020 Epoch 51/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9000 - val_loss: 0.7867 - val_accuracy: 0.9024 Epoch 52/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9003 - val_loss: 0.7868 - val_accuracy: 0.9026 Epoch 53/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.8997 - val_loss: 0.7868 - val_accuracy: 0.9025 Epoch 54/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9002 - val_loss: 0.7870 - val_accuracy: 0.9030 Epoch 55/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8999 - val_loss: 0.7869 - val_accuracy: 0.9030 Epoch 56/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9002 - val_loss: 0.7869 - val_accuracy: 0.9033 Epoch 57/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9001 - val_loss: 0.7877 - val_accuracy: 0.9021 Epoch 58/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9002 - val_loss: 0.7871 - val_accuracy: 0.9026 Epoch 59/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9005 - val_loss: 0.7874 - val_accuracy: 0.9036 Epoch 60/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.9000 - val_loss: 0.7873 - val_accuracy: 0.9032 Epoch 61/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9000 - val_loss: 0.7864 - val_accuracy: 0.9025 Epoch 62/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9001 - val_loss: 0.7872 - val_accuracy: 0.9020 Epoch 63/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9003 - val_loss: 0.7876 - val_accuracy: 0.9028 Epoch 64/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9002 - val_loss: 0.7870 - val_accuracy: 0.9021 Epoch 65/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9005 - val_loss: 0.7872 - val_accuracy: 0.9031 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9004 - val_loss: 0.7873 - val_accuracy: 0.9028 Epoch 67/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9006 - val_loss: 0.7868 - val_accuracy: 0.9026 Epoch 68/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9005 - val_loss: 0.7861 - val_accuracy: 0.9032 Epoch 69/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9005 - val_loss: 0.7869 - val_accuracy: 0.9032 Epoch 70/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7880 - val_accuracy: 0.9029 Epoch 71/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9002 - val_loss: 0.7878 - val_accuracy: 0.9029 Epoch 72/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8041 - accuracy: 0.9006 - val_loss: 0.7864 - val_accuracy: 0.9031 Epoch 73/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8041 - accuracy: 0.9005 - val_loss: 0.7868 - val_accuracy: 0.9030 Epoch 74/200 235/235 [==============================] - 3s 11ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7869 - val_accuracy: 0.9024 Epoch 75/200 235/235 [==============================] - 3s 12ms/step - loss: 0.8039 - accuracy: 0.9005 - val_loss: 0.7870 - val_accuracy: 0.9030 Epoch 76/200 235/235 [==============================] - 3s 11ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7863 - val_accuracy: 0.9032 Epoch 77/200 235/235 [==============================] - 3s 12ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9024 Epoch 78/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9029 Epoch 79/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8040 - accuracy: 0.9006 - val_loss: 0.7875 - val_accuracy: 0.9031 Epoch 80/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7884 - val_accuracy: 0.9031 Epoch 81/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9031 Epoch 82/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9032 Epoch 83/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7875 - val_accuracy: 0.9026 Epoch 84/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9028 Epoch 85/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7873 - val_accuracy: 0.9036 Epoch 86/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7871 - val_accuracy: 0.9034 Epoch 87/200 235/235 [==============================] - 3s 11ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7873 - val_accuracy: 0.9032 Epoch 88/200 235/235 [==============================] - 3s 13ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7871 - val_accuracy: 0.9034 Epoch 89/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7865 - val_accuracy: 0.9034 Epoch 90/200 235/235 [==============================] - 3s 11ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9029 Epoch 91/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7865 - val_accuracy: 0.9031 Epoch 92/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9035 Epoch 93/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9030 Epoch 94/200 235/235 [==============================] - 3s 11ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 95/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 96/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7869 - val_accuracy: 0.9032 Epoch 97/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7861 - val_accuracy: 0.9040 Epoch 98/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9029 Epoch 99/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7868 - val_accuracy: 0.9032 Epoch 100/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9032 Epoch 101/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7871 - val_accuracy: 0.9038 Epoch 102/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7869 - val_accuracy: 0.9031 Epoch 103/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7872 - val_accuracy: 0.9031 Epoch 104/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7869 - val_accuracy: 0.9030 Epoch 105/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7869 - val_accuracy: 0.9029 Epoch 106/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7865 - val_accuracy: 0.9029 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9032 Epoch 108/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7869 - val_accuracy: 0.9039 Epoch 109/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9026 Epoch 110/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9028 Epoch 111/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7873 - val_accuracy: 0.9038 Epoch 112/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7867 - val_accuracy: 0.9034 Epoch 113/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7863 - val_accuracy: 0.9036 Epoch 114/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9029 Epoch 115/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7869 - val_accuracy: 0.9028 Epoch 116/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9021 Epoch 117/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7869 - val_accuracy: 0.9034 Epoch 118/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7882 - val_accuracy: 0.9025 Epoch 119/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7862 - val_accuracy: 0.9038 Epoch 120/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7863 - val_accuracy: 0.9036 Epoch 121/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7866 - val_accuracy: 0.9031 Epoch 122/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9026 Epoch 123/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9033 Epoch 124/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7867 - val_accuracy: 0.9028 Epoch 125/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9028 Epoch 126/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9028 Epoch 127/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9029 Epoch 128/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9028 Epoch 129/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9029 Epoch 130/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9012 - val_loss: 0.7869 - val_accuracy: 0.9029 Epoch 131/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9030 Epoch 132/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9031 Epoch 133/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9033 Epoch 134/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7871 - val_accuracy: 0.9029 Epoch 135/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9008 - val_loss: 0.7871 - val_accuracy: 0.9025 Epoch 136/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 137/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7865 - val_accuracy: 0.9030 Epoch 138/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7858 - val_accuracy: 0.9040 Epoch 139/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7865 - val_accuracy: 0.9031 Epoch 140/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7865 - val_accuracy: 0.9031 Epoch 141/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9033 Epoch 142/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7873 - val_accuracy: 0.9030 Epoch 143/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9028 Epoch 144/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7868 - val_accuracy: 0.9032 Epoch 145/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7869 - val_accuracy: 0.9026 Epoch 146/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7863 - val_accuracy: 0.9030 Epoch 147/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9023 Epoch 148/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8030 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9031 Epoch 149/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9025 Epoch 150/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7867 - val_accuracy: 0.9028 Epoch 151/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7867 - val_accuracy: 0.9030 Epoch 152/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9029 Epoch 153/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9034 Epoch 154/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7867 - val_accuracy: 0.9032 Epoch 155/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7868 - val_accuracy: 0.9042 Epoch 156/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9030 Epoch 157/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9032 Epoch 158/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7867 - val_accuracy: 0.9029 Epoch 159/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9033 Epoch 160/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9029 Epoch 161/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9027 Epoch 162/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9029 Epoch 163/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9028 Epoch 164/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9034 Epoch 165/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9035 Epoch 166/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7871 - val_accuracy: 0.9039 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7869 - val_accuracy: 0.9032 Epoch 168/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7870 - val_accuracy: 0.9035 Epoch 169/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9027 Epoch 170/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9030 Epoch 171/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 172/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9024 Epoch 173/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7868 - val_accuracy: 0.9035 Epoch 174/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9030 Epoch 175/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9029 Epoch 176/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9033 Epoch 177/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7871 - val_accuracy: 0.9022 Epoch 178/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7869 - val_accuracy: 0.9031 Epoch 179/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9032 Epoch 180/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9029 Epoch 181/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7863 - val_accuracy: 0.9033 Epoch 182/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7875 - val_accuracy: 0.9039 Epoch 183/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9034 Epoch 184/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7868 - val_accuracy: 0.9030 Epoch 185/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033 Epoch 186/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9031 Epoch 187/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7874 - val_accuracy: 0.9038 Epoch 188/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9033 Epoch 189/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7864 - val_accuracy: 0.9029 Epoch 190/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9036 Epoch 191/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9032 Epoch 192/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9035 Epoch 193/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9035 Epoch 194/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9032 Epoch 195/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9030 Epoch 196/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9037 Epoch 197/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9028 Epoch 198/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9028 Epoch 199/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9032 Epoch 200/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8031 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9032 Epoch 1/200 235/235 [==============================] - 3s 10ms/step - loss: 0.4704 - accuracy: 0.8695 - val_loss: 0.2471 - val_accuracy: 0.9290 Epoch 2/200 235/235 [==============================] - 2s 10ms/step - loss: 0.2262 - accuracy: 0.9347 - val_loss: 0.1815 - val_accuracy: 0.9472 Epoch 3/200 235/235 [==============================] - 2s 9ms/step - loss: 0.1690 - accuracy: 0.9512 - val_loss: 0.1488 - val_accuracy: 0.9564 Epoch 4/200 235/235 [==============================] - 2s 10ms/step - loss: 0.1341 - accuracy: 0.9609 - val_loss: 0.1292 - val_accuracy: 0.9615 Epoch 5/200 235/235 [==============================] - 2s 9ms/step - loss: 0.1104 - accuracy: 0.9674 - val_loss: 0.1171 - val_accuracy: 0.9649 Epoch 6/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0924 - accuracy: 0.9730 - val_loss: 0.1098 - val_accuracy: 0.9663 Epoch 7/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0784 - accuracy: 0.9773 - val_loss: 0.1055 - val_accuracy: 0.9662 Epoch 8/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0673 - accuracy: 0.9806 - val_loss: 0.1017 - val_accuracy: 0.9677 Epoch 9/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0580 - accuracy: 0.9837 - val_loss: 0.1005 - val_accuracy: 0.9684 Epoch 10/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0503 - accuracy: 0.9861 - val_loss: 0.0994 - val_accuracy: 0.9695 Epoch 11/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0439 - accuracy: 0.9883 - val_loss: 0.0981 - val_accuracy: 0.9704 Epoch 12/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0378 - accuracy: 0.9902 - val_loss: 0.0991 - val_accuracy: 0.9708 Epoch 13/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0328 - accuracy: 0.9921 - val_loss: 0.0990 - val_accuracy: 0.9715 Epoch 14/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0282 - accuracy: 0.9934 - val_loss: 0.0988 - val_accuracy: 0.9725 Epoch 15/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0242 - accuracy: 0.9947 - val_loss: 0.1004 - val_accuracy: 0.9727 Epoch 16/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0207 - accuracy: 0.9956 - val_loss: 0.0999 - val_accuracy: 0.9730 Epoch 17/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.1040 - val_accuracy: 0.9728 Epoch 18/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0154 - accuracy: 0.9974 - val_loss: 0.1061 - val_accuracy: 0.9723 Epoch 19/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0137 - accuracy: 0.9979 - val_loss: 0.1100 - val_accuracy: 0.9729 Epoch 20/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0122 - accuracy: 0.9981 - val_loss: 0.1138 - val_accuracy: 0.9732 Epoch 21/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0112 - accuracy: 0.9981 - val_loss: 0.1104 - val_accuracy: 0.9747 Epoch 22/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0107 - accuracy: 0.9981 - val_loss: 0.1074 - val_accuracy: 0.9762 Epoch 23/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0111 - accuracy: 0.9973 - val_loss: 0.1125 - val_accuracy: 0.9759 Epoch 24/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0108 - accuracy: 0.9970 - val_loss: 0.1102 - val_accuracy: 0.9762 Epoch 25/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9972 - val_loss: 0.1140 - val_accuracy: 0.9752 Epoch 26/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9974 - val_loss: 0.1259 - val_accuracy: 0.9734 Epoch 27/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0080 - accuracy: 0.9979 - val_loss: 0.1358 - val_accuracy: 0.9710 Epoch 28/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0065 - accuracy: 0.9984 - val_loss: 0.1181 - val_accuracy: 0.9751 Epoch 29/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0048 - accuracy: 0.9991 - val_loss: 0.1120 - val_accuracy: 0.9754 Epoch 30/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.1284 - val_accuracy: 0.9725 Epoch 31/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0028 - accuracy: 0.9997 - val_loss: 0.1235 - val_accuracy: 0.9744 Epoch 32/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1201 - val_accuracy: 0.9767 Epoch 33/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.1248 - val_accuracy: 0.9750 Epoch 34/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.1256 - val_accuracy: 0.9764 Epoch 35/200 235/235 [==============================] - 2s 10ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9763 Epoch 36/200 235/235 [==============================] - 2s 10ms/step - loss: 9.9739e-04 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9770 Epoch 37/200 235/235 [==============================] - 2s 9ms/step - loss: 7.7755e-04 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9772 Epoch 38/200 235/235 [==============================] - 2s 9ms/step - loss: 6.3688e-04 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9773 Epoch 39/200 235/235 [==============================] - 2s 10ms/step - loss: 5.1745e-04 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9770 Epoch 40/200 235/235 [==============================] - 2s 9ms/step - loss: 4.3214e-04 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9776 Epoch 41/200 235/235 [==============================] - 2s 9ms/step - loss: 3.7065e-04 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9777 Epoch 42/200 235/235 [==============================] - 2s 9ms/step - loss: 3.1979e-04 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9776 Epoch 43/200 235/235 [==============================] - 2s 9ms/step - loss: 2.7523e-04 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9778 Epoch 44/200 235/235 [==============================] - 2s 9ms/step - loss: 2.3767e-04 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9776 Epoch 45/200 235/235 [==============================] - 2s 9ms/step - loss: 2.0828e-04 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 0.9776 Epoch 46/200 235/235 [==============================] - 2s 9ms/step - loss: 1.8280e-04 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9774 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6160e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9772 Epoch 48/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4284e-04 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9772 Epoch 49/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2653e-04 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9774 Epoch 50/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1231e-04 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9772 Epoch 51/200 235/235 [==============================] - 2s 9ms/step - loss: 9.9600e-05 - accuracy: 1.0000 - val_loss: 0.1406 - val_accuracy: 0.9772 Epoch 52/200 235/235 [==============================] - 2s 9ms/step - loss: 8.7962e-05 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9773 Epoch 53/200 235/235 [==============================] - 2s 9ms/step - loss: 7.8065e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9772 Epoch 54/200 235/235 [==============================] - 2s 9ms/step - loss: 6.9024e-05 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9771 Epoch 55/200 235/235 [==============================] - 2s 9ms/step - loss: 6.1254e-05 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9769 Epoch 56/200 235/235 [==============================] - 2s 9ms/step - loss: 5.4070e-05 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9772 Epoch 57/200 235/235 [==============================] - 2s 9ms/step - loss: 4.7747e-05 - accuracy: 1.0000 - val_loss: 0.1505 - val_accuracy: 0.9771 Epoch 58/200 235/235 [==============================] - 2s 9ms/step - loss: 4.2182e-05 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9772 Epoch 59/200 235/235 [==============================] - 2s 9ms/step - loss: 3.7287e-05 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9772 Epoch 60/200 235/235 [==============================] - 2s 9ms/step - loss: 3.2869e-05 - accuracy: 1.0000 - val_loss: 0.1556 - val_accuracy: 0.9772 Epoch 61/200 235/235 [==============================] - 2s 9ms/step - loss: 2.9063e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9771 Epoch 62/200 235/235 [==============================] - 2s 9ms/step - loss: 2.5500e-05 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9773 Epoch 63/200 235/235 [==============================] - 2s 9ms/step - loss: 2.2495e-05 - accuracy: 1.0000 - val_loss: 0.1608 - val_accuracy: 0.9773 Epoch 64/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9868e-05 - accuracy: 1.0000 - val_loss: 0.1628 - val_accuracy: 0.9771 Epoch 65/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7457e-05 - accuracy: 1.0000 - val_loss: 0.1644 - val_accuracy: 0.9772 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5375e-05 - accuracy: 1.0000 - val_loss: 0.1662 - val_accuracy: 0.9775 Epoch 67/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3563e-05 - accuracy: 1.0000 - val_loss: 0.1679 - val_accuracy: 0.9775 Epoch 68/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1917e-05 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9776 Epoch 69/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0508e-05 - accuracy: 1.0000 - val_loss: 0.1715 - val_accuracy: 0.9774 Epoch 70/200 235/235 [==============================] - 2s 9ms/step - loss: 9.2394e-06 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9777 Epoch 71/200 235/235 [==============================] - 2s 9ms/step - loss: 8.1362e-06 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9776 Epoch 72/200 235/235 [==============================] - 2s 9ms/step - loss: 7.1619e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9775 Epoch 73/200 235/235 [==============================] - 2s 9ms/step - loss: 6.3073e-06 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9777 Epoch 74/200 235/235 [==============================] - 2s 9ms/step - loss: 5.5540e-06 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9776 Epoch 75/200 235/235 [==============================] - 2s 9ms/step - loss: 4.8807e-06 - accuracy: 1.0000 - val_loss: 0.1817 - val_accuracy: 0.9775 Epoch 76/200 235/235 [==============================] - 2s 9ms/step - loss: 4.3057e-06 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9776 Epoch 77/200 235/235 [==============================] - 2s 9ms/step - loss: 3.7866e-06 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9776 Epoch 78/200 235/235 [==============================] - 2s 9ms/step - loss: 3.3445e-06 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9774 Epoch 79/200 235/235 [==============================] - 2s 9ms/step - loss: 2.9457e-06 - accuracy: 1.0000 - val_loss: 0.1886 - val_accuracy: 0.9775 Epoch 80/200 235/235 [==============================] - 2s 9ms/step - loss: 2.5947e-06 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9774 Epoch 81/200 235/235 [==============================] - 2s 9ms/step - loss: 2.2864e-06 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9775 Epoch 82/200 235/235 [==============================] - 2s 9ms/step - loss: 2.0161e-06 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9775 Epoch 83/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7798e-06 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9774 Epoch 84/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5695e-06 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9774 Epoch 85/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3889e-06 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9772 Epoch 86/200 235/235 [==============================] - 2s 10ms/step - loss: 1.2239e-06 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9772 Epoch 87/200 235/235 [==============================] - 2s 10ms/step - loss: 1.0846e-06 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9772 Epoch 88/200 235/235 [==============================] - 2s 9ms/step - loss: 9.5878e-07 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9772 Epoch 89/200 235/235 [==============================] - 2s 9ms/step - loss: 8.5063e-07 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9773 Epoch 90/200 235/235 [==============================] - 2s 9ms/step - loss: 7.5222e-07 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9773 Epoch 91/200 235/235 [==============================] - 2s 9ms/step - loss: 6.6803e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9773 Epoch 92/200 235/235 [==============================] - 2s 9ms/step - loss: 5.9362e-07 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9773 Epoch 93/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2587e-07 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9772 Epoch 94/200 235/235 [==============================] - 2s 9ms/step - loss: 4.6713e-07 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9772 Epoch 95/200 235/235 [==============================] - 2s 9ms/step - loss: 4.1617e-07 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9773 Epoch 96/200 235/235 [==============================] - 2s 9ms/step - loss: 3.7071e-07 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9773 Epoch 97/200 235/235 [==============================] - 2s 8ms/step - loss: 3.3102e-07 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9773 Epoch 98/200 235/235 [==============================] - 2s 9ms/step - loss: 2.9511e-07 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9773 Epoch 99/200 235/235 [==============================] - 2s 9ms/step - loss: 2.6466e-07 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9773 Epoch 100/200 235/235 [==============================] - 2s 9ms/step - loss: 2.3678e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9773 Epoch 101/200 235/235 [==============================] - 2s 9ms/step - loss: 2.1255e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9774 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9094e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9776 Epoch 103/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7185e-07 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9775 Epoch 104/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5509e-07 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9775 Epoch 105/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4000e-07 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9776 Epoch 106/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2681e-07 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9775 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1556e-07 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9775 Epoch 108/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0466e-07 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9775 Epoch 109/200 235/235 [==============================] - 2s 9ms/step - loss: 9.5405e-08 - accuracy: 1.0000 - val_loss: 0.2334 - val_accuracy: 0.9776 Epoch 110/200 235/235 [==============================] - 2s 9ms/step - loss: 8.7096e-08 - accuracy: 1.0000 - val_loss: 0.2343 - val_accuracy: 0.9775 Epoch 111/200 235/235 [==============================] - 2s 9ms/step - loss: 7.9731e-08 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9775 Epoch 112/200 235/235 [==============================] - 2s 9ms/step - loss: 7.3077e-08 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.9775 Epoch 113/200 235/235 [==============================] - 2s 9ms/step - loss: 6.7186e-08 - accuracy: 1.0000 - val_loss: 0.2375 - val_accuracy: 0.9775 Epoch 114/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2054e-08 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9776 Epoch 115/200 235/235 [==============================] - 2s 9ms/step - loss: 5.7270e-08 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 0.9776 Epoch 116/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2937e-08 - accuracy: 1.0000 - val_loss: 0.2402 - val_accuracy: 0.9776 Epoch 117/200 235/235 [==============================] - 2s 9ms/step - loss: 4.9106e-08 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9777 Epoch 118/200 235/235 [==============================] - 2s 9ms/step - loss: 4.5886e-08 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9777 Epoch 119/200 235/235 [==============================] - 2s 9ms/step - loss: 4.2740e-08 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9777 Epoch 120/200 235/235 [==============================] - 2s 9ms/step - loss: 4.0009e-08 - accuracy: 1.0000 - val_loss: 0.2437 - val_accuracy: 0.9776 Epoch 121/200 235/235 [==============================] - 2s 9ms/step - loss: 3.7563e-08 - accuracy: 1.0000 - val_loss: 0.2445 - val_accuracy: 0.9776 Epoch 122/200 235/235 [==============================] - 2s 9ms/step - loss: 3.5097e-08 - accuracy: 1.0000 - val_loss: 0.2452 - val_accuracy: 0.9775 Epoch 123/200 235/235 [==============================] - 2s 9ms/step - loss: 3.3055e-08 - accuracy: 1.0000 - val_loss: 0.2460 - val_accuracy: 0.9775 Epoch 124/200 235/235 [==============================] - 2s 10ms/step - loss: 3.1187e-08 - accuracy: 1.0000 - val_loss: 0.2467 - val_accuracy: 0.9775 Epoch 125/200 235/235 [==============================] - 2s 9ms/step - loss: 2.9596e-08 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9774 Epoch 126/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8016e-08 - accuracy: 1.0000 - val_loss: 0.2479 - val_accuracy: 0.9774 Epoch 127/200 235/235 [==============================] - 2s 9ms/step - loss: 2.6528e-08 - accuracy: 1.0000 - val_loss: 0.2486 - val_accuracy: 0.9773 Epoch 128/200 235/235 [==============================] - 2s 9ms/step - loss: 2.5205e-08 - accuracy: 1.0000 - val_loss: 0.2491 - val_accuracy: 0.9773 Epoch 129/200 235/235 [==============================] - 2s 9ms/step - loss: 2.3901e-08 - accuracy: 1.0000 - val_loss: 0.2498 - val_accuracy: 0.9773 Epoch 130/200 235/235 [==============================] - 2s 9ms/step - loss: 2.2854e-08 - accuracy: 1.0000 - val_loss: 0.2504 - val_accuracy: 0.9772 Epoch 131/200 235/235 [==============================] - 2s 9ms/step - loss: 2.1787e-08 - accuracy: 1.0000 - val_loss: 0.2509 - val_accuracy: 0.9772 Epoch 132/200 235/235 [==============================] - 2s 9ms/step - loss: 2.0754e-08 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9774 Epoch 133/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9942e-08 - accuracy: 1.0000 - val_loss: 0.2520 - val_accuracy: 0.9774 Epoch 134/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9111e-08 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9774 Epoch 135/200 235/235 [==============================] - 2s 9ms/step - loss: 1.8366e-08 - accuracy: 1.0000 - val_loss: 0.2531 - val_accuracy: 0.9775 Epoch 136/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7627e-08 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9775 Epoch 137/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7001e-08 - accuracy: 1.0000 - val_loss: 0.2540 - val_accuracy: 0.9776 Epoch 138/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6312e-08 - accuracy: 1.0000 - val_loss: 0.2544 - val_accuracy: 0.9776 Epoch 139/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5795e-08 - accuracy: 1.0000 - val_loss: 0.2550 - val_accuracy: 0.9776 Epoch 140/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5302e-08 - accuracy: 1.0000 - val_loss: 0.2554 - val_accuracy: 0.9775 Epoch 141/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4802e-08 - accuracy: 1.0000 - val_loss: 0.2560 - val_accuracy: 0.9774 Epoch 142/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4351e-08 - accuracy: 1.0000 - val_loss: 0.2564 - val_accuracy: 0.9775 Epoch 143/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3938e-08 - accuracy: 1.0000 - val_loss: 0.2569 - val_accuracy: 0.9774 Epoch 144/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3526e-08 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9774 Epoch 145/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3093e-08 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9775 Epoch 146/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2690e-08 - accuracy: 1.0000 - val_loss: 0.2581 - val_accuracy: 0.9775 Epoch 147/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2292e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9775 Epoch 148/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1973e-08 - accuracy: 1.0000 - val_loss: 0.2588 - val_accuracy: 0.9775 Epoch 149/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1663e-08 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9775 Epoch 150/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1327e-08 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9776 Epoch 151/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1011e-08 - accuracy: 1.0000 - val_loss: 0.2598 - val_accuracy: 0.9776 Epoch 152/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0677e-08 - accuracy: 1.0000 - val_loss: 0.2601 - val_accuracy: 0.9775 Epoch 153/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0415e-08 - accuracy: 1.0000 - val_loss: 0.2604 - val_accuracy: 0.9775 Epoch 154/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0234e-08 - accuracy: 1.0000 - val_loss: 0.2608 - val_accuracy: 0.9775 Epoch 155/200 235/235 [==============================] - 2s 9ms/step - loss: 9.9003e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9776 Epoch 156/200 235/235 [==============================] - 2s 9ms/step - loss: 9.7434e-09 - accuracy: 1.0000 - val_loss: 0.2612 - val_accuracy: 0.9777 Epoch 157/200 235/235 [==============================] - 2s 10ms/step - loss: 9.4672e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9776 Epoch 158/200 235/235 [==============================] - 2s 10ms/step - loss: 9.2049e-09 - accuracy: 1.0000 - val_loss: 0.2617 - val_accuracy: 0.9776 Epoch 159/200 235/235 [==============================] - 2s 10ms/step - loss: 9.0381e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9777 Epoch 160/200 235/235 [==============================] - 2s 9ms/step - loss: 8.8394e-09 - accuracy: 1.0000 - val_loss: 0.2621 - val_accuracy: 0.9777 Epoch 161/200 235/235 [==============================] - 2s 9ms/step - loss: 8.6983e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9777 Epoch 162/200 235/235 [==============================] - 2s 9ms/step - loss: 8.4599e-09 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9776 Epoch 163/200 235/235 [==============================] - 2s 9ms/step - loss: 8.2751e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9777 Epoch 164/200 235/235 [==============================] - 2s 9ms/step - loss: 8.0844e-09 - accuracy: 1.0000 - val_loss: 0.2630 - val_accuracy: 0.9776 Epoch 165/200 235/235 [==============================] - 2s 9ms/step - loss: 7.9354e-09 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9776 Epoch 166/200 235/235 [==============================] - 2s 9ms/step - loss: 7.7645e-09 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9775 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 7.6274e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9774 Epoch 168/200 235/235 [==============================] - 2s 9ms/step - loss: 7.4347e-09 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9774 Epoch 169/200 235/235 [==============================] - 2s 9ms/step - loss: 7.2956e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9774 Epoch 170/200 235/235 [==============================] - 2s 9ms/step - loss: 7.1704e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9774 Epoch 171/200 235/235 [==============================] - 2s 9ms/step - loss: 7.0035e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9776 Epoch 172/200 235/235 [==============================] - 2s 9ms/step - loss: 6.9062e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9776 Epoch 173/200 235/235 [==============================] - 2s 10ms/step - loss: 6.7671e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9775 Epoch 174/200 235/235 [==============================] - 2s 9ms/step - loss: 6.6102e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9775 Epoch 175/200 235/235 [==============================] - 2s 9ms/step - loss: 6.4810e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9775 Epoch 176/200 235/235 [==============================] - 2s 9ms/step - loss: 6.3697e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9773 Epoch 177/200 235/235 [==============================] - 2s 9ms/step - loss: 6.2446e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9773 Epoch 178/200 235/235 [==============================] - 2s 9ms/step - loss: 6.1611e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9773 Epoch 179/200 235/235 [==============================] - 2s 9ms/step - loss: 6.0598e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9775 Epoch 180/200 235/235 [==============================] - 2s 9ms/step - loss: 5.9783e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9775 Epoch 181/200 235/235 [==============================] - 2s 9ms/step - loss: 5.8651e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9775 Epoch 182/200 235/235 [==============================] - 2s 9ms/step - loss: 5.7459e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9775 Epoch 183/200 235/235 [==============================] - 2s 9ms/step - loss: 5.6525e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9775 Epoch 184/200 235/235 [==============================] - 2s 9ms/step - loss: 5.5691e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9775 Epoch 185/200 235/235 [==============================] - 2s 9ms/step - loss: 5.4995e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9775 Epoch 186/200 235/235 [==============================] - 2s 9ms/step - loss: 5.4061e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9776 Epoch 187/200 235/235 [==============================] - 2s 9ms/step - loss: 5.3326e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9775 Epoch 188/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2373e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9776 Epoch 189/200 235/235 [==============================] - 2s 9ms/step - loss: 5.1399e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9776 Epoch 190/200 235/235 [==============================] - 2s 9ms/step - loss: 5.0465e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9776 Epoch 191/200 235/235 [==============================] - 2s 9ms/step - loss: 4.9611e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9776 Epoch 192/200 235/235 [==============================] - 2s 9ms/step - loss: 4.8677e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9776 Epoch 193/200 235/235 [==============================] - 2s 9ms/step - loss: 4.8121e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9777 Epoch 194/200 235/235 [==============================] - 2s 9ms/step - loss: 4.7048e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9776 Epoch 195/200 235/235 [==============================] - 2s 9ms/step - loss: 4.6750e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9777 Epoch 196/200 235/235 [==============================] - 2s 9ms/step - loss: 4.6055e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9777 Epoch 197/200 235/235 [==============================] - 2s 9ms/step - loss: 4.5439e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9777 Epoch 198/200 235/235 [==============================] - 2s 9ms/step - loss: 4.4942e-09 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9777 Epoch 199/200 235/235 [==============================] - 2s 9ms/step - loss: 4.3929e-09 - accuracy: 1.0000 - val_loss: 0.2678 - val_accuracy: 0.9777 Epoch 200/200 235/235 [==============================] - 2s 9ms/step - loss: 4.3233e-09 - accuracy: 1.0000 - val_loss: 0.2679 - val_accuracy: 0.9777 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03711757808923721 Thresholhold -0.0392148457467556 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06093437783420086 Thresholhold 0.05750960856676102 Using suggest threshold. Applying new mask Percentage zeros 0.47363332 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 0. 0. 1.] ... [1. 1. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.11950229108333588 Thresholhold -0.07819221913814545 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 5/235 [..............................] - ETA: 2s - loss: 7.5759 - accuracy: 0.4180 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0122s vs `on_train_batch_begin` time: 11.8797s). Check your callbacks. 235/235 [==============================] - 76s 17ms/step - loss: 2.1743 - accuracy: 0.9234 - val_loss: 1.6469 - val_accuracy: 0.8219 [ 2.6182713e-07 2.7255973e-07 -5.4995253e-10 ... 6.9539361e-02 1.7933668e-01 -1.4609440e-01] Sparsity at: 0.05337716003005259 Epoch 2/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4467 - accuracy: 0.9609 - val_loss: 0.5192 - val_accuracy: 0.9575 [ 1.27812452e-12 1.25070071e-12 -1.26711987e-14 ... 6.27598241e-02 1.56362295e-01 -1.05136074e-01] Sparsity at: 0.05337716003005259 Epoch 3/500 235/235 [==============================] - 4s 16ms/step - loss: 0.3087 - accuracy: 0.9639 - val_loss: 0.3475 - val_accuracy: 0.9466 [ 3.6899336e-18 5.4916910e-18 -3.3695073e-21 ... 6.2770337e-02 1.4459158e-01 -7.6582618e-02] Sparsity at: 0.05337716003005259 Epoch 4/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2789 - accuracy: 0.9666 - val_loss: 0.3000 - val_accuracy: 0.9551 [-2.4170925e-23 -2.1056073e-23 2.8305059e-25 ... 5.2354824e-02 1.3261829e-01 -5.5241436e-02] Sparsity at: 0.05337716003005259 Epoch 5/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2613 - accuracy: 0.9681 - val_loss: 0.3123 - val_accuracy: 0.9486 [-5.1553792e-29 7.0079728e-29 -2.8508540e-31 ... 4.3344885e-02 1.2501729e-01 -4.8855189e-02] Sparsity at: 0.05337716003005259 Epoch 6/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2522 - accuracy: 0.9682 - val_loss: 0.2599 - val_accuracy: 0.9612 [ 5.0166462e-34 -3.9579262e-35 7.1719144e-33 ... 3.2381877e-02 1.1879123e-01 -4.0542919e-02] Sparsity at: 0.05337716003005259 Epoch 7/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2371 - accuracy: 0.9704 - val_loss: 0.2990 - val_accuracy: 0.9478 [ 5.0166462e-34 -3.9579262e-35 1.9939151e-05 ... 1.6526887e-02 1.1332396e-01 -3.4175690e-02] Sparsity at: 0.053380916604057096 Epoch 8/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2296 - accuracy: 0.9707 - val_loss: 0.2618 - val_accuracy: 0.9607 [ 5.0166462e-34 -3.9579262e-35 -3.0223157e-10 ... 1.1858181e-02 1.0491302e-01 -2.6528461e-02] Sparsity at: 0.053380916604057096 Epoch 9/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2237 - accuracy: 0.9717 - val_loss: 0.2793 - val_accuracy: 0.9527 [ 5.0166462e-34 -3.9579262e-35 2.4131467e-15 ... 4.6975757e-03 9.8115675e-02 -2.0106828e-02] Sparsity at: 0.053380916604057096 Epoch 10/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2207 - accuracy: 0.9715 - val_loss: 0.2619 - val_accuracy: 0.9564 [ 5.0166462e-34 -3.9579262e-35 1.5732008e-20 ... 3.9040274e-03 9.3779020e-02 -2.1069059e-02] Sparsity at: 0.053380916604057096 Epoch 11/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2095 - accuracy: 0.9724 - val_loss: 0.2776 - val_accuracy: 0.9511 [ 5.0166462e-34 -3.9579262e-35 3.9994102e-08 ... 1.4377410e-03 9.1147780e-02 -2.6767194e-02] Sparsity at: 0.05338467317806161 Epoch 12/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2057 - accuracy: 0.9733 - val_loss: 0.2524 - val_accuracy: 0.9580 [ 5.0166462e-34 -3.9579262e-35 3.0700200e-09 ... -2.8368442e-03 7.4733689e-02 -2.9467864e-02] Sparsity at: 0.05338467317806161 Epoch 13/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2038 - accuracy: 0.9724 - val_loss: 0.2603 - val_accuracy: 0.9508 [ 5.0166462e-34 -3.9579262e-35 -1.3601838e-14 ... -3.3074119e-03 6.4808674e-02 -2.2849694e-02] Sparsity at: 0.05338467317806161 Epoch 14/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1968 - accuracy: 0.9739 - val_loss: 0.2722 - val_accuracy: 0.9519 [ 5.0166462e-34 -3.9579262e-35 -1.9443069e-19 ... 8.4357371e-04 5.6750868e-02 -2.0543942e-02] Sparsity at: 0.05338467317806161 Epoch 15/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1948 - accuracy: 0.9742 - val_loss: 0.2612 - val_accuracy: 0.9500 [ 5.0166462e-34 -3.9579262e-35 -5.5318935e-07 ... 6.5877554e-03 5.0148167e-02 -2.2757500e-02] Sparsity at: 0.05338467317806161 Epoch 16/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1912 - accuracy: 0.9745 - val_loss: 0.2624 - val_accuracy: 0.9533 [ 5.0166462e-34 -3.9579262e-35 -6.2919158e-13 ... 2.7826377e-03 4.2091645e-02 -2.5734803e-02] Sparsity at: 0.05338467317806161 Epoch 17/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1893 - accuracy: 0.9742 - val_loss: 0.2541 - val_accuracy: 0.9515 [ 5.0166462e-34 -3.9579262e-35 3.9528993e-18 ... 4.2283740e-03 3.3975795e-02 -2.3419119e-02] Sparsity at: 0.05338467317806161 Epoch 18/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1901 - accuracy: 0.9740 - val_loss: 0.2272 - val_accuracy: 0.9583 [ 5.0166462e-34 -3.9579262e-35 3.8717550e-07 ... 1.6569829e-02 2.5503192e-02 -3.0548433e-02] Sparsity at: 0.05338467317806161 Epoch 19/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1863 - accuracy: 0.9744 - val_loss: 0.2450 - val_accuracy: 0.9585 [ 5.0166462e-34 -3.9579262e-35 -3.4265086e-12 ... 1.9503133e-02 2.1660034e-02 -3.2672361e-02] Sparsity at: 0.05338467317806161 Epoch 20/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1824 - accuracy: 0.9752 - val_loss: 0.2235 - val_accuracy: 0.9608 [ 5.0166462e-34 -3.9579262e-35 2.2170715e-13 ... 2.4956504e-02 1.5207321e-02 -1.9665049e-02] Sparsity at: 0.05338467317806161 Epoch 21/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1789 - accuracy: 0.9755 - val_loss: 0.2169 - val_accuracy: 0.9616 [ 5.0166462e-34 -3.9579262e-35 -2.9218391e-08 ... 2.0570545e-02 1.1101723e-02 -1.9768164e-02] Sparsity at: 0.05338467317806161 Epoch 22/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1804 - accuracy: 0.9744 - val_loss: 0.2287 - val_accuracy: 0.9607 [ 5.0166462e-34 -3.9579262e-35 1.9623945e-13 ... 1.7647263e-02 7.3507223e-03 -1.5398765e-02] Sparsity at: 0.05338467317806161 Epoch 23/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1771 - accuracy: 0.9754 - val_loss: 0.2116 - val_accuracy: 0.9656 [ 5.0166462e-34 -3.9579262e-35 3.2583202e-06 ... 1.0712972e-02 4.2403848e-03 -1.7174795e-02] Sparsity at: 0.05338467317806161 Epoch 24/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1755 - accuracy: 0.9752 - val_loss: 0.2310 - val_accuracy: 0.9579 [ 5.0166462e-34 -3.9579262e-35 3.8201664e-11 ... 1.3251809e-02 -9.6835248e-04 -1.6826019e-02] Sparsity at: 0.05338467317806161 Epoch 25/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1735 - accuracy: 0.9760 - val_loss: 0.2171 - val_accuracy: 0.9632 [ 5.0166462e-34 -3.9579262e-35 -1.4232317e-11 ... 1.0068036e-02 1.2489640e-03 -9.7984700e-03] Sparsity at: 0.05338467317806161 Epoch 26/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1741 - accuracy: 0.9761 - val_loss: 0.2150 - val_accuracy: 0.9613 [ 5.0166462e-34 -3.9579262e-35 -6.5875181e-09 ... 1.0325787e-02 -1.2203654e-03 -7.4751927e-03] Sparsity at: 0.05338467317806161 Epoch 27/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1728 - accuracy: 0.9751 - val_loss: 0.2427 - val_accuracy: 0.9546 [ 5.0166462e-34 -3.9579262e-35 -9.7149512e-15 ... 1.3703915e-02 -1.3237198e-02 -9.7690606e-03] Sparsity at: 0.05338467317806161 Epoch 28/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1716 - accuracy: 0.9760 - val_loss: 0.2414 - val_accuracy: 0.9534 [ 5.0166462e-34 -3.9579262e-35 -2.1649271e-07 ... 7.1665542e-03 -1.8820412e-02 -1.9201253e-02] Sparsity at: 0.05338467317806161 Epoch 29/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1698 - accuracy: 0.9755 - val_loss: 0.2365 - val_accuracy: 0.9535 [ 5.0166462e-34 -3.9579262e-35 -4.3212448e-12 ... 1.0658165e-02 -1.8117230e-02 -1.9476769e-02] Sparsity at: 0.05338467317806161 Epoch 30/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1699 - accuracy: 0.9755 - val_loss: 0.2184 - val_accuracy: 0.9595 [ 5.0166462e-34 -3.9579262e-35 -1.8915485e-05 ... 1.2829820e-02 -2.3846824e-02 -2.4275405e-02] Sparsity at: 0.05338467317806161 Epoch 31/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1691 - accuracy: 0.9760 - val_loss: 0.2381 - val_accuracy: 0.9545 [ 5.0166462e-34 -3.9579262e-35 7.3397177e-11 ... 9.7641647e-03 -2.8089058e-02 -1.8779812e-02] Sparsity at: 0.05338467317806161 Epoch 32/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1632 - accuracy: 0.9767 - val_loss: 0.2316 - val_accuracy: 0.9566 [ 5.0166462e-34 -3.9579262e-35 3.7741330e-09 ... 1.3506287e-02 -2.2983087e-02 -1.7499605e-02] Sparsity at: 0.05338467317806161 Epoch 33/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1687 - accuracy: 0.9748 - val_loss: 0.2118 - val_accuracy: 0.9622 [ 5.0166462e-34 -3.9579262e-35 5.6163501e-09 ... 1.4217476e-02 -3.0416472e-02 -1.2726890e-02] Sparsity at: 0.05338467317806161 Epoch 34/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1645 - accuracy: 0.9768 - val_loss: 0.2123 - val_accuracy: 0.9618 [ 5.0166462e-34 -3.9579262e-35 1.1473854e-13 ... 1.5452255e-02 -3.5286523e-02 -1.1925710e-02] Sparsity at: 0.05338467317806161 Epoch 35/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1635 - accuracy: 0.9761 - val_loss: 0.2275 - val_accuracy: 0.9574 [ 5.0166462e-34 -3.9579262e-35 -6.3272822e-08 ... 1.4674430e-02 -2.7876941e-02 -1.2471416e-02] Sparsity at: 0.05338467317806161 Epoch 36/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1667 - accuracy: 0.9758 - val_loss: 0.2257 - val_accuracy: 0.9594 [ 5.0166462e-34 -3.9579262e-35 -7.2662926e-13 ... 1.4933964e-02 -3.2886770e-02 -6.4423378e-03] Sparsity at: 0.05338467317806161 Epoch 37/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1634 - accuracy: 0.9764 - val_loss: 0.1973 - val_accuracy: 0.9669 [ 5.0166462e-34 -3.9579262e-35 1.1768242e-06 ... 1.0340488e-02 -2.6485782e-02 -1.0832785e-02] Sparsity at: 0.05338467317806161 Epoch 38/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1628 - accuracy: 0.9763 - val_loss: 0.2128 - val_accuracy: 0.9615 [ 5.0166462e-34 -3.9579262e-35 7.2762013e-12 ... 1.0905896e-02 -2.4131672e-02 -8.0275489e-03] Sparsity at: 0.05338467317806161 Epoch 39/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1631 - accuracy: 0.9760 - val_loss: 0.2010 - val_accuracy: 0.9656 [ 5.0166462e-34 -3.9579262e-35 2.3399634e-06 ... 8.1663085e-03 -2.4927408e-02 -1.4740099e-02] Sparsity at: 0.05338467317806161 Epoch 40/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1599 - accuracy: 0.9773 - val_loss: 0.2234 - val_accuracy: 0.9590 [ 5.0166462e-34 -3.9579262e-35 4.6575004e-11 ... 1.0923654e-02 -3.5388030e-02 -1.5200370e-02] Sparsity at: 0.05338467317806161 Epoch 41/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1625 - accuracy: 0.9758 - val_loss: 0.2111 - val_accuracy: 0.9609 [ 5.0166462e-34 -3.9579262e-35 -8.3646737e-06 ... 2.1427993e-03 -2.2664459e-02 -1.9955078e-02] Sparsity at: 0.05338467317806161 Epoch 42/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1633 - accuracy: 0.9751 - val_loss: 0.2607 - val_accuracy: 0.9496 [ 5.0166462e-34 -3.9579262e-35 9.4319538e-11 ... 4.2143008e-03 -3.8583081e-02 -1.4358026e-02] Sparsity at: 0.05338467317806161 Epoch 43/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1600 - accuracy: 0.9771 - val_loss: 0.2015 - val_accuracy: 0.9646 [ 5.0166462e-34 -3.9579262e-35 3.5953258e-09 ... 5.6377659e-03 -2.3705304e-02 -1.7880758e-02] Sparsity at: 0.05338467317806161 Epoch 44/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1576 - accuracy: 0.9772 - val_loss: 0.2094 - val_accuracy: 0.9626 [ 5.0166462e-34 -3.9579262e-35 5.0679145e-09 ... 1.4536776e-02 -3.0341765e-02 -1.8914200e-02] Sparsity at: 0.05338467317806161 Epoch 45/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1612 - accuracy: 0.9762 - val_loss: 0.1974 - val_accuracy: 0.9672 [ 5.0166462e-34 -3.9579262e-35 -1.8783124e-12 ... 2.3019256e-02 -3.4548748e-02 -1.5799886e-02] Sparsity at: 0.05338467317806161 Epoch 46/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1592 - accuracy: 0.9762 - val_loss: 0.1872 - val_accuracy: 0.9704 [ 5.0166462e-34 -3.9579262e-35 3.2089194e-08 ... 1.9440114e-02 -3.1856921e-02 -2.6698433e-02] Sparsity at: 0.05338467317806161 Epoch 47/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1606 - accuracy: 0.9765 - val_loss: 0.2049 - val_accuracy: 0.9628 [ 5.0166462e-34 -3.9579262e-35 -1.5128660e-13 ... 1.4182773e-02 -2.7691234e-02 -2.3910008e-02] Sparsity at: 0.05338467317806161 Epoch 48/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1606 - accuracy: 0.9765 - val_loss: 0.2064 - val_accuracy: 0.9665loss: 0.1613 - accu [ 5.0166462e-34 -3.9579262e-35 5.5486066e-08 ... 1.4384116e-02 -2.4636896e-02 -2.0474030e-02] Sparsity at: 0.05338467317806161 Epoch 49/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1575 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9501 [ 5.0166462e-34 -3.9579262e-35 1.0389762e-12 ... 1.7705215e-02 -3.6468856e-02 -1.9604597e-02] Sparsity at: 0.05338467317806161 Epoch 50/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1572 - accuracy: 0.9767 - val_loss: 0.1959 - val_accuracy: 0.9684 [ 5.0166462e-34 -3.9579262e-35 3.3418155e-07 ... 1.4178975e-02 -3.0434823e-02 -1.7258856e-02] Sparsity at: 0.05338467317806161 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 4.127688005100672e-34 Thresholhold 5.0166462207447374e-34 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 2.1617254299427043e-08 Thresholhold 0.0006648348644375801 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 0. 0. 1.] ... [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.03189364128356509 Thresholhold 0.008343097753822803 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 161s 16ms/step - loss: 0.1535 - accuracy: 0.9777 - val_loss: 0.2277 - val_accuracy: 0.9568 [ 5.0166462e-34 0.0000000e+00 4.0623971e-12 ... 1.3479545e-02 -2.9032305e-02 -1.4903450e-02] Sparsity at: 0.6441059353869272 Epoch 52/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1578 - accuracy: 0.9764 - val_loss: 0.2105 - val_accuracy: 0.9610 [ 5.01664622e-34 0.00000000e+00 -1.89223988e-06 ... 1.42970905e-02 -3.20940502e-02 -1.25430441e-02] Sparsity at: 0.6441059353869272 Epoch 53/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1581 - accuracy: 0.9767 - val_loss: 0.2061 - val_accuracy: 0.9630 [ 5.0166462e-34 0.0000000e+00 -2.9856560e-12 ... 1.0371569e-02 -2.4364771e-02 -7.7982987e-03] Sparsity at: 0.6441059353869272 Epoch 54/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1571 - accuracy: 0.9762 - val_loss: 0.2224 - val_accuracy: 0.9597 [ 5.0166462e-34 0.0000000e+00 -8.8333909e-06 ... 1.9195370e-02 -2.4613300e-02 -1.9365584e-02] Sparsity at: 0.6441059353869272 Epoch 55/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1554 - accuracy: 0.9770 - val_loss: 0.1912 - val_accuracy: 0.9674 [ 5.0166462e-34 0.0000000e+00 -4.7829588e-12 ... 2.1803521e-02 -2.5473414e-02 -1.5462674e-02] Sparsity at: 0.6441059353869272 Epoch 56/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1591 - accuracy: 0.9763 - val_loss: 0.2186 - val_accuracy: 0.9585 [ 5.0166462e-34 0.0000000e+00 -9.7146876e-06 ... 2.4091730e-02 -3.2752600e-02 -1.6575871e-02] Sparsity at: 0.6441059353869272 Epoch 57/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1561 - accuracy: 0.9767 - val_loss: 0.2191 - val_accuracy: 0.9593 [ 5.0166462e-34 0.0000000e+00 -3.1474162e-10 ... 2.6636785e-02 -3.1338960e-02 -2.8078005e-02] Sparsity at: 0.6441059353869272 Epoch 58/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1570 - accuracy: 0.9764 - val_loss: 0.2238 - val_accuracy: 0.9562 [ 5.0166462e-34 0.0000000e+00 -1.8697406e-07 ... 2.4864063e-02 -2.6355470e-02 -2.6583431e-02] Sparsity at: 0.6441059353869272 Epoch 59/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1544 - accuracy: 0.9774 - val_loss: 0.1986 - val_accuracy: 0.9660 [ 5.0166462e-34 0.0000000e+00 -1.1679702e-09 ... 1.3369675e-02 -3.8235225e-02 -1.8721934e-02] Sparsity at: 0.6441059353869272 Epoch 60/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1535 - accuracy: 0.9777 - val_loss: 0.2132 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 -6.3867418e-11 ... 4.4711653e-04 -3.8320549e-02 -1.9163255e-02] Sparsity at: 0.6441059353869272 Epoch 61/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1552 - accuracy: 0.9778 - val_loss: 0.2310 - val_accuracy: 0.9544 [ 5.0166462e-34 0.0000000e+00 -1.0883481e-09 ... 7.9630557e-03 -3.0702971e-02 -2.2882050e-02] Sparsity at: 0.6441059353869272 Epoch 62/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1535 - accuracy: 0.9770 - val_loss: 0.2194 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 9.8072332e-13 ... 9.8965326e-03 -3.3567477e-02 -2.5015902e-02] Sparsity at: 0.6441059353869272 Epoch 63/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1583 - accuracy: 0.9758 - val_loss: 0.2064 - val_accuracy: 0.9626 [ 5.0166462e-34 0.0000000e+00 -5.5153365e-08 ... 1.2028722e-02 -2.7328763e-02 -2.8118985e-02] Sparsity at: 0.6441059353869272 Epoch 64/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1511 - accuracy: 0.9779 - val_loss: 0.2052 - val_accuracy: 0.9588 [ 5.0166462e-34 0.0000000e+00 -3.3608088e-13 ... 1.3083044e-02 -2.6202209e-02 -2.7347594e-02] Sparsity at: 0.6441059353869272 Epoch 65/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1511 - accuracy: 0.9777 - val_loss: 0.1986 - val_accuracy: 0.9618 [ 5.0166462e-34 0.0000000e+00 -1.9550271e-07 ... 8.4086014e-03 -2.3641180e-02 -1.5738163e-02] Sparsity at: 0.6441059353869272 Epoch 66/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1495 - accuracy: 0.9777 - val_loss: 0.2364 - val_accuracy: 0.9514 [ 5.0166462e-34 0.0000000e+00 9.6862091e-13 ... 9.3865478e-03 -3.8449239e-02 -1.5006943e-02] Sparsity at: 0.6441059353869272 Epoch 67/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1545 - accuracy: 0.9765 - val_loss: 0.1978 - val_accuracy: 0.9650 [ 5.0166462e-34 0.0000000e+00 1.1961954e-06 ... 4.5176172e-03 -3.0956587e-02 -1.9349823e-02] Sparsity at: 0.6441059353869272 Epoch 68/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1503 - accuracy: 0.9776 - val_loss: 0.2365 - val_accuracy: 0.9534 [ 5.0166462e-34 0.0000000e+00 -6.0259497e-12 ... 2.6058832e-03 -3.0703098e-02 -1.9611778e-02] Sparsity at: 0.6441059353869272 Epoch 69/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1480 - accuracy: 0.9782 - val_loss: 0.2041 - val_accuracy: 0.9637 [ 5.0166462e-34 0.0000000e+00 -1.4919138e-05 ... -4.0144813e-03 -2.4919795e-02 -1.1992470e-02] Sparsity at: 0.6441059353869272 Epoch 70/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1499 - accuracy: 0.9773 - val_loss: 0.2215 - val_accuracy: 0.9591 [ 5.0166462e-34 0.0000000e+00 -2.7269520e-11 ... -5.1395381e-03 -2.8584626e-02 -1.5197810e-02] Sparsity at: 0.6441059353869272 Epoch 71/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1495 - accuracy: 0.9783 - val_loss: 0.2231 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 1.2278906e-04 ... 7.0645693e-03 -3.0856764e-02 -1.1189264e-02] Sparsity at: 0.6441059353869272 Epoch 72/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1516 - accuracy: 0.9766 - val_loss: 0.2088 - val_accuracy: 0.9610 [ 5.0166462e-34 0.0000000e+00 3.0474456e-10 ... 9.5961168e-03 -2.7384391e-02 -1.2730742e-02] Sparsity at: 0.6441059353869272 Epoch 73/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1514 - accuracy: 0.9767 - val_loss: 0.2165 - val_accuracy: 0.9601 [ 5.0166462e-34 0.0000000e+00 -7.8143442e-10 ... 2.2332519e-03 -1.3410567e-02 -1.2902572e-02] Sparsity at: 0.6441059353869272 Epoch 74/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1497 - accuracy: 0.9776 - val_loss: 0.2270 - val_accuracy: 0.9536 [ 5.016646e-34 0.000000e+00 -8.154175e-10 ... 7.903611e-03 -3.088829e-02 -1.119129e-02] Sparsity at: 0.6441059353869272 Epoch 75/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9778 - val_loss: 0.1891 - val_accuracy: 0.9665 [ 5.0166462e-34 0.0000000e+00 -4.8531670e-13 ... -1.2072031e-03 -2.8435392e-02 -7.9397941e-03] Sparsity at: 0.6441059353869272 Epoch 76/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1475 - accuracy: 0.9778 - val_loss: 0.2053 - val_accuracy: 0.9612 [ 5.0166462e-34 0.0000000e+00 6.5276836e-08 ... 6.3317469e-03 -3.1204470e-02 -1.6628483e-02] Sparsity at: 0.6441059353869272 Epoch 77/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1494 - accuracy: 0.9773 - val_loss: 0.2220 - val_accuracy: 0.9579 [ 5.0166462e-34 0.0000000e+00 4.9464366e-13 ... 7.4462583e-03 -2.2881800e-02 -1.8518686e-02] Sparsity at: 0.6441059353869272 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1472 - accuracy: 0.9779 - val_loss: 0.2034 - val_accuracy: 0.9626 [ 5.0166462e-34 0.0000000e+00 -1.2117837e-07 ... -1.8681637e-04 -2.6354477e-02 -1.8567117e-02] Sparsity at: 0.6441059353869272 Epoch 79/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9781 - val_loss: 0.2069 - val_accuracy: 0.9632 [ 5.0166462e-34 0.0000000e+00 -2.1818647e-12 ... -1.2605761e-03 -1.8196050e-02 -9.6477866e-03] Sparsity at: 0.6441059353869272 Epoch 80/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1461 - accuracy: 0.9783 - val_loss: 0.2220 - val_accuracy: 0.9553 [ 5.0166462e-34 0.0000000e+00 6.7258503e-07 ... -8.7429502e-04 -2.1387301e-02 -1.0669601e-02] Sparsity at: 0.6441059353869272 Epoch 81/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1487 - accuracy: 0.9778 - val_loss: 0.1931 - val_accuracy: 0.9651 [ 5.0166462e-34 0.0000000e+00 -6.0830421e-12 ... -3.3901175e-03 -1.9290127e-02 -1.2895949e-02] Sparsity at: 0.6441059353869272 Epoch 82/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1483 - accuracy: 0.9777 - val_loss: 0.2021 - val_accuracy: 0.9599 [ 5.0166462e-34 0.0000000e+00 1.5140465e-05 ... -8.1940005e-03 -2.1523420e-02 -2.2413734e-02] Sparsity at: 0.6441059353869272 Epoch 83/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1486 - accuracy: 0.9773 - val_loss: 0.2217 - val_accuracy: 0.9569 [ 5.0166462e-34 0.0000000e+00 6.8045181e-11 ... -5.2355332e-03 -7.5918632e-03 -2.2861354e-02] Sparsity at: 0.6441059353869272 Epoch 84/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9780 - val_loss: 0.2229 - val_accuracy: 0.9584 [ 5.0166462e-34 0.0000000e+00 6.1325278e-05 ... 1.3714503e-03 -2.0043679e-02 -1.7996848e-02] Sparsity at: 0.6441059353869272 Epoch 85/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1463 - accuracy: 0.9782 - val_loss: 0.1940 - val_accuracy: 0.9661 [ 5.0166462e-34 0.0000000e+00 -6.1296201e-10 ... 1.4417048e-03 -2.0907404e-02 -7.3444662e-03] Sparsity at: 0.6441059353869272 Epoch 86/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1471 - accuracy: 0.9783 - val_loss: 0.1946 - val_accuracy: 0.9628 [ 5.0166462e-34 0.0000000e+00 -7.0597839e-09 ... -1.6848978e-02 -1.2460677e-02 -1.6703008e-02] Sparsity at: 0.6441059353869272 Epoch 87/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1479 - accuracy: 0.9777 - val_loss: 0.2036 - val_accuracy: 0.9604 [ 5.0166462e-34 0.0000000e+00 -3.1662686e-09 ... -3.4173774e-03 -1.5515212e-02 -1.9193310e-02] Sparsity at: 0.6441059353869272 Epoch 88/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1458 - accuracy: 0.9779 - val_loss: 0.2112 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 -5.5103916e-12 ... -3.9985105e-03 -1.6525606e-02 -1.8901506e-02] Sparsity at: 0.6441059353869272 Epoch 89/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1428 - accuracy: 0.9792 - val_loss: 0.2194 - val_accuracy: 0.9612 [ 5.0166462e-34 0.0000000e+00 -6.1624661e-09 ... -2.5452795e-03 -8.1167966e-03 -1.7700922e-02] Sparsity at: 0.6441059353869272 Epoch 90/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9775 - val_loss: 0.1938 - val_accuracy: 0.9658 [ 5.0166462e-34 0.0000000e+00 -2.4435442e-13 ... 1.3421847e-03 -1.6745526e-02 -2.0333633e-02] Sparsity at: 0.6441059353869272 Epoch 91/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1465 - accuracy: 0.9779 - val_loss: 0.2139 - val_accuracy: 0.9597 [ 5.0166462e-34 0.0000000e+00 -1.3257443e-07 ... -6.0850023e-03 -1.0935331e-02 -1.6774198e-02] Sparsity at: 0.6441059353869272 Epoch 92/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1464 - accuracy: 0.9781 - val_loss: 0.2053 - val_accuracy: 0.9613 [ 5.0166462e-34 0.0000000e+00 2.1528997e-13 ... -1.7070226e-02 -2.2637552e-02 -1.1558738e-02] Sparsity at: 0.6441059353869272 Epoch 93/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9778 - val_loss: 0.2040 - val_accuracy: 0.9612 [ 5.0166462e-34 0.0000000e+00 1.5209645e-07 ... -1.7273937e-03 -1.8124724e-02 -2.0251274e-02] Sparsity at: 0.6441059353869272 Epoch 94/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1470 - accuracy: 0.9778 - val_loss: 0.2489 - val_accuracy: 0.9530 [ 5.01664622e-34 0.00000000e+00 1.05434324e-14 ... -5.50695695e-03 -2.21271366e-02 -1.62406899e-02] Sparsity at: 0.6441059353869272 Epoch 95/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9777 - val_loss: 0.2087 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 -3.7033842e-06 ... -8.3438465e-03 -1.8339496e-02 -1.5137114e-02] Sparsity at: 0.6441059353869272 Epoch 96/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1458 - accuracy: 0.9783 - val_loss: 0.2354 - val_accuracy: 0.9507 [ 5.01664622e-34 0.00000000e+00 -1.52993347e-11 ... -1.13332085e-02 -1.55136567e-02 -4.46621887e-03] Sparsity at: 0.6441059353869272 Epoch 97/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1441 - accuracy: 0.9784 - val_loss: 0.2132 - val_accuracy: 0.9597 0s - loss: 0 [ 5.0166462e-34 0.0000000e+00 -3.3091434e-05 ... -4.2730221e-03 -1.7229708e-02 -7.2580515e-03] Sparsity at: 0.6441059353869272 Epoch 98/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1447 - accuracy: 0.9787 - val_loss: 0.2106 - val_accuracy: 0.9601 [ 5.0166462e-34 0.0000000e+00 3.1454078e-10 ... -3.7654387e-03 -2.0741191e-02 -1.1330617e-02] Sparsity at: 0.6441059353869272 Epoch 99/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1472 - accuracy: 0.9773 - val_loss: 0.2123 - val_accuracy: 0.9622 [ 5.0166462e-34 0.0000000e+00 3.4703738e-09 ... -8.4781321e-03 -2.5007870e-02 -3.4283102e-03] Sparsity at: 0.6441059353869272 Epoch 100/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1500 - accuracy: 0.9768 - val_loss: 0.2188 - val_accuracy: 0.9575 [ 5.0166462e-34 0.0000000e+00 -3.4889458e-09 ... -5.7886345e-03 -1.3090068e-02 -4.3662172e-03] Sparsity at: 0.6441059353869272 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 4.843794464026135e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 7.005245859754415e-08 Thresholhold -1.1641729358302655e-08 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 0. 0. 1.] ... [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.04016310498981923 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 294s 15ms/step - loss: 0.1452 - accuracy: 0.9786 - val_loss: 0.2239 - val_accuracy: 0.9571 [ 5.0166462e-34 0.0000000e+00 1.6359491e-13 ... -4.5295502e-03 -1.3375368e-02 -4.4609844e-03] Sparsity at: 0.6441059353869272 Epoch 102/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1509 - accuracy: 0.9769 - val_loss: 0.2283 - val_accuracy: 0.9563 [ 5.0166462e-34 0.0000000e+00 5.8804844e-08 ... -6.9973022e-03 -1.6044024e-02 -9.6602067e-03] Sparsity at: 0.6441059353869272 Epoch 103/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1468 - accuracy: 0.9781 - val_loss: 0.1983 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 -7.2934551e-15 ... -3.6859934e-03 -1.6095224e-03 -2.1698272e-02] Sparsity at: 0.6441059353869272 Epoch 104/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1465 - accuracy: 0.9772 - val_loss: 0.2169 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 -1.3184379e-06 ... -8.1925420e-03 -8.4327478e-03 -2.7418265e-02] Sparsity at: 0.6441059353869272 Epoch 105/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1440 - accuracy: 0.9786 - val_loss: 0.2141 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 -7.4742651e-12 ... 2.4576788e-03 -6.0032932e-03 -1.8727563e-02] Sparsity at: 0.6441059353869272 Epoch 106/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1446 - accuracy: 0.9787 - val_loss: 0.2119 - val_accuracy: 0.9598 [ 5.0166462e-34 0.0000000e+00 -2.2651082e-05 ... -5.7037799e-03 -9.7872298e-03 -1.5475204e-02] Sparsity at: 0.6441059353869272 Epoch 107/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1491 - accuracy: 0.9773 - val_loss: 0.2048 - val_accuracy: 0.9632 [ 5.01664622e-34 0.00000000e+00 -8.17483026e-11 ... -8.65891483e-03 -1.50544485e-02 -1.73985399e-02] Sparsity at: 0.6441059353869272 Epoch 108/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1429 - accuracy: 0.9789 - val_loss: 0.1970 - val_accuracy: 0.9640 [ 5.0166462e-34 0.0000000e+00 1.8529958e-04 ... 1.0554134e-02 -1.5312563e-02 -1.6008602e-02] Sparsity at: 0.6441059353869272 Epoch 109/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1458 - accuracy: 0.9775 - val_loss: 0.2070 - val_accuracy: 0.9609 [ 5.0166462e-34 0.0000000e+00 7.8281592e-10 ... 2.0660977e-03 -1.0992130e-02 -1.0932870e-02] Sparsity at: 0.6441059353869272 Epoch 110/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9780 - val_loss: 0.2190 - val_accuracy: 0.9578 [ 5.0166462e-34 0.0000000e+00 1.5836985e-08 ... -2.8291738e-03 -1.0496937e-02 -1.3941357e-02] Sparsity at: 0.6441059353869272 Epoch 111/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1463 - accuracy: 0.9772 - val_loss: 0.1908 - val_accuracy: 0.9639 [ 5.0166462e-34 0.0000000e+00 -2.0012987e-09 ... 1.8041985e-03 -1.7547794e-02 -1.3510820e-02] Sparsity at: 0.6441059353869272 Epoch 112/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1441 - accuracy: 0.9788 - val_loss: 0.2137 - val_accuracy: 0.9591 [ 5.0166462e-34 0.0000000e+00 -2.4272511e-11 ... -6.7572546e-05 -1.2409200e-02 -1.3275783e-02] Sparsity at: 0.6441059353869272 Epoch 113/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9780 - val_loss: 0.1923 - val_accuracy: 0.9642 [ 5.0166462e-34 0.0000000e+00 1.4537335e-08 ... 4.4772658e-04 -1.8451089e-02 -7.5737289e-03] Sparsity at: 0.6441059353869272 Epoch 114/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1429 - accuracy: 0.9790 - val_loss: 0.1950 - val_accuracy: 0.9670 [ 5.0166462e-34 0.0000000e+00 -1.0241354e-12 ... -9.4325794e-04 -1.3005402e-02 -1.4674718e-02] Sparsity at: 0.6441059353869272 Epoch 115/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1416 - accuracy: 0.9783 - val_loss: 0.1990 - val_accuracy: 0.9634 [ 5.01664622e-34 0.00000000e+00 4.82964779e-08 ... -1.43142715e-02 -1.02217747e-02 -9.05843358e-03] Sparsity at: 0.6441059353869272 Epoch 116/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9785 - val_loss: 0.2259 - val_accuracy: 0.9569 [ 5.0166462e-34 0.0000000e+00 5.3147428e-13 ... -8.8199405e-03 -5.7418658e-03 -1.5898623e-02] Sparsity at: 0.6441059353869272 Epoch 117/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1395 - accuracy: 0.9798 - val_loss: 0.2017 - val_accuracy: 0.9621 [ 5.0166462e-34 0.0000000e+00 -1.3179192e-07 ... -8.7538138e-03 -1.5333158e-02 -9.5256632e-03] Sparsity at: 0.6441059353869272 Epoch 118/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1421 - accuracy: 0.9780 - val_loss: 0.2504 - val_accuracy: 0.9473 [ 5.0166462e-34 0.0000000e+00 -8.4940258e-13 ... -5.1972885e-03 -1.3927585e-02 -7.8176390e-03] Sparsity at: 0.6441059353869272 Epoch 119/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1430 - accuracy: 0.9787 - val_loss: 0.2387 - val_accuracy: 0.9514 [ 5.0166462e-34 0.0000000e+00 1.0370704e-07 ... -6.9519563e-04 -1.0389775e-02 -1.6700611e-02] Sparsity at: 0.6441059353869272 Epoch 120/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1436 - accuracy: 0.9780 - val_loss: 0.1896 - val_accuracy: 0.9659 [ 5.0166462e-34 0.0000000e+00 2.1134531e-12 ... -4.9449233e-03 -1.6929176e-02 -1.8318223e-02] Sparsity at: 0.6441059353869272 Epoch 121/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1413 - accuracy: 0.9786 - val_loss: 0.2451 - val_accuracy: 0.9497 [ 5.0166462e-34 0.0000000e+00 -2.5602327e-07 ... -1.3167212e-02 -1.8170573e-02 -1.7048089e-02] Sparsity at: 0.6441059353869272 Epoch 122/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1401 - accuracy: 0.9793 - val_loss: 0.1895 - val_accuracy: 0.9649 [ 5.0166462e-34 0.0000000e+00 -3.3407982e-13 ... -7.8073479e-03 -1.5467769e-02 -1.1018596e-02] Sparsity at: 0.6441059353869272 Epoch 123/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1420 - accuracy: 0.9786 - val_loss: 0.2002 - val_accuracy: 0.9649 [ 5.016646e-34 0.000000e+00 -5.315796e-07 ... -6.627698e-03 -1.676392e-02 -5.065035e-03] Sparsity at: 0.6441059353869272 Epoch 124/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1430 - accuracy: 0.9783 - val_loss: 0.1982 - val_accuracy: 0.9637 [ 5.0166462e-34 0.0000000e+00 -5.5410776e-12 ... -3.3665195e-03 -1.3678995e-02 -7.4321218e-03] Sparsity at: 0.6441059353869272 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9783 - val_loss: 0.1996 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 -1.1658190e-06 ... -6.3401705e-04 -5.7373382e-03 -1.3058696e-02] Sparsity at: 0.6441059353869272 Epoch 126/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1428 - accuracy: 0.9790 - val_loss: 0.2054 - val_accuracy: 0.9619 [ 5.0166462e-34 0.0000000e+00 -2.5690450e-11 ... -6.6906838e-03 -1.0618143e-02 -1.6082095e-02] Sparsity at: 0.6441059353869272 Epoch 127/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1433 - accuracy: 0.9785 - val_loss: 0.2406 - val_accuracy: 0.9519 [ 5.0166462e-34 0.0000000e+00 -8.1194667e-06 ... -1.3427250e-02 1.4790015e-03 -1.8008687e-02] Sparsity at: 0.6441059353869272 Epoch 128/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1435 - accuracy: 0.9787 - val_loss: 0.1943 - val_accuracy: 0.9638 [ 5.0166462e-34 0.0000000e+00 2.4100999e-11 ... -9.7214151e-03 -1.6177583e-02 -2.3430079e-02] Sparsity at: 0.6441059353869272 Epoch 129/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1420 - accuracy: 0.9785 - val_loss: 0.2121 - val_accuracy: 0.9599 [ 5.0166462e-34 0.0000000e+00 8.8364341e-06 ... -1.7442445e-03 -1.3602024e-02 -2.8492359e-02] Sparsity at: 0.6441059353869272 Epoch 130/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1413 - accuracy: 0.9792 - val_loss: 0.2103 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 -1.4257053e-09 ... -1.4578969e-03 -6.7315223e-03 -2.0727526e-02] Sparsity at: 0.6441059353869272 Epoch 131/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1404 - accuracy: 0.9789 - val_loss: 0.2277 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 1.0530149e-10 ... -6.1588828e-03 -5.3962730e-03 -1.4300667e-02] Sparsity at: 0.6441059353869272 Epoch 132/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1423 - accuracy: 0.9787 - val_loss: 0.2316 - val_accuracy: 0.9550 [ 5.0166462e-34 0.0000000e+00 3.8762371e-09 ... 1.0364726e-03 -1.5876614e-02 -8.6401878e-03] Sparsity at: 0.6441059353869272 Epoch 133/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9780 - val_loss: 0.1938 - val_accuracy: 0.9656 [ 5.0166462e-34 0.0000000e+00 -4.4175351e-13 ... -4.8034624e-03 -6.9611799e-03 -7.5629111e-03] Sparsity at: 0.6441059353869272 Epoch 134/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1420 - accuracy: 0.9781 - val_loss: 0.1989 - val_accuracy: 0.9625 [ 5.016646e-34 0.000000e+00 -6.965106e-08 ... -6.485253e-03 -5.513031e-03 -9.988348e-03] Sparsity at: 0.6441059353869272 Epoch 135/500 235/235 [==============================] - 5s 19ms/step - loss: 0.1400 - accuracy: 0.9784 - val_loss: 0.2211 - val_accuracy: 0.9570 [ 5.0166462e-34 0.0000000e+00 -5.4137809e-13 ... -5.8609359e-03 -5.4674139e-03 -1.3368375e-02] Sparsity at: 0.6441059353869272 Epoch 136/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1406 - accuracy: 0.9794 - val_loss: 0.2129 - val_accuracy: 0.9589 [ 5.0166462e-34 0.0000000e+00 1.8019051e-08 ... -9.6572062e-04 -1.3946003e-02 -7.3130005e-03] Sparsity at: 0.6441059353869272 Epoch 137/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1421 - accuracy: 0.9781 - val_loss: 0.2171 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 5.9782799e-13 ... -3.1107927e-03 -6.7630424e-03 -1.2689728e-02] Sparsity at: 0.6441059353869272 Epoch 138/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1452 - accuracy: 0.9776 - val_loss: 0.2126 - val_accuracy: 0.9597 [ 5.0166462e-34 0.0000000e+00 7.9289117e-07 ... -4.2895153e-03 -3.7860803e-03 -1.5833996e-02] Sparsity at: 0.6441059353869272 Epoch 139/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9779 - val_loss: 0.2242 - val_accuracy: 0.9598 [ 5.0166462e-34 0.0000000e+00 -6.3239813e-12 ... -1.6388968e-02 -1.0843138e-03 -1.3391660e-02] Sparsity at: 0.6441059353869272 Epoch 140/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1410 - accuracy: 0.9782 - val_loss: 0.2220 - val_accuracy: 0.9565 [ 5.0166462e-34 0.0000000e+00 2.6486509e-06 ... -1.8014932e-02 4.5895278e-03 -6.4535742e-04] Sparsity at: 0.6441059353869272 Epoch 141/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1426 - accuracy: 0.9781 - val_loss: 0.2058 - val_accuracy: 0.9629 [ 5.0166462e-34 0.0000000e+00 3.0427015e-12 ... -1.1716771e-02 -1.1202599e-02 -2.1562644e-03] Sparsity at: 0.6441059353869272 Epoch 142/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1400 - accuracy: 0.9794 - val_loss: 0.2095 - val_accuracy: 0.9619 [ 5.0166462e-34 0.0000000e+00 2.0596019e-05 ... -1.3577618e-02 -9.6821934e-03 -8.6959582e-03] Sparsity at: 0.6441059353869272 Epoch 143/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1394 - accuracy: 0.9793 - val_loss: 0.2424 - val_accuracy: 0.9495 [ 5.0166462e-34 0.0000000e+00 1.4210935e-10 ... -2.0182857e-02 -1.4675720e-02 2.8802040e-03] Sparsity at: 0.6441059353869272 Epoch 144/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1406 - accuracy: 0.9790 - val_loss: 0.2081 - val_accuracy: 0.9618 [ 5.0166462e-34 0.0000000e+00 -2.2241055e-07 ... -2.7923353e-02 -1.2339463e-02 5.1958975e-03] Sparsity at: 0.6441059353869272 Epoch 145/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1420 - accuracy: 0.9782 - val_loss: 0.2035 - val_accuracy: 0.9623 [ 5.0166462e-34 0.0000000e+00 -2.5706077e-09 ... -1.5227474e-02 -1.3130808e-02 9.5693553e-03] Sparsity at: 0.6441059353869272 Epoch 146/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1418 - accuracy: 0.9793 - val_loss: 0.2013 - val_accuracy: 0.9632 [ 5.0166462e-34 0.0000000e+00 8.8383066e-13 ... -2.8191656e-02 -6.4869400e-04 -9.0897731e-05] Sparsity at: 0.6441059353869272 Epoch 147/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9795 - val_loss: 0.2008 - val_accuracy: 0.9635 [ 5.0166462e-34 0.0000000e+00 -4.4478345e-08 ... -1.9833276e-02 2.4158790e-04 -6.5873638e-03] Sparsity at: 0.6441059353869272 Epoch 148/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1367 - accuracy: 0.9798 - val_loss: 0.2053 - val_accuracy: 0.9599 [ 5.0166462e-34 0.0000000e+00 -3.7843570e-13 ... -2.2054341e-02 -1.2026252e-03 -1.3040462e-02] Sparsity at: 0.6441059353869272 Epoch 149/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2027 - val_accuracy: 0.9633 [ 5.0166462e-34 0.0000000e+00 1.2201292e-07 ... -3.1325094e-02 -7.8931851e-03 -1.0048496e-02] Sparsity at: 0.6441059353869272 Epoch 150/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1409 - accuracy: 0.9785 - val_loss: 0.1932 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 -8.1458473e-13 ... -2.8685726e-02 -1.4671220e-02 -1.1146025e-02] Sparsity at: 0.6441059353869272 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 5.561928792053335e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 1.7915367989284735e-05 Thresholhold 1.4058052329346538e-05 Using suggest threshold. Applying new mask Percentage zeros 0.84183335 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.048193727521117724 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 330s 16ms/step - loss: 0.1398 - accuracy: 0.9791 - val_loss: 0.2020 - val_accuracy: 0.9616 [ 5.0166462e-34 0.0000000e+00 -1.0428030e-06 ... -1.3755406e-02 -1.0549697e-02 -9.6231019e-03] Sparsity at: 0.666190833959429 Epoch 152/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9788 - val_loss: 0.2024 - val_accuracy: 0.9629 [ 5.0166462e-34 0.0000000e+00 -1.1950715e-11 ... -2.7210250e-02 1.9448778e-03 -1.7553082e-02] Sparsity at: 0.666190833959429 Epoch 153/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1379 - accuracy: 0.9793 - val_loss: 0.2246 - val_accuracy: 0.9553 [ 5.0166462e-34 0.0000000e+00 1.4553223e-05 ... -2.3162231e-02 6.8728172e-04 -1.1814996e-02] Sparsity at: 0.666190833959429 Epoch 154/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1409 - accuracy: 0.9785 - val_loss: 0.2271 - val_accuracy: 0.9546 [ 5.0166462e-34 0.0000000e+00 -9.7470670e-11 ... -2.3616191e-02 -7.4772034e-03 -1.7338131e-02] Sparsity at: 0.666190833959429 Epoch 155/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1414 - accuracy: 0.9789 - val_loss: 0.2001 - val_accuracy: 0.9628 [ 5.0166462e-34 0.0000000e+00 4.7177826e-05 ... -2.1276670e-02 -1.0550897e-02 1.1847372e-04] Sparsity at: 0.666190833959429 Epoch 156/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1419 - accuracy: 0.9784 - val_loss: 0.1943 - val_accuracy: 0.9636 [ 5.0166462e-34 0.0000000e+00 4.3932383e-10 ... -1.4112723e-02 6.8439594e-03 -1.0257018e-02] Sparsity at: 0.666190833959429 Epoch 157/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1392 - accuracy: 0.9797 - val_loss: 0.2009 - val_accuracy: 0.9645 [ 5.0166462e-34 0.0000000e+00 -2.6344167e-06 ... -4.0357420e-03 -1.1591322e-02 -1.5631227e-02] Sparsity at: 0.666190833959429 Epoch 158/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1396 - accuracy: 0.9792 - val_loss: 0.1950 - val_accuracy: 0.9642 [ 5.0166462e-34 0.0000000e+00 1.0939798e-09 ... -1.2036924e-02 -1.5667819e-03 -1.1506682e-02] Sparsity at: 0.666190833959429 Epoch 159/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1340 - accuracy: 0.9804 - val_loss: 0.1983 - val_accuracy: 0.9636 [ 5.0166462e-34 0.0000000e+00 -3.3897234e-09 ... -9.2213098e-03 -8.3209975e-03 -9.8570054e-03] Sparsity at: 0.666190833959429 Epoch 160/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1410 - accuracy: 0.9784 - val_loss: 0.2082 - val_accuracy: 0.9606 [ 5.0166462e-34 0.0000000e+00 6.3108283e-09 ... -1.6320411e-02 -7.0029320e-03 -9.7289449e-03] Sparsity at: 0.666190833959429 Epoch 161/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1401 - accuracy: 0.9785 - val_loss: 0.2060 - val_accuracy: 0.9611 [ 5.0166462e-34 0.0000000e+00 3.2755746e-11 ... -1.9962240e-02 -3.2161083e-03 -8.7259011e-03] Sparsity at: 0.666190833959429 Epoch 162/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1422 - accuracy: 0.9783 - val_loss: 0.1984 - val_accuracy: 0.9649 [ 5.0166462e-34 0.0000000e+00 -3.6269352e-09 ... -1.4742458e-02 -9.9299923e-03 -9.6295439e-03] Sparsity at: 0.666190833959429 Epoch 163/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1404 - accuracy: 0.9789 - val_loss: 0.2012 - val_accuracy: 0.9640 0s - loss: [ 5.0166462e-34 0.0000000e+00 6.7924111e-13 ... -2.4452128e-02 -1.8198988e-03 -2.6614077e-03] Sparsity at: 0.666190833959429 Epoch 164/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1392 - accuracy: 0.9797 - val_loss: 0.1992 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 -2.1372152e-08 ... -2.8700300e-02 -1.0452360e-03 -5.4271794e-03] Sparsity at: 0.666190833959429 Epoch 165/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1405 - accuracy: 0.9791 - val_loss: 0.2055 - val_accuracy: 0.9601 [ 5.0166462e-34 0.0000000e+00 -4.4497170e-13 ... -2.5781730e-02 2.3416094e-03 -1.2780037e-03] Sparsity at: 0.666190833959429 Epoch 166/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1362 - accuracy: 0.9795 - val_loss: 0.2041 - val_accuracy: 0.9608 [ 5.0166462e-34 0.0000000e+00 1.3665303e-07 ... -2.1483352e-02 -1.0857211e-02 4.1082455e-03] Sparsity at: 0.666190833959429 Epoch 167/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1411 - accuracy: 0.9785 - val_loss: 0.2248 - val_accuracy: 0.9540 [ 5.0166462e-34 0.0000000e+00 1.7451446e-12 ... -2.4913087e-02 -4.2846669e-03 -3.5073892e-03] Sparsity at: 0.666190833959429 Epoch 168/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9795 - val_loss: 0.1889 - val_accuracy: 0.9658 [ 5.0166462e-34 0.0000000e+00 -1.2982126e-06 ... -1.3665289e-02 -1.4549807e-02 -1.5015699e-02] Sparsity at: 0.666190833959429 Epoch 169/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1421 - accuracy: 0.9788 - val_loss: 0.2104 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 8.4399840e-12 ... -2.2227364e-02 -9.4286175e-03 -5.9940829e-03] Sparsity at: 0.666190833959429 Epoch 170/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1419 - accuracy: 0.9785 - val_loss: 0.2022 - val_accuracy: 0.9611 [ 5.0166462e-34 0.0000000e+00 -1.2240731e-05 ... -2.2379842e-02 -5.5304966e-03 -1.1375462e-02] Sparsity at: 0.666190833959429 Epoch 171/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1396 - accuracy: 0.9787 - val_loss: 0.2275 - val_accuracy: 0.9545 [ 5.0166462e-34 0.0000000e+00 4.0207782e-11 ... -2.3706652e-02 -1.4869887e-02 -1.5742071e-02] Sparsity at: 0.666190833959429 Epoch 172/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.2255 - val_accuracy: 0.9567 [ 5.0166462e-34 0.0000000e+00 3.3586414e-07 ... -2.2746198e-02 -1.8505011e-03 -1.3185559e-02] Sparsity at: 0.666190833959429 Epoch 173/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1446 - accuracy: 0.9776 - val_loss: 0.2494 - val_accuracy: 0.9472 [ 5.0166462e-34 0.0000000e+00 2.6877958e-09 ... -8.9747962e-03 -6.7959023e-03 -8.7169651e-03] Sparsity at: 0.666190833959429 Epoch 174/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1380 - accuracy: 0.9797 - val_loss: 0.2292 - val_accuracy: 0.9531 [ 5.0166462e-34 0.0000000e+00 2.2312755e-12 ... -2.3496486e-02 -6.9485772e-03 -1.3585822e-02] Sparsity at: 0.666190833959429 Epoch 175/500 235/235 [==============================] - 5s 19ms/step - loss: 0.1399 - accuracy: 0.9786 - val_loss: 0.1941 - val_accuracy: 0.9615 [ 5.0166462e-34 0.0000000e+00 2.8667881e-08 ... -7.3658563e-03 -4.9074353e-03 -9.2885997e-03] Sparsity at: 0.666190833959429 Epoch 176/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1360 - accuracy: 0.9800 - val_loss: 0.1938 - val_accuracy: 0.9634 [ 5.01664622e-34 0.00000000e+00 -1.13291944e-13 ... -1.52935423e-02 5.22935670e-03 -1.65853910e-02] Sparsity at: 0.666190833959429 Epoch 177/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.2191 - val_accuracy: 0.9560 [ 5.0166462e-34 0.0000000e+00 8.5603986e-08 ... -2.6603287e-02 2.4573365e-03 -2.1253873e-02] Sparsity at: 0.666190833959429 Epoch 178/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.1863 - val_accuracy: 0.9681 [ 5.0166462e-34 0.0000000e+00 3.5700476e-13 ... -2.3556106e-02 -6.5545430e-03 -6.2924280e-04] Sparsity at: 0.666190833959429 Epoch 179/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1388 - accuracy: 0.9798 - val_loss: 0.2111 - val_accuracy: 0.9585 [ 5.0166462e-34 0.0000000e+00 2.4601832e-07 ... -2.7308574e-02 -4.7198711e-03 -5.8484529e-03] Sparsity at: 0.666190833959429 Epoch 180/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1386 - accuracy: 0.9786 - val_loss: 0.1787 - val_accuracy: 0.9692 [ 5.0166462e-34 0.0000000e+00 2.5584331e-13 ... -7.2520124e-03 -6.7848782e-03 -2.7476251e-03] Sparsity at: 0.666190833959429 Epoch 181/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1387 - accuracy: 0.9795 - val_loss: 0.2056 - val_accuracy: 0.9603 [ 5.0166462e-34 0.0000000e+00 4.0222976e-06 ... -1.6783053e-02 1.8976850e-03 -1.4208882e-02] Sparsity at: 0.666190833959429 Epoch 182/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1390 - accuracy: 0.9790 - val_loss: 0.2564 - val_accuracy: 0.9451 [ 5.0166462e-34 0.0000000e+00 2.0630522e-11 ... -1.5837548e-02 -5.7896036e-03 -1.1394896e-02] Sparsity at: 0.666190833959429 Epoch 183/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9797 - val_loss: 0.1918 - val_accuracy: 0.9634 [ 5.0166462e-34 0.0000000e+00 2.9752009e-05 ... -2.2433538e-02 -1.5479299e-03 -1.8344555e-02] Sparsity at: 0.666190833959429 Epoch 184/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1365 - accuracy: 0.9791 - val_loss: 0.2368 - val_accuracy: 0.9497 [ 5.01664622e-34 0.00000000e+00 1.51760132e-10 ... -1.29018575e-02 3.42086283e-03 -1.33878635e-02] Sparsity at: 0.666190833959429 Epoch 185/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9793 - val_loss: 0.2352 - val_accuracy: 0.9497 [ 5.0166462e-34 0.0000000e+00 4.9737298e-07 ... -1.1504206e-02 4.6023787e-03 -1.1970945e-02] Sparsity at: 0.666190833959429 Epoch 186/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1400 - accuracy: 0.9791 - val_loss: 0.2036 - val_accuracy: 0.9618 [ 5.0166462e-34 0.0000000e+00 -2.3621656e-09 ... -8.8635217e-03 1.9548852e-02 -1.1269747e-02] Sparsity at: 0.666190833959429 Epoch 187/500 235/235 [==============================] - 5s 19ms/step - loss: 0.1379 - accuracy: 0.9798 - val_loss: 0.2146 - val_accuracy: 0.9601 [ 5.0166462e-34 0.0000000e+00 7.6479573e-11 ... -3.4631761e-03 9.5573440e-03 -1.7066875e-02] Sparsity at: 0.666190833959429 Epoch 188/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1363 - accuracy: 0.9796 - val_loss: 0.1877 - val_accuracy: 0.9646 [ 5.0166462e-34 0.0000000e+00 -1.6892114e-09 ... -6.7601171e-03 1.0735933e-02 -9.9162692e-03] Sparsity at: 0.666190833959429 Epoch 189/500 235/235 [==============================] - 4s 19ms/step - loss: 0.1388 - accuracy: 0.9788 - val_loss: 0.1918 - val_accuracy: 0.9635 [ 5.0166462e-34 0.0000000e+00 -1.8575834e-13 ... -1.0103357e-02 6.3989991e-03 -5.3814324e-03] Sparsity at: 0.666190833959429 Epoch 190/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1387 - accuracy: 0.9792 - val_loss: 0.1942 - val_accuracy: 0.9633 [ 5.016646e-34 0.000000e+00 -5.643024e-08 ... -5.884890e-03 9.144009e-04 -9.003337e-03] Sparsity at: 0.666190833959429 Epoch 191/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1360 - accuracy: 0.9797 - val_loss: 0.2116 - val_accuracy: 0.9584 [ 5.0166462e-34 0.0000000e+00 -6.5754731e-13 ... -3.4714427e-03 1.1487506e-02 -3.1153958e-03] Sparsity at: 0.666190833959429 Epoch 192/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1375 - accuracy: 0.9793 - val_loss: 0.2352 - val_accuracy: 0.9514 [ 5.0166462e-34 0.0000000e+00 6.8042425e-07 ... -6.5463083e-03 1.7846018e-02 -1.0635743e-02] Sparsity at: 0.666190833959429 Epoch 193/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1400 - accuracy: 0.9785 - val_loss: 0.1945 - val_accuracy: 0.9641 [ 5.0166462e-34 0.0000000e+00 1.2146959e-12 ... -5.3144572e-03 1.3442005e-02 -1.1392523e-02] Sparsity at: 0.666190833959429 Epoch 194/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9802 - val_loss: 0.1941 - val_accuracy: 0.9644 [ 5.0166462e-34 0.0000000e+00 -1.8330716e-06 ... -1.5116739e-03 5.0201197e-03 -1.5820652e-02] Sparsity at: 0.666190833959429 Epoch 195/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1361 - accuracy: 0.9797 - val_loss: 0.2186 - val_accuracy: 0.9568 [ 5.0166462e-34 0.0000000e+00 2.3349385e-11 ... -1.7341573e-02 1.3230094e-02 -7.9691513e-03] Sparsity at: 0.666190833959429 Epoch 196/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.1895 - val_accuracy: 0.9646 [ 5.0166462e-34 0.0000000e+00 -1.2386603e-05 ... -9.1612972e-03 9.3596466e-03 -6.0775355e-03] Sparsity at: 0.666190833959429 Epoch 197/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1362 - accuracy: 0.9798 - val_loss: 0.2263 - val_accuracy: 0.9529 [ 5.0166462e-34 0.0000000e+00 -4.0690201e-11 ... -1.4374478e-02 1.6040500e-02 -4.7292556e-03] Sparsity at: 0.666190833959429 Epoch 198/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1398 - accuracy: 0.9785 - val_loss: 0.1862 - val_accuracy: 0.9661 [ 5.0166462e-34 0.0000000e+00 -4.9593000e-05 ... -1.0642501e-02 6.1431583e-03 -5.4059713e-03] Sparsity at: 0.666190833959429 Epoch 199/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1369 - accuracy: 0.9797 - val_loss: 0.2385 - val_accuracy: 0.9486 [ 5.0166462e-34 0.0000000e+00 2.4752533e-10 ... -8.4419176e-03 3.0181359e-03 -8.3414475e-03] Sparsity at: 0.666190833959429 Epoch 200/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1387 - accuracy: 0.9797 - val_loss: 0.1900 - val_accuracy: 0.9654 [ 5.01664622e-34 0.00000000e+00 6.58159569e-11 ... -1.37163531e-02 1.26233045e-02 1.03443069e-02] Sparsity at: 0.666190833959429 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.0008035016810398754 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 2.352087186751218e-05 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.84183335 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.05744847457906932 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 320s 16ms/step - loss: 0.1377 - accuracy: 0.9795 - val_loss: 0.1851 - val_accuracy: 0.9653 [ 5.0166462e-34 0.0000000e+00 4.8755098e-09 ... -1.3043190e-02 4.4964748e-03 -5.4804692e-03] Sparsity at: 0.666190833959429 Epoch 202/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9796 - val_loss: 0.1951 - val_accuracy: 0.9628 [ 5.0166462e-34 0.0000000e+00 6.6320593e-14 ... -1.7589958e-02 2.0040069e-03 -8.5466104e-03] Sparsity at: 0.666190833959429 Epoch 203/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1418 - accuracy: 0.9776 - val_loss: 0.2233 - val_accuracy: 0.9564 [ 5.01664622e-34 0.00000000e+00 2.25073222e-07 ... -1.64648555e-02 -1.13766305e-02 -2.02886648e-02] Sparsity at: 0.666190833959429 Epoch 204/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9804 - val_loss: 0.1888 - val_accuracy: 0.9651 [ 5.0166462e-34 0.0000000e+00 1.2062463e-12 ... -8.8463724e-03 6.7390283e-05 -8.8814348e-03] Sparsity at: 0.666190833959429 Epoch 205/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1387 - accuracy: 0.9789 - val_loss: 0.2194 - val_accuracy: 0.9541 [ 5.0166462e-34 0.0000000e+00 7.5119174e-06 ... -2.0170030e-03 -2.6937143e-03 -1.1178119e-02] Sparsity at: 0.666190833959429 Epoch 206/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1359 - accuracy: 0.9802 - val_loss: 0.2448 - val_accuracy: 0.9467 [ 5.0166462e-34 0.0000000e+00 -1.4233156e-11 ... -1.0380659e-02 2.4750275e-03 -9.6675260e-03] Sparsity at: 0.666190833959429 Epoch 207/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1378 - accuracy: 0.9789 - val_loss: 0.2178 - val_accuracy: 0.9590 [ 5.0166462e-34 0.0000000e+00 1.2507968e-04 ... -1.5268379e-02 -2.2254761e-03 -1.1236632e-02] Sparsity at: 0.666190833959429 Epoch 208/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1372 - accuracy: 0.9792 - val_loss: 0.1893 - val_accuracy: 0.9661 [ 5.0166462e-34 0.0000000e+00 -5.8355049e-10 ... -9.2871971e-03 -3.8212744e-04 -1.4200823e-02] Sparsity at: 0.666190833959429 Epoch 209/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1366 - accuracy: 0.9794 - val_loss: 0.2224 - val_accuracy: 0.9556 [ 5.0166462e-34 0.0000000e+00 -5.3764255e-09 ... -1.7078537e-02 2.6902920e-03 -1.6608965e-02] Sparsity at: 0.666190833959429 Epoch 210/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1371 - accuracy: 0.9789 - val_loss: 0.2162 - val_accuracy: 0.9556 [ 5.0166462e-34 0.0000000e+00 3.2753267e-10 ... -1.7577101e-02 3.3908749e-03 -1.7387353e-02] Sparsity at: 0.666190833959429 Epoch 211/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1380 - accuracy: 0.9791 - val_loss: 0.2367 - val_accuracy: 0.9520 [ 5.0166462e-34 0.0000000e+00 2.7855871e-12 ... -1.8230313e-02 4.1310475e-03 -6.3017914e-03] Sparsity at: 0.666190833959429 Epoch 212/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1350 - accuracy: 0.9799 - val_loss: 0.2018 - val_accuracy: 0.9614 [ 5.0166462e-34 0.0000000e+00 -2.3446454e-08 ... -1.3320859e-02 1.4658483e-02 -6.6999830e-03] Sparsity at: 0.666190833959429 Epoch 213/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9800 - val_loss: 0.2188 - val_accuracy: 0.9576 [ 5.0166462e-34 0.0000000e+00 -3.4396807e-13 ... -7.5290818e-03 2.0089974e-03 -3.3436914e-03] Sparsity at: 0.666190833959429 Epoch 214/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.2184 - val_accuracy: 0.9555 [ 5.0166462e-34 0.0000000e+00 -8.3651230e-08 ... -7.7453237e-03 -1.8298705e-03 -1.5288311e-02] Sparsity at: 0.666190833959429 Epoch 215/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1396 - accuracy: 0.9777 - val_loss: 0.2546 - val_accuracy: 0.9473 [ 5.0166462e-34 0.0000000e+00 3.2415837e-13 ... -1.5400510e-02 5.4168068e-03 -8.5148066e-03] Sparsity at: 0.666190833959429 Epoch 216/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1392 - accuracy: 0.9786 - val_loss: 0.1908 - val_accuracy: 0.9619 [ 5.0166462e-34 0.0000000e+00 3.9851767e-07 ... -8.4415851e-03 -2.3003173e-05 -5.5112964e-03] Sparsity at: 0.666190833959429 Epoch 217/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9800 - val_loss: 0.1939 - val_accuracy: 0.9639 [ 5.0166462e-34 0.0000000e+00 -3.0048481e-12 ... -2.3809563e-02 -4.9921237e-03 -6.3833883e-03] Sparsity at: 0.666190833959429 Epoch 218/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1387 - accuracy: 0.9778 - val_loss: 0.2303 - val_accuracy: 0.9536 [ 5.0166462e-34 0.0000000e+00 -7.6785036e-06 ... -9.3865786e-03 1.8736207e-03 2.1538506e-03] Sparsity at: 0.666190833959429 Epoch 219/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1383 - accuracy: 0.9790 - val_loss: 0.1969 - val_accuracy: 0.9620 [ 5.0166462e-34 0.0000000e+00 -2.6505522e-11 ... -2.8311048e-02 -5.1541807e-05 -1.5137208e-03] Sparsity at: 0.666190833959429 Epoch 220/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.2000 - val_accuracy: 0.9628 [ 5.0166462e-34 0.0000000e+00 1.4177978e-07 ... -1.3272017e-03 5.7211183e-03 -8.4905652e-03] Sparsity at: 0.666190833959429 Epoch 221/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1393 - accuracy: 0.9786 - val_loss: 0.2153 - val_accuracy: 0.9581 [ 5.0166462e-34 0.0000000e+00 -3.0743816e-09 ... -1.3453597e-02 -5.1749282e-04 -1.3276461e-02] Sparsity at: 0.666190833959429 Epoch 222/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.1855 - val_accuracy: 0.9638 [ 5.0166462e-34 0.0000000e+00 -5.9684510e-14 ... -3.8985512e-03 1.5372393e-03 -6.9678058e-03] Sparsity at: 0.666190833959429 Epoch 223/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9793 - val_loss: 0.1879 - val_accuracy: 0.9646 [ 5.01664622e-34 0.00000000e+00 -1.12355004e-07 ... -4.09543468e-03 3.52310226e-03 1.07571235e-04] Sparsity at: 0.666190833959429 Epoch 224/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2018 - val_accuracy: 0.9625 [ 5.0166462e-34 0.0000000e+00 1.2780354e-13 ... -1.0804565e-03 -3.9763697e-03 1.3977815e-02] Sparsity at: 0.666190833959429 Epoch 225/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2036 - val_accuracy: 0.9611 [ 5.0166462e-34 0.0000000e+00 -8.2392512e-07 ... -1.2893980e-05 -2.7332548e-03 2.2523210e-03] Sparsity at: 0.666190833959429 Epoch 226/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1366 - accuracy: 0.9793 - val_loss: 0.2055 - val_accuracy: 0.9612 [ 5.0166462e-34 0.0000000e+00 8.9900830e-12 ... -1.5774058e-02 -6.8947161e-03 4.6047298e-03] Sparsity at: 0.666190833959429 Epoch 227/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1383 - accuracy: 0.9780 - val_loss: 0.2100 - val_accuracy: 0.9587 [ 5.0166462e-34 0.0000000e+00 -9.5743788e-05 ... -2.3246925e-02 4.2233109e-03 -3.0906203e-03] Sparsity at: 0.666190833959429 Epoch 228/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9803 - val_loss: 0.2068 - val_accuracy: 0.9609 [ 5.0166462e-34 0.0000000e+00 -9.7018643e-11 ... -1.2659587e-02 3.0478816e-03 -1.0195106e-03] Sparsity at: 0.666190833959429 Epoch 229/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9787 - val_loss: 0.2018 - val_accuracy: 0.9621 [ 5.0166462e-34 0.0000000e+00 2.3127650e-10 ... -1.0595206e-02 -3.3647071e-03 1.1063647e-02] Sparsity at: 0.666190833959429 Epoch 230/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1350 - accuracy: 0.9792 - val_loss: 0.2200 - val_accuracy: 0.9557 [ 5.0166462e-34 0.0000000e+00 8.8874952e-09 ... -1.7448664e-02 -1.1313785e-02 -8.7357225e-04] Sparsity at: 0.666190833959429 Epoch 231/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1329 - accuracy: 0.9802 - val_loss: 0.2070 - val_accuracy: 0.9594 [ 5.01664622e-34 0.00000000e+00 1.04616856e-13 ... -6.34946628e-03 6.82687969e-04 -1.38360411e-02] Sparsity at: 0.666190833959429 Epoch 232/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1329 - accuracy: 0.9802 - val_loss: 0.2007 - val_accuracy: 0.9604 [ 5.0166462e-34 0.0000000e+00 -1.6428581e-07 ... -8.2641831e-03 2.8233682e-03 -9.6772099e-03] Sparsity at: 0.666190833959429 Epoch 233/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9798 - val_loss: 0.1911 - val_accuracy: 0.9635 [ 5.0166462e-34 0.0000000e+00 5.3890833e-13 ... -3.1657279e-03 -4.1182539e-03 -5.2120602e-03] Sparsity at: 0.666190833959429 Epoch 234/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.2087 - val_accuracy: 0.9623 [ 5.0166462e-34 0.0000000e+00 -2.6554526e-06 ... -2.8246432e-03 1.4247248e-03 -5.9326608e-03] Sparsity at: 0.666190833959429 Epoch 235/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.2071 - val_accuracy: 0.9576 [ 5.0166462e-34 0.0000000e+00 1.6300149e-11 ... -8.6762160e-03 -3.8412286e-03 -1.4873040e-02] Sparsity at: 0.666190833959429 Epoch 236/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1394 - accuracy: 0.9784 - val_loss: 0.2336 - val_accuracy: 0.9543 [5.0166462e-34 0.0000000e+00 1.0179226e-12 ... 1.7033176e-03 9.5056588e-05 1.4884573e-03] Sparsity at: 0.666190833959429 Epoch 237/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2159 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 2.0724180e-08 ... -7.5409352e-04 -4.3460294e-03 2.4321808e-03] Sparsity at: 0.666190833959429 Epoch 238/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1364 - accuracy: 0.9788 - val_loss: 0.1938 - val_accuracy: 0.9626 [ 5.01664622e-34 0.00000000e+00 1.08640514e-13 ... -4.23583435e-03 -4.96472884e-03 5.31715713e-03] Sparsity at: 0.666190833959429 Epoch 239/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9801 - val_loss: 0.1937 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 1.9580233e-05 ... -1.3631639e-02 -6.0756169e-03 -1.2440525e-03] Sparsity at: 0.666190833959429 Epoch 240/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.1894 - val_accuracy: 0.9652 [ 5.0166462e-34 0.0000000e+00 1.1746920e-10 ... -1.3547566e-02 -9.0799510e-04 -4.4999793e-03] Sparsity at: 0.666190833959429 Epoch 241/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9800 - val_loss: 0.1846 - val_accuracy: 0.9664 [ 5.0166462e-34 0.0000000e+00 -1.5227401e-15 ... -9.5827887e-03 -3.1739252e-03 3.7934701e-03] Sparsity at: 0.666190833959429 Epoch 242/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1365 - accuracy: 0.9788 - val_loss: 0.1951 - val_accuracy: 0.9625 [ 5.0166462e-34 0.0000000e+00 -1.1672378e-07 ... -8.2868366e-03 -7.1084765e-03 -8.3195716e-03] Sparsity at: 0.666190833959429 Epoch 243/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1344 - accuracy: 0.9794 - val_loss: 0.2223 - val_accuracy: 0.9545 [ 5.0166462e-34 0.0000000e+00 -1.0759066e-12 ... -7.5666057e-03 5.6932876e-03 -8.3142025e-03] Sparsity at: 0.666190833959429 Epoch 244/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9791 - val_loss: 0.1992 - val_accuracy: 0.9610 [ 5.0166462e-34 0.0000000e+00 -9.7063530e-06 ... -1.6686749e-02 1.3403445e-02 -7.5690686e-03] Sparsity at: 0.666190833959429 Epoch 245/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9805 - val_loss: 0.2073 - val_accuracy: 0.9612 [ 5.0166462e-34 0.0000000e+00 -1.7113921e-10 ... -2.1975081e-02 1.9176187e-02 -2.6952343e-03] Sparsity at: 0.666190833959429 Epoch 246/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1413 - accuracy: 0.9778 - val_loss: 0.2215 - val_accuracy: 0.9552 [ 5.0166462e-34 0.0000000e+00 -7.8157289e-15 ... -1.2002042e-02 6.7663607e-03 -1.1201041e-02] Sparsity at: 0.666190833959429 Epoch 247/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9801 - val_loss: 0.2019 - val_accuracy: 0.9611 [ 5.0166462e-34 0.0000000e+00 2.0312747e-09 ... -1.4106735e-02 2.4849037e-02 -3.7968036e-04] Sparsity at: 0.666190833959429 Epoch 248/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1408 - accuracy: 0.9776 - val_loss: 0.1986 - val_accuracy: 0.9606 [ 5.0166462e-34 0.0000000e+00 3.8714745e-13 ... -1.4783944e-02 2.0893378e-02 -1.0648832e-02] Sparsity at: 0.666190833959429 Epoch 249/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9806 - val_loss: 0.1940 - val_accuracy: 0.9627 [ 5.0166462e-34 0.0000000e+00 -5.2437619e-05 ... -5.1252232e-03 1.4152872e-02 -7.4495953e-03] Sparsity at: 0.666190833959429 Epoch 250/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.2244 - val_accuracy: 0.9537 [ 5.0166462e-34 0.0000000e+00 -4.2562615e-10 ... -2.0444019e-02 1.6940340e-02 -1.3357890e-02] Sparsity at: 0.666190833959429 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.005424971972829762 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.0050160842121825255 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.84183335 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.06822590968293785 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 289s 16ms/step - loss: 0.1336 - accuracy: 0.9800 - val_loss: 0.1841 - val_accuracy: 0.9667 [ 5.0166462e-34 0.0000000e+00 4.1078277e-15 ... -8.8087656e-03 2.8525928e-02 -8.2089501e-03] Sparsity at: 0.666190833959429 Epoch 252/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1311 - accuracy: 0.9796 - val_loss: 0.2054 - val_accuracy: 0.9571 [ 5.0166462e-34 0.0000000e+00 2.8870778e-07 ... -2.6478749e-03 1.0651431e-02 -9.5302248e-03] Sparsity at: 0.666190833959429 Epoch 253/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9796 - val_loss: 0.2103 - val_accuracy: 0.9578 [ 5.0166462e-34 0.0000000e+00 -6.0715538e-13 ... -3.3162530e-03 8.2712178e-04 -1.0158852e-02] Sparsity at: 0.666190833959429 Epoch 254/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9795 - val_loss: 0.2110 - val_accuracy: 0.9584 [ 5.0166462e-34 0.0000000e+00 -1.0203345e-05 ... -1.5708815e-02 2.0667030e-03 -6.5091378e-03] Sparsity at: 0.666190833959429 Epoch 255/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1350 - accuracy: 0.9790 - val_loss: 0.1953 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 -1.7835675e-10 ... -5.9302510e-03 4.5689554e-03 -6.2460299e-03] Sparsity at: 0.6661945905334336 Epoch 256/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9794 - val_loss: 0.2231 - val_accuracy: 0.9535 [ 5.0166462e-34 0.0000000e+00 2.7929162e-11 ... -1.2968758e-03 -6.9119567e-03 -1.4083045e-02] Sparsity at: 0.6661945905334336 Epoch 257/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9791 - val_loss: 0.2260 - val_accuracy: 0.9535 [ 5.0166462e-34 0.0000000e+00 1.4479717e-08 ... -3.5293407e-03 -2.1664179e-03 -2.5572847e-03] Sparsity at: 0.6661945905334336 Epoch 258/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.2060 - val_accuracy: 0.9572 [ 5.0166462e-34 0.0000000e+00 -1.4109815e-13 ... -1.3492821e-02 -3.9002132e-03 -6.8006635e-04] Sparsity at: 0.6661945905334336 Epoch 259/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9804 - val_loss: 0.2166 - val_accuracy: 0.9543 [ 5.01664622e-34 0.00000000e+00 -2.57906464e-07 ... -8.65873974e-03 -3.20448889e-03 -1.48104215e-02] Sparsity at: 0.6661945905334336 Epoch 260/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.2219 - val_accuracy: 0.9553 [ 5.0166462e-34 0.0000000e+00 4.1291861e-13 ... -3.8195117e-03 -7.9606066e-04 1.2356406e-02] Sparsity at: 0.6661945905334336 Epoch 261/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1355 - accuracy: 0.9789 - val_loss: 0.2206 - val_accuracy: 0.9543 [ 5.0166462e-34 0.0000000e+00 -2.1737378e-07 ... -6.8226671e-03 -1.5340904e-03 -6.8431385e-03] Sparsity at: 0.6661945905334336 Epoch 262/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1312 - accuracy: 0.9804 - val_loss: 0.2316 - val_accuracy: 0.9526 [ 5.0166462e-34 0.0000000e+00 -1.6277379e-11 ... -5.3265733e-03 -5.0257863e-03 -6.2855678e-03] Sparsity at: 0.6661945905334336 Epoch 263/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1395 - accuracy: 0.9781 - val_loss: 0.1996 - val_accuracy: 0.9633 [ 5.0166462e-34 0.0000000e+00 6.1380160e-06 ... -7.3786494e-03 -5.7332884e-03 -1.5937607e-03] Sparsity at: 0.6661945905334336 Epoch 264/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2046 - val_accuracy: 0.9602 [ 5.0166462e-34 0.0000000e+00 1.0025018e-10 ... -1.4628764e-02 -8.5540852e-03 -8.9091081e-03] Sparsity at: 0.6661945905334336 Epoch 265/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9791 - val_loss: 0.2181 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 -4.2933354e-07 ... -1.5277706e-02 -5.7815476e-03 -7.1723065e-03] Sparsity at: 0.6661945905334336 Epoch 266/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.1945 - val_accuracy: 0.9623 [ 5.0166462e-34 0.0000000e+00 -2.1603159e-09 ... -6.6790855e-03 -9.6520623e-03 4.4465205e-03] Sparsity at: 0.6661945905334336 Epoch 267/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1341 - accuracy: 0.9790 - val_loss: 0.2378 - val_accuracy: 0.9478 [ 5.0166462e-34 0.0000000e+00 -2.8488002e-12 ... -3.6254507e-03 -5.0614318e-03 4.6373978e-03] Sparsity at: 0.6661945905334336 Epoch 268/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9787 - val_loss: 0.1973 - val_accuracy: 0.9614 [ 5.0166462e-34 0.0000000e+00 -2.6763981e-08 ... -1.2658463e-02 1.3997826e-03 5.0675757e-03] Sparsity at: 0.6661945905334336 Epoch 269/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9793 - val_loss: 0.2063 - val_accuracy: 0.9617 [ 5.0166462e-34 0.0000000e+00 2.7036560e-13 ... -1.2725029e-02 -8.3470428e-03 6.7114620e-03] Sparsity at: 0.6661945905334336 Epoch 270/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9790 - val_loss: 0.2041 - val_accuracy: 0.9571 [ 5.0166462e-34 0.0000000e+00 8.1289244e-08 ... -1.2470415e-02 -1.1167065e-02 1.4550192e-03] Sparsity at: 0.6661945905334336 Epoch 271/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9793 - val_loss: 0.2202 - val_accuracy: 0.9539 [ 5.0166462e-34 0.0000000e+00 -9.7513989e-13 ... -1.4343457e-02 -5.7524391e-03 5.1763379e-03] Sparsity at: 0.6661945905334336 Epoch 272/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2114 - val_accuracy: 0.9582 [ 5.0166462e-34 0.0000000e+00 -6.7188330e-07 ... -1.0697252e-02 1.8895425e-02 -8.6437650e-03] Sparsity at: 0.6661945905334336 Epoch 273/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1354 - accuracy: 0.9790 - val_loss: 0.1966 - val_accuracy: 0.9638 [ 5.0166462e-34 0.0000000e+00 -4.6940229e-12 ... -2.1669054e-02 1.2107472e-02 -3.6203754e-04] Sparsity at: 0.6661945905334336 Epoch 274/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9792 - val_loss: 0.1871 - val_accuracy: 0.9634 [ 5.0166462e-34 0.0000000e+00 2.8189384e-06 ... -2.0712554e-02 1.3943182e-03 9.4227316e-03] Sparsity at: 0.6661945905334336 Epoch 275/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.1942 - val_accuracy: 0.9635 [ 5.01664622e-34 0.00000000e+00 1.32951115e-11 ... -1.55939404e-02 -2.08610989e-04 1.66784953e-02] Sparsity at: 0.6661945905334336 Epoch 276/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9795 - val_loss: 0.2023 - val_accuracy: 0.9582 [ 5.0166462e-34 0.0000000e+00 4.8257098e-06 ... 8.2011968e-03 -3.3854016e-03 -1.5099021e-04] Sparsity at: 0.6661945905334336 Epoch 277/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9791 - val_loss: 0.2028 - val_accuracy: 0.9603 [ 5.0166462e-34 0.0000000e+00 -4.3871459e-11 ... -8.2591819e-03 4.6813036e-03 -2.4894467e-03] Sparsity at: 0.6661945905334336 Epoch 278/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1309 - accuracy: 0.9805 - val_loss: 0.2121 - val_accuracy: 0.9570 [ 5.0166462e-34 0.0000000e+00 -2.6101266e-05 ... -5.0516636e-03 2.9433434e-04 -6.8966546e-03] Sparsity at: 0.6661945905334336 Epoch 279/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9784 - val_loss: 0.1909 - val_accuracy: 0.9636 [ 5.0166462e-34 0.0000000e+00 1.7483674e-11 ... -2.7394076e-03 5.3594005e-03 -1.7638268e-02] Sparsity at: 0.6661945905334336 Epoch 280/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9797 - val_loss: 0.2015 - val_accuracy: 0.9612: 0.1332 - [ 5.0166462e-34 0.0000000e+00 5.0985022e-05 ... -3.3831631e-03 2.7725506e-03 -1.7784374e-02] Sparsity at: 0.6661945905334336 Epoch 281/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1358 - accuracy: 0.9790 - val_loss: 0.2136 - val_accuracy: 0.9588 [ 5.0166462e-34 0.0000000e+00 2.5149888e-10 ... 3.0595744e-03 3.5595153e-03 -1.2285816e-02] Sparsity at: 0.6661945905334336 Epoch 282/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.1904 - val_accuracy: 0.9633 [ 5.0166462e-34 0.0000000e+00 -8.8632194e-05 ... 2.2162162e-03 3.3864758e-03 -2.2696542e-02] Sparsity at: 0.6661945905334336 Epoch 283/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9787 - val_loss: 0.2203 - val_accuracy: 0.9538 [ 5.0166462e-34 0.0000000e+00 -2.5969096e-10 ... 3.0518125e-03 3.7076464e-03 -4.7855619e-03] Sparsity at: 0.6661945905334336 Epoch 284/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1313 - accuracy: 0.9798 - val_loss: 0.1959 - val_accuracy: 0.9625s - loss: 0.1321 [ 5.016646e-34 0.000000e+00 1.280261e-09 ... -7.025521e-03 2.736197e-03 -6.886986e-03] Sparsity at: 0.6661945905334336 Epoch 285/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1356 - accuracy: 0.9787 - val_loss: 0.2091 - val_accuracy: 0.9595 [ 5.0166462e-34 0.0000000e+00 -5.4844018e-09 ... -3.6379043e-03 1.3934745e-02 -1.5794719e-02] Sparsity at: 0.6661945905334336 Epoch 286/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1311 - accuracy: 0.9797 - val_loss: 0.2239 - val_accuracy: 0.9526 [ 5.01664622e-34 0.00000000e+00 6.99845862e-14 ... -1.54469395e-02 6.91636419e-03 -9.79533698e-03] Sparsity at: 0.6661945905334336 Epoch 287/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9799 - val_loss: 0.1943 - val_accuracy: 0.9618 [ 5.0166462e-34 0.0000000e+00 8.4234443e-08 ... -1.9879712e-02 1.1739079e-02 -1.1992269e-02] Sparsity at: 0.6661945905334336 Epoch 288/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9791 - val_loss: 0.1884 - val_accuracy: 0.9626 [ 5.0166462e-34 0.0000000e+00 1.1066945e-12 ... -5.8060437e-03 7.9021035e-03 -1.0213419e-02] Sparsity at: 0.6661945905334336 Epoch 289/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9783 - val_loss: 0.2043 - val_accuracy: 0.9592 [ 5.0166462e-34 0.0000000e+00 -3.3072440e-06 ... -1.1217592e-02 5.5181463e-03 -1.1749640e-02] Sparsity at: 0.6661945905334336 Epoch 290/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.2013 - val_accuracy: 0.9617 [ 5.0166462e-34 0.0000000e+00 1.8652187e-11 ... -1.5522042e-02 1.5066974e-02 -1.4702387e-02] Sparsity at: 0.6661945905334336 Epoch 291/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1329 - accuracy: 0.9792 - val_loss: 0.2118 - val_accuracy: 0.9577 [ 5.0166462e-34 0.0000000e+00 -3.2374588e-05 ... -1.1716704e-02 1.4093393e-02 -1.0436050e-02] Sparsity at: 0.6661945905334336 Epoch 292/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9793 - val_loss: 0.2150 - val_accuracy: 0.9570 [ 5.0166462e-34 0.0000000e+00 -1.9133001e-11 ... -1.5228307e-02 7.2818608e-03 1.3726990e-03] Sparsity at: 0.6661945905334336 Epoch 293/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9790 - val_loss: 0.2225 - val_accuracy: 0.9516 [ 5.01664622e-34 0.00000000e+00 1.21524696e-04 ... -1.05045643e-02 1.19396467e-02 -3.99276614e-03] Sparsity at: 0.6661945905334336 Epoch 294/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9799 - val_loss: 0.2292 - val_accuracy: 0.9514 [ 5.0166462e-34 0.0000000e+00 1.0207988e-09 ... -1.2707563e-02 1.5120127e-02 -4.1666930e-03] Sparsity at: 0.6661945905334336 Epoch 295/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1341 - accuracy: 0.9789 - val_loss: 0.2195 - val_accuracy: 0.9576 [ 5.0166462e-34 0.0000000e+00 1.6728348e-09 ... -8.4953010e-03 1.1073551e-02 5.9924703e-03] Sparsity at: 0.6661945905334336 Epoch 296/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9789 - val_loss: 0.2403 - val_accuracy: 0.9507 [ 5.016646e-34 0.000000e+00 3.779241e-09 ... -9.129332e-03 6.536076e-03 5.635218e-03] Sparsity at: 0.6661945905334336 Epoch 297/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9796 - val_loss: 0.2002 - val_accuracy: 0.9583 [ 5.01664622e-34 0.00000000e+00 -3.94146684e-13 ... -1.18225627e-02 1.12283705e-02 -5.90698421e-03] Sparsity at: 0.6661945905334336 Epoch 298/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9792 - val_loss: 0.2207 - val_accuracy: 0.9555 [ 5.0166462e-34 0.0000000e+00 6.2766681e-08 ... -1.5015728e-02 1.5950866e-02 -1.0268219e-02] Sparsity at: 0.6661945905334336 Epoch 299/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9791 - val_loss: 0.2237 - val_accuracy: 0.9549 [ 5.0166462e-34 0.0000000e+00 -4.4277928e-13 ... -2.1599604e-02 1.1957290e-02 -1.2891023e-02] Sparsity at: 0.6661945905334336 Epoch 300/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1332 - accuracy: 0.9793 - val_loss: 0.1796 - val_accuracy: 0.9648 [ 5.0166462e-34 0.0000000e+00 7.6373283e-08 ... -2.4667840e-02 2.3287600e-02 -1.1523353e-02] Sparsity at: 0.6661945905334336 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.011396164229506844 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.01809298950022642 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.84183335 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.0786105740883043 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 285s 16ms/step - loss: 0.1324 - accuracy: 0.9792 - val_loss: 0.2089 - val_accuracy: 0.9574 [ 5.0166462e-34 0.0000000e+00 -2.3702320e-12 ... -1.8242219e-02 2.2528322e-02 -2.3960277e-02] Sparsity at: 0.6661945905334336 Epoch 302/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1321 - accuracy: 0.9798 - val_loss: 0.2126 - val_accuracy: 0.9558 [ 5.0166462e-34 0.0000000e+00 6.1803826e-07 ... -1.4370861e-02 2.2725252e-02 -2.3305086e-02] Sparsity at: 0.6661945905334336 Epoch 303/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1336 - accuracy: 0.9790 - val_loss: 0.2051 - val_accuracy: 0.9579 [ 5.0166462e-34 0.0000000e+00 -9.1631407e-12 ... -1.2030389e-02 2.0712299e-02 -1.7406259e-02] Sparsity at: 0.6661945905334336 Epoch 304/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1343 - accuracy: 0.9794 - val_loss: 0.2236 - val_accuracy: 0.9562 [ 5.0166462e-34 0.0000000e+00 -9.8235587e-06 ... -6.9225444e-03 2.2390695e-02 -2.0534968e-02] Sparsity at: 0.6661945905334336 Epoch 305/500 235/235 [==============================] - 5s 19ms/step - loss: 0.1322 - accuracy: 0.9797 - val_loss: 0.2087 - val_accuracy: 0.9581 [ 5.0166462e-34 0.0000000e+00 4.8186344e-11 ... -8.1087612e-03 1.4207467e-02 -2.3832196e-02] Sparsity at: 0.6661945905334336 Epoch 306/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1314 - accuracy: 0.9799 - val_loss: 0.1922 - val_accuracy: 0.9632 [ 5.0166462e-34 0.0000000e+00 -1.7762761e-05 ... -1.7818516e-02 1.3395157e-02 -1.7064344e-02] Sparsity at: 0.6661945905334336 Epoch 307/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.1915 - val_accuracy: 0.9647 [ 5.0166462e-34 0.0000000e+00 6.2061280e-11 ... -1.5127133e-02 -1.8796418e-06 -2.1614496e-02] Sparsity at: 0.6661945905334336 Epoch 308/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1333 - accuracy: 0.9786 - val_loss: 0.2179 - val_accuracy: 0.9520 [ 5.0166462e-34 0.0000000e+00 -1.3301493e-05 ... -2.7700020e-02 1.0548029e-02 -1.7826401e-02] Sparsity at: 0.6661945905334336 Epoch 309/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1354 - accuracy: 0.9789 - val_loss: 0.1897 - val_accuracy: 0.9638 [ 5.0166462e-34 0.0000000e+00 8.5978347e-10 ... -1.8092519e-02 9.2711914e-03 -1.6787298e-02] Sparsity at: 0.6661945905334336 Epoch 310/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9796 - val_loss: 0.2108 - val_accuracy: 0.9590 [ 5.0166462e-34 0.0000000e+00 -4.6183862e-11 ... -2.2075607e-02 1.5041044e-03 -2.1821933e-02] Sparsity at: 0.6661945905334336 Epoch 311/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9797 - val_loss: 0.2215 - val_accuracy: 0.9553 [ 5.0166462e-34 0.0000000e+00 -3.9882391e-09 ... -1.5371803e-02 1.5222040e-02 -1.1591384e-02] Sparsity at: 0.6661945905334336 Epoch 312/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9787 - val_loss: 0.2160 - val_accuracy: 0.9573 [ 5.01664622e-34 0.00000000e+00 1.06812196e-13 ... -1.46025969e-02 7.06102187e-03 -1.72168072e-02] Sparsity at: 0.6661945905334336 Epoch 313/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1356 - accuracy: 0.9791 - val_loss: 0.2053 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 -3.1783111e-07 ... -2.0769602e-02 1.8100586e-02 -1.4454367e-02] Sparsity at: 0.6661945905334336 Epoch 314/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9804 - val_loss: 0.2311 - val_accuracy: 0.9509 [ 5.0166462e-34 0.0000000e+00 -1.9262818e-12 ... -1.1952524e-02 1.1275795e-02 -1.2317302e-02] Sparsity at: 0.6661945905334336 Epoch 315/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1304 - accuracy: 0.9803 - val_loss: 0.2079 - val_accuracy: 0.9607 [ 5.0166462e-34 0.0000000e+00 -1.0100735e-05 ... -8.5355360e-03 -2.4386854e-03 -1.3146252e-02] Sparsity at: 0.6661945905334336 Epoch 316/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.2237 - val_accuracy: 0.9552 [ 5.0166462e-34 0.0000000e+00 -5.3484647e-12 ... -3.9367643e-03 8.4361220e-03 -2.6458047e-02] Sparsity at: 0.6661945905334336 Epoch 317/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9797 - val_loss: 0.1890 - val_accuracy: 0.9647 [ 5.0166462e-34 0.0000000e+00 4.5105378e-05 ... -1.1967882e-02 1.4722745e-02 -2.1711055e-02] Sparsity at: 0.6661945905334336 Epoch 318/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9803 - val_loss: 0.2164 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 -1.1884742e-09 ... 5.2370979e-03 1.4491105e-02 -1.4545539e-02] Sparsity at: 0.6661945905334336 Epoch 319/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1310 - accuracy: 0.9805 - val_loss: 0.1903 - val_accuracy: 0.9655 [ 5.0166462e-34 0.0000000e+00 1.1941486e-12 ... -9.0121105e-03 2.4271395e-02 -1.3893281e-02] Sparsity at: 0.6661945905334336 Epoch 320/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9801 - val_loss: 0.2259 - val_accuracy: 0.9549 [ 5.0166462e-34 0.0000000e+00 -1.8264657e-08 ... -1.6260551e-02 8.4577557e-03 -1.8699598e-02] Sparsity at: 0.6661945905334336 Epoch 321/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2077 - val_accuracy: 0.9589 [ 5.0166462e-34 0.0000000e+00 1.3092449e-13 ... -2.1273766e-02 1.6674172e-02 -1.3141829e-02] Sparsity at: 0.6661945905334336 Epoch 322/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9793 - val_loss: 0.2228 - val_accuracy: 0.9563 [ 5.0166462e-34 0.0000000e+00 6.2886647e-07 ... -1.6637189e-02 2.2465816e-02 -8.5178306e-03] Sparsity at: 0.6661945905334336 Epoch 323/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9794 - val_loss: 0.1928 - val_accuracy: 0.9651 [ 5.0166462e-34 0.0000000e+00 -1.3348803e-11 ... -2.3974145e-02 3.2854218e-02 -2.9167530e-04] Sparsity at: 0.6661945905334336 Epoch 324/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1293 - accuracy: 0.9801 - val_loss: 0.2229 - val_accuracy: 0.9548 [ 5.0166462e-34 0.0000000e+00 -1.9049575e-04 ... -1.9254422e-02 2.5088148e-02 -1.4602958e-02] Sparsity at: 0.6661945905334336 Epoch 325/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9789 - val_loss: 0.1919 - val_accuracy: 0.9633 [ 5.0166462e-34 0.0000000e+00 4.7352333e-10 ... -1.4094747e-02 2.1239776e-02 -5.9801517e-03] Sparsity at: 0.6661945905334336 Epoch 326/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9801 - val_loss: 0.2142 - val_accuracy: 0.9561 [ 5.01664622e-34 0.00000000e+00 -5.37947551e-12 ... -1.17453495e-02 3.03752674e-03 -1.45679880e-02] Sparsity at: 0.6661945905334336 Epoch 327/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9791 - val_loss: 0.2294 - val_accuracy: 0.9546 [ 5.0166462e-34 0.0000000e+00 1.8562138e-08 ... -1.2465824e-02 1.2475618e-02 -7.5133797e-03] Sparsity at: 0.6661945905334336 Epoch 328/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1303 - accuracy: 0.9801 - val_loss: 0.2050 - val_accuracy: 0.9592 [ 5.0166462e-34 0.0000000e+00 -9.1033768e-14 ... -1.4011636e-02 1.2803468e-02 -6.8804398e-03] Sparsity at: 0.6661945905334336 Epoch 329/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1316 - accuracy: 0.9796 - val_loss: 0.2063 - val_accuracy: 0.9621 [ 5.0166462e-34 0.0000000e+00 -5.7358716e-07 ... -1.0516850e-02 3.2680419e-03 -6.1329873e-03] Sparsity at: 0.6661945905334336 Epoch 330/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1329 - accuracy: 0.9791 - val_loss: 0.2045 - val_accuracy: 0.9583 [ 5.0166462e-34 0.0000000e+00 3.4191283e-12 ... -2.0310344e-02 -4.4605369e-03 -5.5692075e-03] Sparsity at: 0.6661945905334336 Epoch 331/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9795 - val_loss: 0.2181 - val_accuracy: 0.9556 [ 5.0166462e-34 0.0000000e+00 -1.2942956e-05 ... -1.6684642e-02 -4.1639176e-03 -5.8261766e-03] Sparsity at: 0.6661945905334336 Epoch 332/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1312 - accuracy: 0.9798 - val_loss: 0.2107 - val_accuracy: 0.9583 [ 5.0166462e-34 0.0000000e+00 1.5556086e-10 ... -1.0945871e-02 -1.0664497e-03 -4.7471542e-03] Sparsity at: 0.6661945905334336 Epoch 333/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9789 - val_loss: 0.2073 - val_accuracy: 0.9590 [ 5.016646e-34 0.000000e+00 2.748752e-15 ... -2.041337e-02 -4.115544e-03 -8.328827e-03] Sparsity at: 0.6661945905334336 Epoch 334/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9803 - val_loss: 0.2079 - val_accuracy: 0.9572 [ 5.0166462e-34 0.0000000e+00 6.0321099e-09 ... -1.4147059e-02 1.0227805e-02 -1.7437987e-02] Sparsity at: 0.6661945905334336 Epoch 335/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.1818 - val_accuracy: 0.9663 [ 5.0166462e-34 0.0000000e+00 4.8706904e-13 ... -6.2247445e-03 9.5995078e-03 -2.0691784e-02] Sparsity at: 0.6661945905334336 Epoch 336/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1308 - accuracy: 0.9797 - val_loss: 0.1946 - val_accuracy: 0.9620 [ 5.0166462e-34 0.0000000e+00 2.0230744e-05 ... -4.7127861e-03 8.1583625e-03 9.2168443e-04] Sparsity at: 0.6661945905334336 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9797 - val_loss: 0.2124 - val_accuracy: 0.9556 [ 5.0166462e-34 0.0000000e+00 -1.2016507e-10 ... -9.3236389e-03 -1.8080815e-03 -4.0031327e-03] Sparsity at: 0.6661945905334336 Epoch 338/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9792 - val_loss: 0.2202 - val_accuracy: 0.9547 [ 5.0166462e-34 0.0000000e+00 8.8193949e-12 ... -1.1737032e-02 3.8471487e-03 1.0326997e-02] Sparsity at: 0.6661945905334336 Epoch 339/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9800 - val_loss: 0.2434 - val_accuracy: 0.9468 [ 5.01664622e-34 0.00000000e+00 1.63267266e-09 ... -1.06334975e-02 5.82451420e-03 -1.63395831e-03] Sparsity at: 0.6661945905334336 Epoch 340/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9791 - val_loss: 0.2107 - val_accuracy: 0.9562 [ 5.0166462e-34 0.0000000e+00 4.0483086e-14 ... -1.1281181e-02 1.0191390e-03 -6.1182515e-03] Sparsity at: 0.6661945905334336 Epoch 341/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9798 - val_loss: 0.2077 - val_accuracy: 0.9566 [ 5.0166462e-34 0.0000000e+00 6.2784312e-07 ... -1.2749154e-02 1.9580455e-02 -9.2525557e-03] Sparsity at: 0.6661945905334336 Epoch 342/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.2046 - val_accuracy: 0.9598 [ 5.0166462e-34 0.0000000e+00 -2.9631467e-12 ... -1.9894438e-02 1.7226735e-02 -3.8211532e-03] Sparsity at: 0.6661945905334336 Epoch 343/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9790 - val_loss: 0.2066 - val_accuracy: 0.9595 [ 5.0166462e-34 0.0000000e+00 2.1980832e-06 ... -2.3388274e-02 1.4149473e-02 -1.8189080e-02] Sparsity at: 0.6661945905334336 Epoch 344/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9801 - val_loss: 0.2154 - val_accuracy: 0.9568 [ 5.0166462e-34 0.0000000e+00 8.5287624e-11 ... -1.5402656e-02 5.0745727e-03 -1.6026553e-02] Sparsity at: 0.6661945905334336 Epoch 345/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1289 - accuracy: 0.9803 - val_loss: 0.2084 - val_accuracy: 0.9583 [ 5.0166462e-34 0.0000000e+00 -3.5860998e-05 ... -1.4443289e-02 9.3781678e-03 8.0649846e-04] Sparsity at: 0.6661945905334336 Epoch 346/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1343 - accuracy: 0.9788 - val_loss: 0.2032 - val_accuracy: 0.9595 [ 5.0166462e-34 0.0000000e+00 -1.0291356e-09 ... -2.2654267e-02 9.6237659e-03 -3.1983352e-03] Sparsity at: 0.6661945905334336 Epoch 347/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9798 - val_loss: 0.2355 - val_accuracy: 0.9526 [ 5.0166462e-34 0.0000000e+00 9.3713183e-11 ... -2.3953097e-02 3.4885462e-03 1.0725718e-03] Sparsity at: 0.6661945905334336 Epoch 348/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9794 - val_loss: 0.2520 - val_accuracy: 0.9485 [ 5.0166462e-34 0.0000000e+00 8.6070635e-09 ... -5.0405944e-03 -8.2615390e-03 -2.4087301e-02] Sparsity at: 0.6661945905334336 Epoch 349/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.2037 - val_accuracy: 0.9601 [ 5.0166462e-34 0.0000000e+00 1.4061285e-13 ... 2.3777741e-03 -5.0382805e-03 -2.5125226e-02] Sparsity at: 0.6661945905334336 Epoch 350/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1287 - accuracy: 0.9803 - val_loss: 0.2136 - val_accuracy: 0.9564 [ 5.0166462e-34 0.0000000e+00 4.4193747e-08 ... 2.8114193e-03 -6.3742017e-03 -3.5064004e-02] Sparsity at: 0.6661945905334336 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.017434749236987512 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.029788859901648035 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.84183335 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.08050235161440078 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 335s 16ms/step - loss: 0.1345 - accuracy: 0.9793 - val_loss: 0.2046 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 5.8957743e-13 ... 1.2488515e-02 -1.5884835e-02 -5.1940709e-02] Sparsity at: 0.6661945905334336 Epoch 352/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1326 - accuracy: 0.9792 - val_loss: 0.2187 - val_accuracy: 0.9576 [ 5.0166462e-34 0.0000000e+00 -4.2192119e-07 ... 1.1118654e-02 -1.2221551e-02 -4.7764443e-02] Sparsity at: 0.6661945905334336 Epoch 353/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1295 - accuracy: 0.9804 - val_loss: 0.2183 - val_accuracy: 0.9581 [ 5.0166462e-34 0.0000000e+00 -4.4247110e-12 ... 1.6902167e-02 4.4779978e-03 -4.6188347e-02] Sparsity at: 0.6661945905334336 Epoch 354/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1315 - accuracy: 0.9798 - val_loss: 0.2129 - val_accuracy: 0.9581 [ 5.0166462e-34 0.0000000e+00 -5.6141289e-07 ... 2.8519338e-02 -9.2720296e-03 -4.7764119e-02] Sparsity at: 0.6661945905334336 Epoch 355/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1303 - accuracy: 0.9804 - val_loss: 0.2162 - val_accuracy: 0.9555uracy: [ 5.0166462e-34 0.0000000e+00 -1.7933197e-11 ... 2.0556360e-02 9.2330696e-03 -4.1158084e-02] Sparsity at: 0.6661945905334336 Epoch 356/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1300 - accuracy: 0.9802 - val_loss: 0.1953 - val_accuracy: 0.9625 [ 5.0166462e-34 0.0000000e+00 -1.7770592e-05 ... 3.0165171e-02 -6.2958631e-03 -5.7087801e-02] Sparsity at: 0.6661945905334336 Epoch 357/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1292 - accuracy: 0.9797 - val_loss: 0.2357 - val_accuracy: 0.9491 [ 5.0166462e-34 0.0000000e+00 2.1001957e-10 ... 2.8506894e-02 -1.7188465e-03 -3.7222117e-02] Sparsity at: 0.6661945905334336 Epoch 358/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1274 - accuracy: 0.9803 - val_loss: 0.2251 - val_accuracy: 0.9526 [ 5.0166462e-34 0.0000000e+00 -1.4999537e-05 ... 3.8802572e-02 1.3308596e-02 -3.1848133e-02] Sparsity at: 0.6661945905334336 Epoch 359/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1336 - accuracy: 0.9795 - val_loss: 0.2048 - val_accuracy: 0.9604 [ 5.0166462e-34 0.0000000e+00 -1.2536223e-09 ... 2.7177701e-02 6.6147419e-03 -2.9729892e-02] Sparsity at: 0.6661945905334336 Epoch 360/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1308 - accuracy: 0.9794 - val_loss: 0.2029 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 -2.0987207e-11 ... 3.2178987e-02 1.5719826e-03 -4.3029077e-02] Sparsity at: 0.6661945905334336 Epoch 361/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1307 - accuracy: 0.9798 - val_loss: 0.1983 - val_accuracy: 0.9634 [ 5.0166462e-34 0.0000000e+00 -1.4190608e-08 ... 3.6039576e-02 -6.4102947e-03 -4.1300118e-02] Sparsity at: 0.6661945905334336 Epoch 362/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9789 - val_loss: 0.2088 - val_accuracy: 0.9583 [ 5.0166462e-34 0.0000000e+00 3.1177207e-13 ... 2.9584855e-02 -3.0890966e-04 -3.7495367e-02] Sparsity at: 0.6661945905334336 Epoch 363/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1301 - accuracy: 0.9797 - val_loss: 0.2169 - val_accuracy: 0.9557 [ 5.0166462e-34 0.0000000e+00 -2.9521702e-08 ... 3.5900716e-02 -8.2435217e-03 -3.2542981e-02] Sparsity at: 0.6661945905334336 Epoch 364/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1293 - accuracy: 0.9800 - val_loss: 0.2134 - val_accuracy: 0.9582 [ 5.0166462e-34 0.0000000e+00 8.2353986e-13 ... 3.2868866e-02 -1.6639662e-03 -3.7886836e-02] Sparsity at: 0.6661945905334336 Epoch 365/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.2110 - val_accuracy: 0.9573 [ 5.0166462e-34 0.0000000e+00 3.2103361e-07 ... 2.3626272e-02 -2.0054677e-03 -3.0642426e-02] Sparsity at: 0.6661945905334336 Epoch 366/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1311 - accuracy: 0.9799 - val_loss: 0.2080 - val_accuracy: 0.9588 [ 5.0166462e-34 0.0000000e+00 2.5757100e-12 ... 3.0733455e-02 -2.4378446e-03 -3.7532512e-02] Sparsity at: 0.6661945905334336 Epoch 367/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1331 - accuracy: 0.9791 - val_loss: 0.1872 - val_accuracy: 0.9646 [ 5.0166462e-34 0.0000000e+00 1.5192638e-06 ... 1.9180356e-02 -9.5219770e-04 -3.6745586e-02] Sparsity at: 0.6661945905334336 Epoch 368/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1321 - accuracy: 0.9795 - val_loss: 0.2196 - val_accuracy: 0.9538 [ 5.0166462e-34 0.0000000e+00 -7.1119768e-12 ... 2.4660589e-02 -9.5603373e-03 -2.3976712e-02] Sparsity at: 0.6661945905334336 Epoch 369/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1328 - accuracy: 0.9792 - val_loss: 0.2332 - val_accuracy: 0.9519 [ 5.0166462e-34 0.0000000e+00 9.3108074e-06 ... 2.5102228e-02 -2.3864459e-03 -3.9766040e-02] Sparsity at: 0.6661945905334336 Epoch 370/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1325 - accuracy: 0.9797 - val_loss: 0.1936 - val_accuracy: 0.9640 [ 5.0166462e-34 0.0000000e+00 -1.5575041e-13 ... 2.1633491e-02 -5.6903646e-03 -3.9527357e-02] Sparsity at: 0.6661945905334336 Epoch 371/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1308 - accuracy: 0.9793 - val_loss: 0.2386 - val_accuracy: 0.9504 [ 5.0166462e-34 0.0000000e+00 6.4366206e-05 ... 3.4552068e-02 -4.5697121e-03 -4.3708708e-02] Sparsity at: 0.6661945905334336 Epoch 372/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1332 - accuracy: 0.9796 - val_loss: 0.1865 - val_accuracy: 0.9659 [ 5.0166462e-34 0.0000000e+00 3.9390574e-12 ... 3.2431535e-02 -9.9957585e-03 -4.5200132e-02] Sparsity at: 0.6661945905334336 Epoch 373/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1335 - accuracy: 0.9789 - val_loss: 0.2095 - val_accuracy: 0.9589 [ 5.0166462e-34 0.0000000e+00 6.9456894e-07 ... 2.8762830e-02 -6.9462038e-03 -4.9897589e-02] Sparsity at: 0.6661945905334336 Epoch 374/500 235/235 [==============================] - 4s 19ms/step - loss: 0.1292 - accuracy: 0.9803 - val_loss: 0.2353 - val_accuracy: 0.9518 [ 5.0166462e-34 0.0000000e+00 1.6167983e-09 ... 2.7011273e-02 -1.9643442e-03 -4.2815819e-02] Sparsity at: 0.6661945905334336 Epoch 375/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1295 - accuracy: 0.9801 - val_loss: 0.1946 - val_accuracy: 0.9630 [ 5.016646e-34 0.000000e+00 -4.129045e-12 ... 3.013569e-02 -6.010564e-03 -3.324266e-02] Sparsity at: 0.6661945905334336 Epoch 376/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1323 - accuracy: 0.9793 - val_loss: 0.1907 - val_accuracy: 0.9637 [ 5.0166462e-34 0.0000000e+00 2.7074265e-08 ... 3.5031065e-02 -7.4370415e-03 -5.2227963e-02] Sparsity at: 0.6661945905334336 Epoch 377/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1319 - accuracy: 0.9800 - val_loss: 0.1949 - val_accuracy: 0.9632 [ 5.0166462e-34 0.0000000e+00 -2.6356814e-13 ... 2.9390654e-02 -8.1426336e-04 -4.7859687e-02] Sparsity at: 0.6661945905334336 Epoch 378/500 235/235 [==============================] - 5s 20ms/step - loss: 0.1319 - accuracy: 0.9797 - val_loss: 0.2322 - val_accuracy: 0.9522 [ 5.0166462e-34 0.0000000e+00 -2.4443636e-07 ... 3.1448398e-02 8.7784640e-03 -4.9626831e-02] Sparsity at: 0.6661945905334336 Epoch 379/500 235/235 [==============================] - 5s 19ms/step - loss: 0.1307 - accuracy: 0.9801 - val_loss: 0.2583 - val_accuracy: 0.9426 [ 5.0166462e-34 0.0000000e+00 9.6504212e-14 ... 2.9293986e-02 5.7697417e-03 -3.0985681e-02] Sparsity at: 0.6661945905334336 Epoch 380/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1303 - accuracy: 0.9797 - val_loss: 0.2206 - val_accuracy: 0.9553 [ 5.0166462e-34 0.0000000e+00 -1.8575988e-06 ... 2.7057441e-02 3.7859948e-03 -3.7359085e-02] Sparsity at: 0.6661945905334336 Epoch 381/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1362 - accuracy: 0.9783 - val_loss: 0.2112 - val_accuracy: 0.9578 [ 5.0166462e-34 0.0000000e+00 -2.0278206e-12 ... 3.1557865e-02 4.8949770e-03 -3.2245442e-02] Sparsity at: 0.6661945905334336 Epoch 382/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1288 - accuracy: 0.9803 - val_loss: 0.2087 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 -1.8182627e-05 ... 3.7171446e-02 9.7972937e-03 -3.4056179e-02] Sparsity at: 0.6661945905334336 Epoch 383/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9793 - val_loss: 0.2213 - val_accuracy: 0.9546 [ 5.0166462e-34 0.0000000e+00 1.2706924e-10 ... 3.4090966e-02 6.0086604e-03 -3.5175726e-02] Sparsity at: 0.6661945905334336 Epoch 384/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1318 - accuracy: 0.9793 - val_loss: 0.2203 - val_accuracy: 0.9583 [ 5.0166462e-34 0.0000000e+00 1.9386724e-05 ... 2.8913746e-02 1.3827384e-02 -3.2566305e-02] Sparsity at: 0.6661945905334336 Epoch 385/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9801 - val_loss: 0.2151 - val_accuracy: 0.9568 [ 5.0166462e-34 0.0000000e+00 -9.3321373e-10 ... 3.6412727e-02 9.3722353e-03 -4.1933380e-02] Sparsity at: 0.6661945905334336 Epoch 386/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9802 - val_loss: 0.1981 - val_accuracy: 0.9607 [ 5.0166462e-34 0.0000000e+00 -6.7631056e-10 ... 3.7425280e-02 1.4755691e-03 -4.2886496e-02] Sparsity at: 0.6661945905334336 Epoch 387/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9796 - val_loss: 0.2020 - val_accuracy: 0.9589 [ 5.0166462e-34 0.0000000e+00 -1.3555002e-09 ... 4.1914202e-02 3.4431075e-03 -4.8598185e-02] Sparsity at: 0.6661945905334336 Epoch 388/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9800 - val_loss: 0.2125 - val_accuracy: 0.9591 [ 5.0166462e-34 0.0000000e+00 3.1771972e-12 ... 3.5622969e-02 -1.2308236e-02 -4.2843826e-02] Sparsity at: 0.6661945905334336 Epoch 389/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9791 - val_loss: 0.2233 - val_accuracy: 0.9554 [ 5.0166462e-34 0.0000000e+00 -3.4145387e-08 ... 4.2909775e-02 -7.8834957e-03 -4.6983235e-02] Sparsity at: 0.6661945905334336 Epoch 390/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9789 - val_loss: 0.2492 - val_accuracy: 0.9502 [ 5.0166462e-34 0.0000000e+00 2.7271244e-13 ... 3.8265847e-02 -7.6803095e-03 -3.9133497e-02] Sparsity at: 0.6661945905334336 Epoch 391/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1290 - accuracy: 0.9812 - val_loss: 0.2085 - val_accuracy: 0.9590 [ 5.0166462e-34 0.0000000e+00 -4.5084761e-08 ... 3.2480195e-02 -1.5979197e-03 -4.9847659e-02] Sparsity at: 0.6661945905334336 Epoch 392/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9796 - val_loss: 0.2060 - val_accuracy: 0.9608 [ 5.0166462e-34 0.0000000e+00 -2.1740975e-13 ... 3.0273179e-02 -1.3435634e-02 -3.9513748e-02] Sparsity at: 0.6661945905334336 Epoch 393/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1294 - accuracy: 0.9802 - val_loss: 0.2093 - val_accuracy: 0.9579 [ 5.0166462e-34 0.0000000e+00 -1.1434133e-07 ... 3.0503336e-02 -1.5596343e-03 -3.2199610e-02] Sparsity at: 0.6661945905334336 Epoch 394/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1279 - accuracy: 0.9800 - val_loss: 0.2032 - val_accuracy: 0.9602 [ 5.0166462e-34 0.0000000e+00 -3.1662888e-13 ... 2.8297052e-02 4.1530184e-03 -3.3677880e-02] Sparsity at: 0.6661945905334336 Epoch 395/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1313 - accuracy: 0.9793 - val_loss: 0.1967 - val_accuracy: 0.9624 [ 5.0166462e-34 0.0000000e+00 6.6424332e-07 ... 2.9361097e-02 -1.8165674e-03 -3.7136026e-02] Sparsity at: 0.6661945905334336 Epoch 396/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9791 - val_loss: 0.2190 - val_accuracy: 0.9551 [ 5.0166462e-34 0.0000000e+00 6.1607100e-13 ... 3.7772931e-02 1.3082376e-03 -3.5317603e-02] Sparsity at: 0.6661945905334336 Epoch 397/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9794 - val_loss: 0.2298 - val_accuracy: 0.9537 [ 5.0166462e-34 0.0000000e+00 7.3241245e-07 ... 3.0229187e-02 5.1592146e-03 -3.7146766e-02] Sparsity at: 0.6661945905334336 Epoch 398/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.2396 - val_accuracy: 0.9509 [ 5.0166462e-34 0.0000000e+00 4.2851715e-11 ... 2.9686911e-02 1.2720291e-02 -4.0892307e-02] Sparsity at: 0.6661945905334336 Epoch 399/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9796 - val_loss: 0.1918 - val_accuracy: 0.9629 [ 5.0166462e-34 0.0000000e+00 -3.1581243e-05 ... 2.5934486e-02 8.5540358e-03 -3.4263596e-02] Sparsity at: 0.6661945905334336 Epoch 400/500 235/235 [==============================] - 5s 19ms/step - loss: 0.1301 - accuracy: 0.9806 - val_loss: 0.2160 - val_accuracy: 0.9603 [ 5.0166462e-34 0.0000000e+00 -2.2723745e-10 ... 3.8275316e-02 7.3109306e-03 -3.3438545e-02] Sparsity at: 0.6661945905334336 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.021286794171819112 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [1. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 0. ... 1. 1. 0.] [0. 0. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.035991625182131504 Thresholhold -0.004473910667002201 Using suggest threshold. Applying new mask Percentage zeros 0.84183335 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.0847288159094921 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.179 tf.Tensor( [[1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 314s 16ms/step - loss: 0.1366 - accuracy: 0.9788 - val_loss: 0.2161 - val_accuracy: 0.9585 [ 5.0166462e-34 0.0000000e+00 -1.7030098e-04 ... 3.3695348e-02 7.3106256e-03 -2.9767919e-02] Sparsity at: 0.6661945905334336 Epoch 402/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9797 - val_loss: 0.2234 - val_accuracy: 0.9557 [ 5.0166462e-34 0.0000000e+00 5.3073029e-10 ... 2.5435541e-02 8.9312075e-03 -3.8330525e-02] Sparsity at: 0.6661945905334336 Epoch 403/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1303 - accuracy: 0.9797 - val_loss: 0.1993 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 1.2903041e-10 ... 2.4581093e-02 1.2220347e-02 -3.7538722e-02] Sparsity at: 0.6661945905334336 Epoch 404/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9799 - val_loss: 0.2319 - val_accuracy: 0.9542 [ 5.0166462e-34 0.0000000e+00 1.8479582e-09 ... 2.1131694e-02 1.1529819e-02 -3.1216592e-02] Sparsity at: 0.6661945905334336 Epoch 405/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9806 - val_loss: 0.2282 - val_accuracy: 0.9525 [ 5.0166462e-34 0.0000000e+00 -5.9908601e-14 ... 2.7480183e-02 2.0195769e-02 -2.8177053e-02] Sparsity at: 0.6661945905334336 Epoch 406/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9793 - val_loss: 0.2170 - val_accuracy: 0.9577 [ 5.0166462e-34 0.0000000e+00 -2.3530706e-07 ... 2.2083312e-02 1.2195705e-02 -3.0541109e-02] Sparsity at: 0.6661945905334336 Epoch 407/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9796 - val_loss: 0.2121 - val_accuracy: 0.9588 [ 5.0166462e-34 0.0000000e+00 -1.3948997e-12 ... 3.2646816e-02 2.6080599e-03 -3.1332266e-02] Sparsity at: 0.6661945905334336 Epoch 408/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9806 - val_loss: 0.2102 - val_accuracy: 0.9615 [ 5.0166462e-34 0.0000000e+00 -3.7684127e-05 ... 2.4762336e-02 5.1115514e-03 -2.7669128e-02] Sparsity at: 0.6661945905334336 Epoch 409/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1315 - accuracy: 0.9792 - val_loss: 0.2155 - val_accuracy: 0.9559 [ 5.0166462e-34 0.0000000e+00 3.3662428e-10 ... 2.3849143e-02 8.2674921e-03 -2.3807436e-02] Sparsity at: 0.6661945905334336 Epoch 410/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9800 - val_loss: 0.2350 - val_accuracy: 0.9496 [ 5.0166462e-34 0.0000000e+00 8.3640816e-11 ... 2.4263062e-02 -2.2368226e-03 -2.6655937e-02] Sparsity at: 0.6661945905334336 Epoch 411/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1306 - accuracy: 0.9796 - val_loss: 0.2035 - val_accuracy: 0.9596 [ 5.0166462e-34 0.0000000e+00 5.7226401e-09 ... 2.9896941e-02 -1.1668382e-02 -3.1332888e-02] Sparsity at: 0.6661945905334336 Epoch 412/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1315 - accuracy: 0.9796 - val_loss: 0.2301 - val_accuracy: 0.9528 [ 5.0166462e-34 0.0000000e+00 1.1491273e-13 ... 3.1510808e-02 -1.2416903e-02 -3.8731392e-02] Sparsity at: 0.6661945905334336 Epoch 413/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9798 - val_loss: 0.2176 - val_accuracy: 0.9556 [ 5.0166462e-34 0.0000000e+00 1.5143132e-07 ... 3.6174856e-02 -4.7017857e-03 -2.5497245e-02] Sparsity at: 0.6661945905334336 Epoch 414/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1291 - accuracy: 0.9805 - val_loss: 0.2172 - val_accuracy: 0.9545 [ 5.0166462e-34 0.0000000e+00 -7.7384843e-13 ... 4.0978163e-02 4.9593374e-03 -3.3247415e-02] Sparsity at: 0.6661945905334336 Epoch 415/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1286 - accuracy: 0.9803 - val_loss: 0.2290 - val_accuracy: 0.9512 [ 5.0166462e-34 0.0000000e+00 8.9124205e-07 ... 3.4150086e-02 3.6105814e-03 -3.4573276e-02] Sparsity at: 0.6661945905334336 Epoch 416/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9790 - val_loss: 0.1943 - val_accuracy: 0.9632 [ 5.0166462e-34 0.0000000e+00 -3.3500824e-12 ... 2.8278327e-02 9.3308287e-03 -3.7592702e-02] Sparsity at: 0.6661945905334336 Epoch 417/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9797 - val_loss: 0.2230 - val_accuracy: 0.9550 [ 5.0166462e-34 0.0000000e+00 1.4363301e-05 ... 2.3932341e-02 4.2434921e-03 -3.6306161e-02] Sparsity at: 0.6661945905334336 Epoch 418/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1284 - accuracy: 0.9805 - val_loss: 0.2209 - val_accuracy: 0.9545 [ 5.0166462e-34 0.0000000e+00 1.2187328e-11 ... 2.3486931e-02 6.7101177e-03 -3.8759548e-02] Sparsity at: 0.6661945905334336 Epoch 419/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9786 - val_loss: 0.2074 - val_accuracy: 0.9620 [ 5.0166462e-34 0.0000000e+00 5.2370451e-05 ... 1.7969867e-02 1.7991617e-02 -3.6398351e-02] Sparsity at: 0.6661945905334336 Epoch 420/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9801 - val_loss: 0.2119 - val_accuracy: 0.9597 [ 5.0166462e-34 0.0000000e+00 1.0719575e-10 ... 1.5772834e-02 9.8205805e-03 -2.9567460e-02] Sparsity at: 0.6661945905334336 Epoch 421/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9804 - val_loss: 0.2106 - val_accuracy: 0.9581 [ 5.0166462e-34 0.0000000e+00 -8.0907668e-08 ... 2.3440348e-02 1.5317601e-02 -3.3446498e-02] Sparsity at: 0.6661945905334336 Epoch 422/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1272 - accuracy: 0.9808 - val_loss: 0.1988 - val_accuracy: 0.9620 [ 5.0166462e-34 0.0000000e+00 2.0843607e-09 ... 2.6289063e-02 2.6920129e-02 -3.7764721e-02] Sparsity at: 0.6661945905334336 Epoch 423/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9793 - val_loss: 0.1882 - val_accuracy: 0.9662 [ 5.0166462e-34 0.0000000e+00 -1.6385374e-12 ... 3.0213658e-02 2.2953881e-02 -3.5196871e-02] Sparsity at: 0.6661945905334336 Epoch 424/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1305 - accuracy: 0.9801 - val_loss: 0.2050 - val_accuracy: 0.9586 [ 5.0166462e-34 0.0000000e+00 -1.1898731e-08 ... 2.8032901e-02 1.9446323e-02 -3.4592714e-02] Sparsity at: 0.6661945905334336 Epoch 425/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.1998 - val_accuracy: 0.9623 [ 5.01664622e-34 0.00000000e+00 1.00221684e-13 ... 2.14962792e-02 1.65227633e-02 -3.66929844e-02] Sparsity at: 0.6661945905334336 Epoch 426/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1305 - accuracy: 0.9805 - val_loss: 0.1872 - val_accuracy: 0.9642 [ 5.0166462e-34 0.0000000e+00 3.6113369e-07 ... 3.3168435e-02 4.1375863e-03 -3.8761869e-02] Sparsity at: 0.6661945905334336 Epoch 427/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9793 - val_loss: 0.2099 - val_accuracy: 0.9594 [ 5.0166462e-34 0.0000000e+00 -1.6369941e-12 ... 2.4910981e-02 1.5841726e-02 -3.9719313e-02] Sparsity at: 0.6661945905334336 Epoch 428/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1332 - accuracy: 0.9792 - val_loss: 0.1906 - val_accuracy: 0.9638 [ 5.0166462e-34 0.0000000e+00 5.0748622e-07 ... 1.7773718e-02 1.4031833e-02 -4.1157886e-02] Sparsity at: 0.6661945905334336 Epoch 429/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1299 - accuracy: 0.9798 - val_loss: 0.2107 - val_accuracy: 0.9573 [ 5.0166462e-34 0.0000000e+00 -1.7289691e-11 ... 2.3442393e-02 2.3262611e-02 -4.2894352e-02] Sparsity at: 0.6661945905334336 Epoch 430/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1295 - accuracy: 0.9797 - val_loss: 0.2383 - val_accuracy: 0.9493 [ 5.0166462e-34 0.0000000e+00 -1.3089324e-05 ... 2.6219543e-02 1.0280359e-02 -3.9027151e-02] Sparsity at: 0.6661945905334336 Epoch 431/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9792 - val_loss: 0.1891 - val_accuracy: 0.9651 [ 5.0166462e-34 0.0000000e+00 5.7543192e-11 ... 3.0424522e-02 6.9643944e-03 -4.2140618e-02] Sparsity at: 0.6661945905334336 Epoch 432/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1293 - accuracy: 0.9804 - val_loss: 0.1991 - val_accuracy: 0.9616 [ 5.0166462e-34 0.0000000e+00 -1.3232585e-04 ... 3.5531569e-02 3.3531720e-03 -3.3410549e-02] Sparsity at: 0.6661945905334336 Epoch 433/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.2159 - val_accuracy: 0.9560 [ 5.0166462e-34 0.0000000e+00 -5.3958515e-10 ... 3.5795450e-02 7.2374404e-03 -4.1890942e-02] Sparsity at: 0.6661945905334336 Epoch 434/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1275 - accuracy: 0.9808 - val_loss: 0.2245 - val_accuracy: 0.9551 [ 5.0166462e-34 0.0000000e+00 -2.9143706e-07 ... 3.2696735e-02 1.1144673e-02 -4.0690720e-02] Sparsity at: 0.6661945905334336 Epoch 435/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9792 - val_loss: 0.2023 - val_accuracy: 0.9604 [ 5.0166462e-34 0.0000000e+00 -3.3629433e-11 ... 3.2253627e-02 4.1438697e-04 -3.7762336e-02] Sparsity at: 0.6661945905334336 Epoch 436/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9791 - val_loss: 0.1851 - val_accuracy: 0.9642 [ 5.0166462e-34 0.0000000e+00 6.3243008e-11 ... 3.3667326e-02 1.7573556e-03 -3.5908993e-02] Sparsity at: 0.6661945905334336 Epoch 437/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1375 - accuracy: 0.9782 - val_loss: 0.2054 - val_accuracy: 0.9602 [ 5.0166462e-34 0.0000000e+00 -7.9449318e-09 ... 3.5927340e-02 -6.3959445e-04 -3.5168685e-02] Sparsity at: 0.6661945905334336 Epoch 438/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1274 - accuracy: 0.9804 - val_loss: 0.1935 - val_accuracy: 0.9627 [ 5.0166462e-34 0.0000000e+00 -1.5558700e-13 ... 3.3379830e-02 1.4684042e-02 -3.6536660e-02] Sparsity at: 0.6661945905334336 Epoch 439/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9793 - val_loss: 0.2006 - val_accuracy: 0.9614 [ 5.0166462e-34 0.0000000e+00 -1.4287522e-09 ... 3.5465494e-02 2.1416308e-04 -3.0830063e-02] Sparsity at: 0.6661945905334336 Epoch 440/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1302 - accuracy: 0.9794 - val_loss: 0.1858 - val_accuracy: 0.9661 [ 5.0166462e-34 0.0000000e+00 1.2060875e-13 ... 3.5312630e-02 1.5592229e-02 -3.0844605e-02] Sparsity at: 0.6661945905334336 Epoch 441/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1278 - accuracy: 0.9811 - val_loss: 0.1921 - val_accuracy: 0.9629 [ 5.0166462e-34 0.0000000e+00 -2.6459077e-07 ... 3.5115194e-02 1.0440097e-02 -3.1698763e-02] Sparsity at: 0.6661945905334336 Epoch 442/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1312 - accuracy: 0.9800 - val_loss: 0.2173 - val_accuracy: 0.9584 [ 5.0166462e-34 0.0000000e+00 1.8957847e-12 ... 3.2708019e-02 1.1413266e-02 -3.1284310e-02] Sparsity at: 0.6661945905334336 Epoch 443/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9797 - val_loss: 0.2401 - val_accuracy: 0.9490 [ 5.0166462e-34 0.0000000e+00 -7.1751507e-05 ... 2.4949687e-02 1.2319994e-02 -2.3613293e-02] Sparsity at: 0.6661945905334336 Epoch 444/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9788 - val_loss: 0.2352 - val_accuracy: 0.9513 [ 5.0166462e-34 0.0000000e+00 4.8981014e-11 ... 3.4620065e-02 8.7027783e-03 -2.7650103e-02] Sparsity at: 0.6661945905334336 Epoch 445/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.1894 - val_accuracy: 0.9648 [ 5.0166462e-34 0.0000000e+00 -1.8864559e-14 ... 3.2831721e-02 1.4507332e-02 -2.8606158e-02] Sparsity at: 0.6661945905334336 Epoch 446/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9801 - val_loss: 0.2441 - val_accuracy: 0.9504 [ 5.0166462e-34 0.0000000e+00 -5.5716079e-08 ... 3.4155052e-02 1.8863833e-02 -3.0358726e-02] Sparsity at: 0.6661945905334336 Epoch 447/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1303 - accuracy: 0.9794 - val_loss: 0.2155 - val_accuracy: 0.9572 [ 5.0166462e-34 0.0000000e+00 6.3468653e-13 ... 2.7182460e-02 2.1889007e-02 -3.4409065e-02] Sparsity at: 0.6661945905334336 Epoch 448/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1302 - accuracy: 0.9803 - val_loss: 0.2138 - val_accuracy: 0.9568 [ 5.0166462e-34 0.0000000e+00 8.7546359e-06 ... 2.8081672e-02 1.7427117e-02 -3.2683324e-02] Sparsity at: 0.6661945905334336 Epoch 449/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1307 - accuracy: 0.9793 - val_loss: 0.1926 - val_accuracy: 0.9628 [ 5.0166462e-34 0.0000000e+00 -4.2037596e-11 ... 2.3931349e-02 2.1367043e-02 -3.7568647e-02] Sparsity at: 0.6661945905334336 Epoch 450/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1291 - accuracy: 0.9801 - val_loss: 0.2041 - val_accuracy: 0.9610 [ 5.0166462e-34 0.0000000e+00 1.2971173e-06 ... 2.4462642e-02 1.9424165e-02 -2.3107791e-02] Sparsity at: 0.6661945905334336 Epoch 451/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1317 - accuracy: 0.9794 - val_loss: 0.1908 - val_accuracy: 0.9645 [ 5.0166462e-34 0.0000000e+00 -2.1308819e-09 ... 2.5378020e-02 1.5213143e-02 -3.0225599e-02] Sparsity at: 0.6661945905334336 Epoch 452/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1319 - accuracy: 0.9796 - val_loss: 0.2178 - val_accuracy: 0.9536 [ 5.01664622e-34 0.00000000e+00 -4.86081376e-14 ... 2.47786175e-02 1.28195835e-02 -2.88934782e-02] Sparsity at: 0.6661945905334336 Epoch 453/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9790 - val_loss: 0.2295 - val_accuracy: 0.9527 [ 5.0166462e-34 0.0000000e+00 -7.9636571e-08 ... 3.0350950e-02 1.8294608e-02 -2.0713219e-02] Sparsity at: 0.6661945905334336 Epoch 454/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1308 - accuracy: 0.9796 - val_loss: 0.2023 - val_accuracy: 0.9630 [ 5.0166462e-34 0.0000000e+00 7.3057998e-13 ... 3.3765789e-02 5.9622084e-03 -2.7223842e-02] Sparsity at: 0.6661945905334336 Epoch 455/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1321 - accuracy: 0.9803 - val_loss: 0.1946 - val_accuracy: 0.9623 [ 5.0166462e-34 0.0000000e+00 -2.6681580e-06 ... 2.9829403e-02 2.0242143e-02 -2.7752785e-02] Sparsity at: 0.6661945905334336 Epoch 456/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9803 - val_loss: 0.1893 - val_accuracy: 0.9621 [ 5.0166462e-34 0.0000000e+00 4.9105065e-12 ... 3.1063760e-02 1.9509818e-02 -3.3856992e-02] Sparsity at: 0.6661945905334336 Epoch 457/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1285 - accuracy: 0.9799 - val_loss: 0.1859 - val_accuracy: 0.9662 [ 5.0166462e-34 0.0000000e+00 -2.3863926e-05 ... 3.1564310e-02 1.5154132e-02 -3.7474949e-02] Sparsity at: 0.6661945905334336 Epoch 458/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9793 - val_loss: 0.2127 - val_accuracy: 0.9561 [ 5.0166462e-34 0.0000000e+00 -9.2739538e-11 ... 2.7012378e-02 2.2031531e-02 -3.2125648e-02] Sparsity at: 0.6661945905334336 Epoch 459/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9791 - val_loss: 0.2447 - val_accuracy: 0.9485 [ 5.0166462e-34 0.0000000e+00 7.0777824e-05 ... 2.5135579e-02 2.4749571e-02 -3.7654240e-02] Sparsity at: 0.6661945905334336 Epoch 460/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1313 - accuracy: 0.9797 - val_loss: 0.2012 - val_accuracy: 0.9603 [ 5.0166462e-34 0.0000000e+00 1.1374727e-09 ... 2.4070501e-02 2.1299662e-02 -3.5719249e-02] Sparsity at: 0.6661945905334336 Epoch 461/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1286 - accuracy: 0.9805 - val_loss: 0.2112 - val_accuracy: 0.9590 [ 5.0166462e-34 0.0000000e+00 -1.0053436e-09 ... 2.2825822e-02 1.8411534e-02 -3.7973076e-02] Sparsity at: 0.6661945905334336 Epoch 462/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9789 - val_loss: 0.2039 - val_accuracy: 0.9593 [ 5.0166462e-34 0.0000000e+00 7.8198195e-09 ... 2.5847569e-02 1.8466167e-02 -3.6668569e-02] Sparsity at: 0.6661945905334336 Epoch 463/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9791 - val_loss: 0.2105 - val_accuracy: 0.9603 [ 5.0166462e-34 0.0000000e+00 -2.2292281e-12 ... 2.6206233e-02 1.8838052e-02 -2.7534315e-02] Sparsity at: 0.6661945905334336 Epoch 464/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1296 - accuracy: 0.9798 - val_loss: 0.1912 - val_accuracy: 0.9644 [ 5.0166462e-34 0.0000000e+00 1.8013564e-08 ... 2.1012601e-02 2.8967986e-02 -2.8848035e-02] Sparsity at: 0.6661945905334336 Epoch 465/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1308 - accuracy: 0.9797 - val_loss: 0.1980 - val_accuracy: 0.9617 [ 5.0166462e-34 0.0000000e+00 -5.3496454e-13 ... 3.3694621e-02 1.5965614e-02 -3.0848363e-02] Sparsity at: 0.6661945905334336 Epoch 466/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1302 - accuracy: 0.9802 - val_loss: 0.2768 - val_accuracy: 0.9386 [ 5.01664622e-34 0.00000000e+00 -1.13023560e-07 ... 2.48287749e-02 1.33100245e-02 -3.13817635e-02] Sparsity at: 0.6661945905334336 Epoch 467/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1331 - accuracy: 0.9786 - val_loss: 0.1950 - val_accuracy: 0.9614 [ 5.0166462e-34 0.0000000e+00 -4.3734634e-13 ... 2.5379071e-02 1.8889066e-02 -2.5580255e-02] Sparsity at: 0.6661945905334336 Epoch 468/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9792 - val_loss: 0.1930 - val_accuracy: 0.9631 [ 5.0166462e-34 0.0000000e+00 3.0134677e-07 ... 2.9281765e-02 1.5817739e-02 -1.9219046e-02] Sparsity at: 0.6661945905334336 Epoch 469/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1284 - accuracy: 0.9807 - val_loss: 0.2010 - val_accuracy: 0.9603 [ 5.0166462e-34 0.0000000e+00 -2.1538910e-12 ... 3.1510673e-02 1.4559312e-02 -2.7425151e-02] Sparsity at: 0.6661945905334336 Epoch 470/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1282 - accuracy: 0.9811 - val_loss: 0.2058 - val_accuracy: 0.9582 [ 5.0166462e-34 0.0000000e+00 -7.7651214e-07 ... 3.4084164e-02 2.4510140e-02 -2.7484257e-02] Sparsity at: 0.6661945905334336 Epoch 471/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9793 - val_loss: 0.2073 - val_accuracy: 0.9587 [ 5.0166462e-34 0.0000000e+00 4.6306578e-12 ... 3.2265905e-02 1.0921558e-02 -3.0408163e-02] Sparsity at: 0.6661945905334336 Epoch 472/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1328 - accuracy: 0.9793 - val_loss: 0.1934 - val_accuracy: 0.9664 [ 5.0166462e-34 0.0000000e+00 -2.7766364e-06 ... 3.5363849e-02 1.4309766e-02 -3.1650126e-02] Sparsity at: 0.6661945905334336 Epoch 473/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9800 - val_loss: 0.2345 - val_accuracy: 0.9527 [ 5.01664622e-34 0.00000000e+00 1.18122144e-11 ... 3.35260108e-02 1.12362215e-02 -3.58981527e-02] Sparsity at: 0.6661945905334336 Epoch 474/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1301 - accuracy: 0.9796 - val_loss: 0.2037 - val_accuracy: 0.9602 [ 5.0166462e-34 0.0000000e+00 -2.7544767e-05 ... 4.0611811e-02 7.1950620e-03 -2.9724510e-02] Sparsity at: 0.6661945905334336 Epoch 475/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9785 - val_loss: 0.2283 - val_accuracy: 0.9572 [ 5.0166462e-34 0.0000000e+00 -7.6409157e-11 ... 3.3445243e-02 9.2513142e-03 -4.0036768e-02] Sparsity at: 0.6661945905334336 Epoch 476/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1272 - accuracy: 0.9808 - val_loss: 0.2193 - val_accuracy: 0.9549 [ 5.0166462e-34 0.0000000e+00 2.8041622e-08 ... 4.3839898e-02 2.0105566e-03 -3.9353095e-02] Sparsity at: 0.6661945905334336 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9794 - val_loss: 0.2217 - val_accuracy: 0.9562 [ 5.0166462e-34 0.0000000e+00 4.1147978e-09 ... 4.0250488e-02 7.9912217e-03 -4.0568639e-02] Sparsity at: 0.6661945905334336 Epoch 478/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1286 - accuracy: 0.9807 - val_loss: 0.1977 - val_accuracy: 0.9616 [ 5.0166462e-34 0.0000000e+00 1.7125869e-12 ... 3.4014031e-02 1.1212647e-02 -3.6969546e-02] Sparsity at: 0.6661945905334336 Epoch 479/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1298 - accuracy: 0.9799 - val_loss: 0.1956 - val_accuracy: 0.9641 [ 5.0166462e-34 0.0000000e+00 1.2998083e-08 ... 2.7157564e-02 8.5445177e-03 -3.2730255e-02] Sparsity at: 0.6661945905334336 Epoch 480/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9796 - val_loss: 0.1936 - val_accuracy: 0.9661 [ 5.0166462e-34 0.0000000e+00 -2.9026044e-13 ... 3.6168572e-02 1.3901040e-02 -4.1138522e-02] Sparsity at: 0.6661945905334336 Epoch 481/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1293 - accuracy: 0.9801 - val_loss: 0.2027 - val_accuracy: 0.9608 [ 5.0166462e-34 0.0000000e+00 -1.0953354e-07 ... 2.5413845e-02 1.0120479e-02 -4.3176826e-02] Sparsity at: 0.6661945905334336 Epoch 482/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9805 - val_loss: 0.2330 - val_accuracy: 0.9528 [ 5.0166462e-34 0.0000000e+00 -6.3144336e-13 ... 3.4757722e-02 1.5925772e-02 -3.8130615e-02] Sparsity at: 0.6661945905334336 Epoch 483/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1313 - accuracy: 0.9798 - val_loss: 0.2210 - val_accuracy: 0.9569 [ 5.0166462e-34 0.0000000e+00 -4.8368372e-07 ... 3.7440643e-02 1.1787804e-02 -3.8699858e-02] Sparsity at: 0.6661945905334336 Epoch 484/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1295 - accuracy: 0.9803 - val_loss: 0.2069 - val_accuracy: 0.9578 [ 5.01664622e-34 0.00000000e+00 -4.73472607e-12 ... 3.59379053e-02 1.11894915e-02 -3.30985300e-02] Sparsity at: 0.6661945905334336 Epoch 485/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9797 - val_loss: 0.1977 - val_accuracy: 0.9589 [ 5.0166462e-34 0.0000000e+00 3.1105365e-06 ... 2.3438873e-02 9.5222406e-03 -2.9412445e-02] Sparsity at: 0.6661945905334336 Epoch 486/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1292 - accuracy: 0.9799 - val_loss: 0.1886 - val_accuracy: 0.9643 [ 5.0166462e-34 0.0000000e+00 2.8096695e-11 ... 2.7605167e-02 2.8741575e-05 -2.3558874e-02] Sparsity at: 0.6661945905334336 Epoch 487/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1324 - accuracy: 0.9789 - val_loss: 0.2108 - val_accuracy: 0.9598 [ 5.0166462e-34 0.0000000e+00 -1.5028096e-05 ... 2.4362903e-02 8.0653653e-03 -3.0866563e-02] Sparsity at: 0.6661945905334336 Epoch 488/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1309 - accuracy: 0.9800 - val_loss: 0.1972 - val_accuracy: 0.9625 [ 5.0166462e-34 0.0000000e+00 -8.5109100e-11 ... 3.1887610e-02 4.0479116e-03 -2.8064299e-02] Sparsity at: 0.6661945905334336 Epoch 489/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1276 - accuracy: 0.9810 - val_loss: 0.2254 - val_accuracy: 0.9536 [ 5.0166462e-34 0.0000000e+00 4.0138926e-05 ... 3.4619462e-02 3.2950668e-03 -2.7101122e-02] Sparsity at: 0.6661945905334336 Epoch 490/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9793 - val_loss: 0.1910 - val_accuracy: 0.9623 [ 5.0166462e-34 0.0000000e+00 -2.1473656e-10 ... 2.6551168e-02 9.4030909e-03 -2.6809609e-02] Sparsity at: 0.6661945905334336 Epoch 491/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.2136 - val_accuracy: 0.9559 [ 5.0166462e-34 0.0000000e+00 -7.2793264e-05 ... 1.5244971e-02 1.8277496e-02 -3.0468663e-02] Sparsity at: 0.6661945905334336 Epoch 492/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1276 - accuracy: 0.9814 - val_loss: 0.2019 - val_accuracy: 0.9600 [ 5.0166462e-34 0.0000000e+00 -3.8658907e-10 ... 2.2117740e-02 1.8707333e-02 -3.7650708e-02] Sparsity at: 0.6661945905334336 Epoch 493/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1299 - accuracy: 0.9798 - val_loss: 0.2094 - val_accuracy: 0.9587 [ 5.0166462e-34 0.0000000e+00 7.1694376e-07 ... 2.9080246e-02 1.3878999e-02 -3.3167481e-02] Sparsity at: 0.6661945905334336 Epoch 494/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1351 - accuracy: 0.9784 - val_loss: 0.2339 - val_accuracy: 0.9496 [ 5.0166462e-34 0.0000000e+00 -2.1057092e-10 ... 3.3845954e-02 1.5826404e-02 -3.2074660e-02] Sparsity at: 0.6661945905334336 Epoch 495/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1304 - accuracy: 0.9805 - val_loss: 0.2047 - val_accuracy: 0.9584 [ 5.0166462e-34 0.0000000e+00 -3.0252900e-06 ... 3.0809687e-02 9.0651698e-03 -3.5765842e-02] Sparsity at: 0.6661945905334336 Epoch 496/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9790 - val_loss: 0.2109 - val_accuracy: 0.9588 [ 5.0166462e-34 0.0000000e+00 -7.0761619e-10 ... 3.3090234e-02 1.4748472e-02 -3.4498975e-02] Sparsity at: 0.6661945905334336 Epoch 497/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1311 - accuracy: 0.9794 - val_loss: 0.2120 - val_accuracy: 0.9549 [ 5.0166462e-34 0.0000000e+00 1.6825118e-09 ... 2.2164147e-02 1.5568255e-02 -3.2713860e-02] Sparsity at: 0.6661945905334336 Epoch 498/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9797 - val_loss: 0.2291 - val_accuracy: 0.9539 [ 5.0166462e-34 0.0000000e+00 -2.9893639e-09 ... 2.4329815e-02 1.3032331e-02 -3.8005240e-02] Sparsity at: 0.6661945905334336 Epoch 499/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1273 - accuracy: 0.9804 - val_loss: 0.2185 - val_accuracy: 0.9570 [ 5.0166462e-34 0.0000000e+00 9.8324500e-12 ... 2.6388383e-02 8.3673280e-03 -2.9870940e-02] Sparsity at: 0.6661945905334336 Epoch 500/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9791 - val_loss: 0.1973 - val_accuracy: 0.9607 [ 5.0166462e-34 0.0000000e+00 -1.7728471e-08 ... 2.7445143e-02 1.5431383e-02 -3.0578464e-02] Sparsity at: 0.6661945905334336 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.037218498066067696 Thresholhold -0.04982715845108032 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.0609181709587574 Thresholhold 0.0024757087230682373 Using suggest threshold. Applying new mask Percentage zeros 0.020033333 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.11689147353172302 Thresholhold 0.15380525588989258 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 5/235 [..............................] - ETA: 2s - loss: 1.7690 - accuracy: 0.4336 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0120s vs `on_train_batch_begin` time: 11.4425s). Check your callbacks. 235/235 [==============================] - 73s 16ms/step - loss: 0.2540 - accuracy: 0.9258 - val_loss: 0.2206 - val_accuracy: 0.9571 [-0.04982716 0.05973877 0.01948842 ... 0. 0.20478557 0.2136623 ] Sparsity at: 0.0041359879789631855 Epoch 2/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0886 - accuracy: 0.9749 - val_loss: 0.0972 - val_accuracy: 0.9694 [-0.04982716 0.05973877 0.01948842 ... -0. 0.21181315 0.21732321] Sparsity at: 0.0041359879789631855 Epoch 3/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0502 - accuracy: 0.9861 - val_loss: 0.0897 - val_accuracy: 0.9717 [-0.04982716 0.05973877 0.01948842 ... 0. 0.22302365 0.22485492] Sparsity at: 0.0041359879789631855 Epoch 4/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0303 - accuracy: 0.9923 - val_loss: 0.0910 - val_accuracy: 0.9734 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2341781 0.23953493] Sparsity at: 0.0041359879789631855 Epoch 5/500 235/235 [==============================] - 4s 18ms/step - loss: 0.0199 - accuracy: 0.9950 - val_loss: 0.0895 - val_accuracy: 0.9720 [-0.04982716 0.05973877 0.01948842 ... -0. 0.24328542 0.24719958] Sparsity at: 0.0041359879789631855 Epoch 6/500 235/235 [==============================] - 4s 18ms/step - loss: 0.0129 - accuracy: 0.9972 - val_loss: 0.0928 - val_accuracy: 0.9737 [-0.04982716 0.05973877 0.01948842 ... 0. 0.25285584 0.25656718] Sparsity at: 0.0041359879789631855 Epoch 7/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0111 - accuracy: 0.9976 - val_loss: 0.1011 - val_accuracy: 0.9710 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2586816 0.263335 ] Sparsity at: 0.0041359879789631855 Epoch 8/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0133 - accuracy: 0.9963 - val_loss: 0.0988 - val_accuracy: 0.9717 [-0.04982716 0.05973877 0.01948842 ... 0. 0.26232496 0.25895464] Sparsity at: 0.0041359879789631855 Epoch 9/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0106 - accuracy: 0.9969 - val_loss: 0.1065 - val_accuracy: 0.9710 [-0.04982716 0.05973877 0.01948842 ... 0. 0.27342716 0.26351595] Sparsity at: 0.0041359879789631855 Epoch 10/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0127 - accuracy: 0.9963 - val_loss: 0.0846 - val_accuracy: 0.9759 [-0.04982716 0.05973877 0.01948842 ... 0. 0.27977377 0.26159462] Sparsity at: 0.0041359879789631855 Epoch 11/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0101 - accuracy: 0.9973 - val_loss: 0.0875 - val_accuracy: 0.9783 [-0.04982716 0.05973877 0.01948842 ... 0. 0.28169194 0.27414232] Sparsity at: 0.0041359879789631855 Epoch 12/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0073 - accuracy: 0.9979 - val_loss: 0.0934 - val_accuracy: 0.9768 [-0.04982716 0.05973877 0.01948842 ... 0. 0.28030452 0.27433112] Sparsity at: 0.0041359879789631855 Epoch 13/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0074 - accuracy: 0.9978 - val_loss: 0.0872 - val_accuracy: 0.9770 [-0.04982716 0.05973877 0.01948842 ... 0. 0.28139576 0.2773061 ] Sparsity at: 0.0041359879789631855 Epoch 14/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.0901 - val_accuracy: 0.9763 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2809943 0.2672443 ] Sparsity at: 0.0041359879789631855 Epoch 15/500 235/235 [==============================] - 4s 17ms/step - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0997 - val_accuracy: 0.9754 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28502947 0.25800997] Sparsity at: 0.0041359879789631855 Epoch 16/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0070 - accuracy: 0.9978 - val_loss: 0.0830 - val_accuracy: 0.9786 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29293737 0.2527194 ] Sparsity at: 0.0041359879789631855 Epoch 17/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.0898 - val_accuracy: 0.9786 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29453862 0.25558212] Sparsity at: 0.0041359879789631855 Epoch 18/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.0866 - val_accuracy: 0.9783 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29659212 0.26113114] Sparsity at: 0.0041359879789631855 Epoch 19/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0837 - val_accuracy: 0.9803 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2895051 0.26165482] Sparsity at: 0.0041359879789631855 Epoch 20/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.0975 - val_accuracy: 0.9763 [-0.04982716 0.05973877 0.01948842 ... 0. 0.293849 0.26251072] Sparsity at: 0.0041359879789631855 Epoch 21/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0071 - accuracy: 0.9977 - val_loss: 0.1012 - val_accuracy: 0.9769 [-0.04982716 0.05973877 0.01948842 ... -0. 0.2922375 0.26159057] Sparsity at: 0.0041359879789631855 Epoch 22/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.0896 - val_accuracy: 0.9789 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29632998 0.2636737 ] Sparsity at: 0.0041359879789631855 Epoch 23/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0861 - val_accuracy: 0.9812 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29133672 0.26979598] Sparsity at: 0.0041359879789631855 Epoch 24/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.0862 - val_accuracy: 0.9794 [-0.04982716 0.05973877 0.01948842 ... -0. 0.2752739 0.27974817] Sparsity at: 0.0041359879789631855 Epoch 25/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.0826 - val_accuracy: 0.9814 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2806628 0.28145903] Sparsity at: 0.0041359879789631855 Epoch 26/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0801 - val_accuracy: 0.9813 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28829682 0.276715 ] Sparsity at: 0.0041359879789631855 Epoch 27/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1113 - val_accuracy: 0.9772 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29849505 0.26939705] Sparsity at: 0.0041359879789631855 Epoch 28/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.1010 - val_accuracy: 0.9785 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3061583 0.26269224] Sparsity at: 0.0041359879789631855 Epoch 29/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.0944 - val_accuracy: 0.9809 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30132788 0.27088454] Sparsity at: 0.0041359879789631855 Epoch 30/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.0898 - val_accuracy: 0.9798 [-0.04982716 0.05973877 0.01948842 ... -0. 0.2958858 0.2749377 ] Sparsity at: 0.0041359879789631855 Epoch 31/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0799 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29212517 0.27903095] Sparsity at: 0.0041359879789631855 Epoch 32/500 235/235 [==============================] - 4s 15ms/step - loss: 7.3785e-04 - accuracy: 0.9998 - val_loss: 0.0859 - val_accuracy: 0.9814 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29303262 0.28101605] Sparsity at: 0.0041359879789631855 Epoch 33/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 0.1185 - val_accuracy: 0.9748 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28040862 0.264212 ] Sparsity at: 0.0041359879789631855 Epoch 34/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 0.1170 - val_accuracy: 0.9738 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29225263 0.26447278] Sparsity at: 0.0041359879789631855 Epoch 35/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0874 - val_accuracy: 0.9810 [-0.04982716 0.05973877 0.01948842 ... -0. 0.292528 0.27109116] Sparsity at: 0.0041359879789631855 Epoch 36/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.0815 - val_accuracy: 0.9824 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29903105 0.26698968] Sparsity at: 0.0041359879789631855 Epoch 37/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0864 - val_accuracy: 0.9823 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29839352 0.26889652] Sparsity at: 0.0041359879789631855 Epoch 38/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0800 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29959574 0.2672393 ] Sparsity at: 0.0041359879789631855 Epoch 39/500 235/235 [==============================] - 4s 15ms/step - loss: 8.1996e-04 - accuracy: 0.9998 - val_loss: 0.0814 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29619095 0.26595417] Sparsity at: 0.0041359879789631855 Epoch 40/500 235/235 [==============================] - 4s 15ms/step - loss: 6.0527e-04 - accuracy: 0.9998 - val_loss: 0.0753 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3004466 0.26851308] Sparsity at: 0.0041359879789631855 Epoch 41/500 235/235 [==============================] - 4s 15ms/step - loss: 2.3943e-04 - accuracy: 1.0000 - val_loss: 0.0759 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30373895 0.26961058] Sparsity at: 0.0041359879789631855 Epoch 42/500 235/235 [==============================] - 4s 15ms/step - loss: 4.7950e-04 - accuracy: 0.9999 - val_loss: 0.0776 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30561522 0.26942354] Sparsity at: 0.0041359879789631855 Epoch 43/500 235/235 [==============================] - 4s 15ms/step - loss: 3.6180e-04 - accuracy: 0.9999 - val_loss: 0.0771 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30141103 0.27318367] Sparsity at: 0.0041359879789631855 Epoch 44/500 235/235 [==============================] - 4s 15ms/step - loss: 8.7666e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3025155 0.2748859 ] Sparsity at: 0.0041359879789631855 Epoch 45/500 235/235 [==============================] - 4s 16ms/step - loss: 5.2639e-05 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9855 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3030059 0.27575946] Sparsity at: 0.0041359879789631855 Epoch 46/500 235/235 [==============================] - 4s 15ms/step - loss: 4.5326e-05 - accuracy: 1.0000 - val_loss: 0.0763 - val_accuracy: 0.9857 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30317962 0.27659866] Sparsity at: 0.0041359879789631855 Epoch 47/500 235/235 [==============================] - 4s 15ms/step - loss: 4.2967e-05 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30354983 0.27665097] Sparsity at: 0.0041359879789631855 Epoch 48/500 235/235 [==============================] - 4s 15ms/step - loss: 3.8486e-05 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9854 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30382994 0.2779988 ] Sparsity at: 0.0041359879789631855 Epoch 49/500 235/235 [==============================] - 4s 15ms/step - loss: 3.2782e-05 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30410594 0.27929664] Sparsity at: 0.0041359879789631855 Epoch 50/500 235/235 [==============================] - 4s 15ms/step - loss: 2.7584e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3046591 0.28008497] Sparsity at: 0.0041359879789631855 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.07416100226778077 Thresholhold -0.04982715845108032 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.10183334236959318 Thresholhold 0.07005202770233154 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.30993456551384924 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 116s 15ms/step - loss: 4.6706e-05 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.31088376 0.2802743 ] Sparsity at: 0.05532306536438768 Epoch 52/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0134 - accuracy: 0.9959 - val_loss: 0.1981 - val_accuracy: 0.9608 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3168721 0.27842847] Sparsity at: 0.05532306536438768 Epoch 53/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0213 - accuracy: 0.9932 - val_loss: 0.0947 - val_accuracy: 0.9797 [-0.04982716 0.05973877 0.01948842 ... 0. 0.28807792 0.26549312] Sparsity at: 0.05532306536438768 Epoch 54/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.0747 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29878637 0.2553946 ] Sparsity at: 0.05532306536438768 Epoch 55/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0755 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29739475 0.26077503] Sparsity at: 0.05532306536438768 Epoch 56/500 235/235 [==============================] - 4s 15ms/step - loss: 5.4174e-04 - accuracy: 0.9999 - val_loss: 0.0720 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.2994943 0.26392555] Sparsity at: 0.05532306536438768 Epoch 57/500 235/235 [==============================] - 4s 16ms/step - loss: 3.9185e-04 - accuracy: 0.9999 - val_loss: 0.0735 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29759264 0.26670554] Sparsity at: 0.05532306536438768 Epoch 58/500 235/235 [==============================] - 4s 15ms/step - loss: 2.2422e-04 - accuracy: 1.0000 - val_loss: 0.0708 - val_accuracy: 0.9855 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29884848 0.2688403 ] Sparsity at: 0.05532306536438768 Epoch 59/500 235/235 [==============================] - 4s 16ms/step - loss: 1.4396e-04 - accuracy: 1.0000 - val_loss: 0.0711 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29952547 0.27041975] Sparsity at: 0.05532306536438768 Epoch 60/500 235/235 [==============================] - 4s 18ms/step - loss: 1.1313e-04 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9856 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29970995 0.27185225] Sparsity at: 0.05532306536438768 Epoch 61/500 235/235 [==============================] - 4s 16ms/step - loss: 9.1381e-05 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9856 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30020404 0.27265635] Sparsity at: 0.05532306536438768 Epoch 62/500 235/235 [==============================] - 4s 15ms/step - loss: 8.6439e-05 - accuracy: 1.0000 - val_loss: 0.0713 - val_accuracy: 0.9859 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30439514 0.2744069 ] Sparsity at: 0.05532306536438768 Epoch 63/500 235/235 [==============================] - 4s 15ms/step - loss: 2.3999e-04 - accuracy: 0.9999 - val_loss: 0.0714 - val_accuracy: 0.9862 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3036872 0.27507365] Sparsity at: 0.05532306536438768 Epoch 64/500 235/235 [==============================] - 4s 16ms/step - loss: 1.7575e-04 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9855 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30160758 0.27633995] Sparsity at: 0.05532306536438768 Epoch 65/500 235/235 [==============================] - 4s 18ms/step - loss: 1.0677e-04 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 0.9863 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30355382 0.27835494] Sparsity at: 0.05532306536438768 Epoch 66/500 235/235 [==============================] - 4s 18ms/step - loss: 9.6458e-05 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9862 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3034424 0.2777555 ] Sparsity at: 0.05532306536438768 Epoch 67/500 235/235 [==============================] - 4s 17ms/step - loss: 5.6933e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9866 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30382723 0.27944645] Sparsity at: 0.05532306536438768 Epoch 68/500 235/235 [==============================] - 4s 18ms/step - loss: 5.5485e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9861 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3043384 0.28074074] Sparsity at: 0.05532306536438768 Epoch 69/500 235/235 [==============================] - 4s 17ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.1197 - val_accuracy: 0.9760 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30768427 0.27113882] Sparsity at: 0.05532306536438768 Epoch 70/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.0810 - val_accuracy: 0.9816 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30423674 0.2582061 ] Sparsity at: 0.05532306536438768 Epoch 71/500 235/235 [==============================] - 4s 18ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0847 - val_accuracy: 0.9830 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3021722 0.25187626] Sparsity at: 0.05532306536438768 Epoch 72/500 235/235 [==============================] - 4s 16ms/step - loss: 8.0413e-04 - accuracy: 0.9998 - val_loss: 0.0788 - val_accuracy: 0.9829 [-0.04982716 0.05973877 0.01948842 ... -0. 0.294386 0.25542942] Sparsity at: 0.05532306536438768 Epoch 73/500 235/235 [==============================] - 4s 15ms/step - loss: 3.3529e-04 - accuracy: 1.0000 - val_loss: 0.0775 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29657426 0.25942993] Sparsity at: 0.05532306536438768 Epoch 74/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8030e-04 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.2966725 0.2619392 ] Sparsity at: 0.05532306536438768 Epoch 75/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1608e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29728922 0.26399213] Sparsity at: 0.05532306536438768 Epoch 76/500 235/235 [==============================] - 4s 16ms/step - loss: 2.1749e-04 - accuracy: 0.9999 - val_loss: 0.0783 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29840246 0.2631213 ] Sparsity at: 0.05532306536438768 Epoch 77/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0352e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29999495 0.263344 ] Sparsity at: 0.05532306536438768 Epoch 78/500 235/235 [==============================] - 4s 15ms/step - loss: 6.7196e-05 - accuracy: 1.0000 - val_loss: 0.0785 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30081528 0.2646451 ] Sparsity at: 0.05532306536438768 Epoch 79/500 235/235 [==============================] - 4s 16ms/step - loss: 6.1105e-05 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3016633 0.26523677] Sparsity at: 0.05532306536438768 Epoch 80/500 235/235 [==============================] - 4s 16ms/step - loss: 5.2155e-05 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30205697 0.26662493] Sparsity at: 0.05532306536438768 Epoch 81/500 235/235 [==============================] - 4s 16ms/step - loss: 5.2636e-05 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3037289 0.2675096 ] Sparsity at: 0.05532306536438768 Epoch 82/500 235/235 [==============================] - 4s 15ms/step - loss: 4.2220e-05 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3037151 0.26866376] Sparsity at: 0.05532306536438768 Epoch 83/500 235/235 [==============================] - 4s 16ms/step - loss: 3.8581e-05 - accuracy: 1.0000 - val_loss: 0.0781 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30511752 0.26987624] Sparsity at: 0.05532306536438768 Epoch 84/500 235/235 [==============================] - 4s 16ms/step - loss: 3.1091e-05 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3059143 0.27018484] Sparsity at: 0.05532306536438768 Epoch 85/500 235/235 [==============================] - 4s 15ms/step - loss: 3.8367e-05 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30613846 0.27019313] Sparsity at: 0.05532306536438768 Epoch 86/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0148 - accuracy: 0.9953 - val_loss: 0.1003 - val_accuracy: 0.9810 [-0.04982716 0.05973877 0.01948842 ... -0. 0.27227536 0.25412798] Sparsity at: 0.05532306536438768 Epoch 87/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.0844 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28151733 0.25203684] Sparsity at: 0.05532306536438768 Epoch 88/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.0834 - val_accuracy: 0.9814 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28999248 0.24562049] Sparsity at: 0.05532306536438768 Epoch 89/500 235/235 [==============================] - 4s 15ms/step - loss: 5.1121e-04 - accuracy: 0.9999 - val_loss: 0.0746 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28745735 0.24837331] Sparsity at: 0.05532306536438768 Epoch 90/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5512e-04 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28735048 0.25034374] Sparsity at: 0.05532306536438768 Epoch 91/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2494e-04 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28635707 0.2514869 ] Sparsity at: 0.05532306536438768 Epoch 92/500 235/235 [==============================] - 4s 15ms/step - loss: 9.1560e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28741822 0.25149155] Sparsity at: 0.05532306536438768 Epoch 93/500 235/235 [==============================] - 4s 15ms/step - loss: 7.9539e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.28841567 0.25202757] Sparsity at: 0.05532306536438768 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 6.6189e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9854 [-0.04982716 0.05973877 0.01948842 ... 0. 0.28934282 0.25184563] Sparsity at: 0.05532306536438768 Epoch 95/500 235/235 [==============================] - 4s 15ms/step - loss: 5.8065e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29017863 0.25285733] Sparsity at: 0.05532306536438768 Epoch 96/500 235/235 [==============================] - 4s 15ms/step - loss: 3.0534e-04 - accuracy: 0.9999 - val_loss: 0.0772 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29200765 0.25385278] Sparsity at: 0.05532306536438768 Epoch 97/500 235/235 [==============================] - 4s 15ms/step - loss: 8.5834e-05 - accuracy: 1.0000 - val_loss: 0.0761 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2929127 0.25472513] Sparsity at: 0.05532306536438768 Epoch 98/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3179e-05 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29375505 0.2557747 ] Sparsity at: 0.05532306536438768 Epoch 99/500 235/235 [==============================] - 4s 15ms/step - loss: 4.6621e-05 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.294817 0.25398254] Sparsity at: 0.05532306536438768 Epoch 100/500 235/235 [==============================] - 4s 15ms/step - loss: 3.3563e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2952224 0.25539938] Sparsity at: 0.05532306536438768 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.1353847068942109 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.15325765617603082 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.41499605027482644 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 205s 15ms/step - loss: 3.3060e-05 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29604897 0.25505355] Sparsity at: 0.05532306536438768 Epoch 102/500 235/235 [==============================] - 4s 15ms/step - loss: 2.6682e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29714984 0.2561345 ] Sparsity at: 0.05532306536438768 Epoch 103/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2090e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29778838 0.25723168] Sparsity at: 0.05532306536438768 Epoch 104/500 235/235 [==============================] - 4s 15ms/step - loss: 2.2387e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29841363 0.26070467] Sparsity at: 0.05532306536438768 Epoch 105/500 235/235 [==============================] - 4s 15ms/step - loss: 1.6907e-05 - accuracy: 1.0000 - val_loss: 0.0773 - val_accuracy: 0.9854 [-0.04982716 0.05973877 0.01948842 ... 0. 0.2991446 0.26118207] Sparsity at: 0.05532306536438768 Epoch 106/500 235/235 [==============================] - 4s 15ms/step - loss: 1.7917e-05 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9854 [-0.04982716 0.05973877 0.01948842 ... -0. 0.29982653 0.2604146 ] Sparsity at: 0.05532306536438768 Epoch 107/500 235/235 [==============================] - 4s 15ms/step - loss: 9.4394e-04 - accuracy: 0.9998 - val_loss: 0.2249 - val_accuracy: 0.9613 [-0.04982716 0.05973877 0.01948842 ... -0. 0.27953577 0.2598036 ] Sparsity at: 0.05532306536438768 Epoch 108/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0176 - accuracy: 0.9948 - val_loss: 0.1053 - val_accuracy: 0.9797 [-0.04982716 0.05973877 0.01948842 ... -0. 0.2948608 0.24591452] Sparsity at: 0.05532306536438768 Epoch 109/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.0865 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30597484 0.25668395] Sparsity at: 0.05532306536438768 Epoch 110/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0916 - val_accuracy: 0.9822 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30971584 0.25334167] Sparsity at: 0.05532306536438768 Epoch 111/500 235/235 [==============================] - 4s 15ms/step - loss: 2.9436e-04 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.31344947 0.2542721 ] Sparsity at: 0.05532306536438768 Epoch 112/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8015e-04 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.31344506 0.25459245] Sparsity at: 0.05532306536438768 Epoch 113/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2760e-04 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.31242907 0.25747836] Sparsity at: 0.05532306536438768 Epoch 114/500 235/235 [==============================] - 4s 15ms/step - loss: 8.4124e-05 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.312877 0.258229 ] Sparsity at: 0.05532306536438768 Epoch 115/500 235/235 [==============================] - 4s 16ms/step - loss: 6.2730e-05 - accuracy: 1.0000 - val_loss: 0.0799 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.31335565 0.2594165 ] Sparsity at: 0.05532306536438768 Epoch 116/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3580e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... 0. 0.31421694 0.26035887] Sparsity at: 0.05532306536438768 Epoch 117/500 235/235 [==============================] - 4s 15ms/step - loss: 5.1654e-05 - accuracy: 1.0000 - val_loss: 0.0797 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.31515834 0.26139054] Sparsity at: 0.05532306536438768 Epoch 118/500 235/235 [==============================] - 4s 15ms/step - loss: 4.4575e-05 - accuracy: 1.0000 - val_loss: 0.0798 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3148601 0.26247984] Sparsity at: 0.05532306536438768 Epoch 119/500 235/235 [==============================] - 4s 15ms/step - loss: 4.0983e-05 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3154066 0.26289272] Sparsity at: 0.05532306536438768 Epoch 120/500 235/235 [==============================] - 4s 15ms/step - loss: 3.4594e-05 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.31530052 0.26418483] Sparsity at: 0.05532306536438768 Epoch 121/500 235/235 [==============================] - 4s 15ms/step - loss: 3.3877e-05 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3159717 0.26484454] Sparsity at: 0.05532306536438768 Epoch 122/500 235/235 [==============================] - 4s 16ms/step - loss: 4.0860e-05 - accuracy: 1.0000 - val_loss: 0.0817 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.31623426 0.26547432] Sparsity at: 0.05532306536438768 Epoch 123/500 235/235 [==============================] - 4s 16ms/step - loss: 3.6856e-05 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.31848082 0.26667124] Sparsity at: 0.05532306536438768 Epoch 124/500 235/235 [==============================] - 4s 15ms/step - loss: 8.8720e-04 - accuracy: 0.9997 - val_loss: 0.1055 - val_accuracy: 0.9812 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3087373 0.27352598] Sparsity at: 0.05532306536438768 Epoch 125/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0093 - accuracy: 0.9969 - val_loss: 0.1398 - val_accuracy: 0.9768 [-0.04982716 0.05973877 0.01948842 ... 0. 0.31090182 0.26080692] Sparsity at: 0.05532306536438768 Epoch 126/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0056 - accuracy: 0.9980 - val_loss: 0.0997 - val_accuracy: 0.9820 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30002645 0.26113218] Sparsity at: 0.05532306536438768 Epoch 127/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0997 - val_accuracy: 0.9820 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30606556 0.2573662 ] Sparsity at: 0.05532306536438768 Epoch 128/500 235/235 [==============================] - 4s 15ms/step - loss: 4.8395e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... 0. 0.29911128 0.25909916] Sparsity at: 0.05532306536438768 Epoch 129/500 235/235 [==============================] - 4s 16ms/step - loss: 1.4308e-04 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30228415 0.2627522 ] Sparsity at: 0.05532306536438768 Epoch 130/500 235/235 [==============================] - 4s 15ms/step - loss: 9.6640e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3032974 0.26235455] Sparsity at: 0.05532306536438768 Epoch 131/500 235/235 [==============================] - 4s 16ms/step - loss: 7.9228e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.30604923 0.264399 ] Sparsity at: 0.05532306536438768 Epoch 132/500 235/235 [==============================] - 4s 15ms/step - loss: 7.4731e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3071766 0.26380146] Sparsity at: 0.05532306536438768 Epoch 133/500 235/235 [==============================] - 4s 15ms/step - loss: 4.6172e-05 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.30834436 0.26445392] Sparsity at: 0.05532306536438768 Epoch 134/500 235/235 [==============================] - 4s 15ms/step - loss: 4.1116e-04 - accuracy: 0.9999 - val_loss: 0.0939 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3127295 0.2628851 ] Sparsity at: 0.05532306536438768 Epoch 135/500 235/235 [==============================] - 4s 15ms/step - loss: 3.3876e-04 - accuracy: 0.9999 - val_loss: 0.0986 - val_accuracy: 0.9826 [-0.04982716 0.05973877 0.01948842 ... -0. 0.31954125 0.2631654 ] Sparsity at: 0.05532306536438768 Epoch 136/500 235/235 [==============================] - 4s 15ms/step - loss: 6.0937e-04 - accuracy: 0.9999 - val_loss: 0.0995 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3188961 0.2612582 ] Sparsity at: 0.05532306536438768 Epoch 137/500 235/235 [==============================] - 4s 15ms/step - loss: 5.9838e-04 - accuracy: 0.9999 - val_loss: 0.0984 - val_accuracy: 0.9829 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3173903 0.249751 ] Sparsity at: 0.05532306536438768 Epoch 138/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1206 - val_accuracy: 0.9786 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3238519 0.25141045] Sparsity at: 0.05532306536438768 Epoch 139/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.1198 - val_accuracy: 0.9786 [-0.04982716 0.05973877 0.01948842 ... 0. 0.37034503 0.2302945 ] Sparsity at: 0.05532306536438768 Epoch 140/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0964 - val_accuracy: 0.9828 [-0.04982716 0.05973877 0.01948842 ... -0. 0.37223265 0.23050274] Sparsity at: 0.05532306536438768 Epoch 141/500 235/235 [==============================] - 4s 15ms/step - loss: 8.1190e-04 - accuracy: 0.9997 - val_loss: 0.0952 - val_accuracy: 0.9824 [-0.04982716 0.05973877 0.01948842 ... -0. 0.37210748 0.2355605 ] Sparsity at: 0.05532306536438768 Epoch 142/500 235/235 [==============================] - 4s 15ms/step - loss: 1.9571e-04 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38293058 0.23387823] Sparsity at: 0.05532306536438768 Epoch 143/500 235/235 [==============================] - 4s 15ms/step - loss: 1.3818e-04 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38309458 0.23547126] Sparsity at: 0.05532306536438768 Epoch 144/500 235/235 [==============================] - 4s 15ms/step - loss: 6.2643e-05 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38287032 0.23657654] Sparsity at: 0.05532306536438768 Epoch 145/500 235/235 [==============================] - 4s 15ms/step - loss: 9.8550e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38239568 0.2371617 ] Sparsity at: 0.05532306536438768 Epoch 146/500 235/235 [==============================] - 4s 16ms/step - loss: 5.1021e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38436702 0.23820715] Sparsity at: 0.05532306536438768 Epoch 147/500 235/235 [==============================] - 4s 15ms/step - loss: 2.6544e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38565645 0.2392176 ] Sparsity at: 0.05532306536438768 Epoch 148/500 235/235 [==============================] - 4s 15ms/step - loss: 6.6766e-04 - accuracy: 0.9998 - val_loss: 0.0973 - val_accuracy: 0.9826 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3862009 0.24069567] Sparsity at: 0.05532306536438768 Epoch 149/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1213 - val_accuracy: 0.9813 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40024272 0.25369614] Sparsity at: 0.05532306536438768 Epoch 150/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.1078 - val_accuracy: 0.9810 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3984509 0.24945013] Sparsity at: 0.05532306536438768 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.2141127703646868 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.2219345703124782 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.5212721049450231 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 193s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0935 - val_accuracy: 0.9823 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40363902 0.24689984] Sparsity at: 0.05532306536438768 Epoch 152/500 235/235 [==============================] - 4s 16ms/step - loss: 4.0806e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40155357 0.25519785] Sparsity at: 0.05532306536438768 Epoch 153/500 235/235 [==============================] - 4s 15ms/step - loss: 2.9562e-04 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4007723 0.26052234] Sparsity at: 0.05532306536438768 Epoch 154/500 235/235 [==============================] - 3s 15ms/step - loss: 6.1025e-05 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4000809 0.25932118] Sparsity at: 0.05532306536438768 Epoch 155/500 235/235 [==============================] - 3s 15ms/step - loss: 7.0068e-05 - accuracy: 1.0000 - val_loss: 0.0908 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39975688 0.25887883] Sparsity at: 0.05532306536438768 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8888e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4030536 0.25711098] Sparsity at: 0.05532306536438768 Epoch 157/500 235/235 [==============================] - 3s 15ms/step - loss: 3.8447e-05 - accuracy: 1.0000 - val_loss: 0.0913 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40303853 0.25842872] Sparsity at: 0.05532306536438768 Epoch 158/500 235/235 [==============================] - 4s 15ms/step - loss: 3.4097e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40190303 0.2582449 ] Sparsity at: 0.05532306536438768 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1831e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40180627 0.2596528 ] Sparsity at: 0.05532306536438768 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6071e-04 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40058398 0.2606617 ] Sparsity at: 0.05532306536438768 Epoch 161/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1227 - val_accuracy: 0.9794 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4027032 0.2532185 ] Sparsity at: 0.05532306536438768 Epoch 162/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.1106 - val_accuracy: 0.9817 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38838977 0.22324349] Sparsity at: 0.05532306536438768 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1117 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39431074 0.22829254] Sparsity at: 0.05532306536438768 Epoch 164/500 235/235 [==============================] - 3s 15ms/step - loss: 9.7425e-04 - accuracy: 0.9997 - val_loss: 0.1024 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39974904 0.23598598] Sparsity at: 0.05532306536438768 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0981e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4088891 0.23181362] Sparsity at: 0.05532306536438768 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6918e-04 - accuracy: 0.9999 - val_loss: 0.1000 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40933576 0.2326822 ] Sparsity at: 0.05532306536438768 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5070e-04 - accuracy: 0.9999 - val_loss: 0.0999 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40693745 0.23271139] Sparsity at: 0.05532306536438768 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9241e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40815088 0.23293655] Sparsity at: 0.05532306536438768 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7054e-04 - accuracy: 0.9999 - val_loss: 0.1020 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40589342 0.23813158] Sparsity at: 0.05532306536438768 Epoch 170/500 235/235 [==============================] - 3s 15ms/step - loss: 6.5825e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40560204 0.23832662] Sparsity at: 0.05532306536438768 Epoch 171/500 235/235 [==============================] - 3s 15ms/step - loss: 5.2292e-04 - accuracy: 0.9998 - val_loss: 0.1216 - val_accuracy: 0.9808 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40568253 0.23503608] Sparsity at: 0.05532306536438768 Epoch 172/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1083 - val_accuracy: 0.9816 [-0.04982716 0.05973877 0.01948842 ... -0. 0.42796567 0.24256563] Sparsity at: 0.05532306536438768 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1011 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40078318 0.24065906] Sparsity at: 0.05532306536438768 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7311e-04 - accuracy: 0.9998 - val_loss: 0.0978 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3993944 0.24321577] Sparsity at: 0.05532306536438768 Epoch 175/500 235/235 [==============================] - 4s 15ms/step - loss: 3.7274e-04 - accuracy: 0.9999 - val_loss: 0.0946 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38737833 0.24664868] Sparsity at: 0.05532306536438768 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9576e-05 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38738838 0.24869552] Sparsity at: 0.05532306536438768 Epoch 177/500 235/235 [==============================] - 3s 15ms/step - loss: 8.0263e-04 - accuracy: 0.9998 - val_loss: 0.1023 - val_accuracy: 0.9825 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3889975 0.25259748] Sparsity at: 0.05532306536438768 Epoch 178/500 235/235 [==============================] - 3s 15ms/step - loss: 6.3145e-04 - accuracy: 0.9998 - val_loss: 0.1016 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... -0. 0.377169 0.2521522 ] Sparsity at: 0.05532306536438768 Epoch 179/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5752e-04 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40121153 0.25371543] Sparsity at: 0.05532306536438768 Epoch 180/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5374e-04 - accuracy: 0.9999 - val_loss: 0.0974 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40489456 0.252211 ] Sparsity at: 0.05532306536438768 Epoch 181/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2759e-04 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4038098 0.25829002] Sparsity at: 0.05532306536438768 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3021e-04 - accuracy: 0.9999 - val_loss: 0.1013 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40118086 0.2590216 ] Sparsity at: 0.05532306536438768 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1240 - val_accuracy: 0.9806 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39369828 0.26102462] Sparsity at: 0.05532306536438768 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1121 - val_accuracy: 0.9823 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3938333 0.25673178] Sparsity at: 0.05532306536438768 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.1175 - val_accuracy: 0.9822 [-0.04982716 0.05973877 0.01948842 ... -0. 0.395111 0.2591486 ] Sparsity at: 0.05532306536438768 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4228e-04 - accuracy: 0.9998 - val_loss: 0.0982 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40660065 0.26025626] Sparsity at: 0.05532306536438768 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7526e-04 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40353236 0.26038867] Sparsity at: 0.05532306536438768 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4260e-05 - accuracy: 1.0000 - val_loss: 0.0974 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4036507 0.26138842] Sparsity at: 0.05532306536438768 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6241e-05 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40348572 0.26239958] Sparsity at: 0.05532306536438768 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4873e-05 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4027253 0.26282182] Sparsity at: 0.05532306536438768 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6596e-05 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40249202 0.26214966] Sparsity at: 0.05532306536438768 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0795e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39514512 0.26335058] Sparsity at: 0.05532306536438768 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2491e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3944439 0.26202565] Sparsity at: 0.05532306536438768 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1752e-05 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39411375 0.26209912] Sparsity at: 0.05532306536438768 Epoch 195/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6732e-05 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39444962 0.26277745] Sparsity at: 0.05532306536438768 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4727e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9831 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39495662 0.26660532] Sparsity at: 0.05532306536438768 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1241 - val_accuracy: 0.9808 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3924187 0.28874403] Sparsity at: 0.05532306536438768 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.1129 - val_accuracy: 0.9817 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38623077 0.27304542] Sparsity at: 0.05532306536438768 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1182 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38901502 0.25762653] Sparsity at: 0.05532306536438768 Epoch 200/500 235/235 [==============================] - 4s 16ms/step - loss: 4.8139e-04 - accuracy: 0.9998 - val_loss: 0.1056 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38711238 0.27013484] Sparsity at: 0.05532306536438768 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.3013106641342773 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.2964759516939637 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.6115250208721079 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 184s 12ms/step - loss: 2.6372e-04 - accuracy: 0.9999 - val_loss: 0.1035 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39125538 0.2720272 ] Sparsity at: 0.05532306536438768 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2033e-04 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4086597 0.27226263] Sparsity at: 0.05532306536438768 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4562e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.41928494 0.27078995] Sparsity at: 0.05532306536438768 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6892e-04 - accuracy: 0.9999 - val_loss: 0.1085 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40136835 0.27203184] Sparsity at: 0.05532306536438768 Epoch 205/500 235/235 [==============================] - 4s 16ms/step - loss: 8.6147e-05 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3950483 0.27371612] Sparsity at: 0.05532306536438768 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5287e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3970903 0.27386713] Sparsity at: 0.05532306536438768 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3581e-05 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39831993 0.27222818] Sparsity at: 0.05532306536438768 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0380e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.398459 0.2725772 ] Sparsity at: 0.05532306536438768 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9609e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3987309 0.273546 ] Sparsity at: 0.05532306536438768 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4726e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39928967 0.2738743 ] Sparsity at: 0.05532306536438768 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 9.6240e-06 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39993745 0.27403527] Sparsity at: 0.05532306536438768 Epoch 212/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7635e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.40073967 0.27470973] Sparsity at: 0.05532306536438768 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2949e-04 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39315197 0.27361724] Sparsity at: 0.05532306536438768 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1298 - val_accuracy: 0.9812 [-0.04982716 0.05973877 0.01948842 ... -0. 0.37000448 0.27693266] Sparsity at: 0.05532306536438768 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.1220 - val_accuracy: 0.9822 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38308087 0.24401376] Sparsity at: 0.05532306536438768 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1975e-04 - accuracy: 0.9998 - val_loss: 0.1140 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38869387 0.2620652 ] Sparsity at: 0.05532306536438768 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9901e-04 - accuracy: 0.9999 - val_loss: 0.1104 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39033696 0.2618823 ] Sparsity at: 0.05532306536438768 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8180e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3913286 0.26662627] Sparsity at: 0.05532306536438768 Epoch 219/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7902e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3908382 0.26734078] Sparsity at: 0.05532306536438768 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2300e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39184713 0.26754943] Sparsity at: 0.05532306536438768 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0248e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39319795 0.26747864] Sparsity at: 0.05532306536438768 Epoch 222/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6220e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39990935 0.26688474] Sparsity at: 0.05532306536438768 Epoch 223/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0096e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39938045 0.26859707] Sparsity at: 0.05532306536438768 Epoch 224/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7357e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39890456 0.26935485] Sparsity at: 0.05532306536438768 Epoch 225/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6362e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39875814 0.27026972] Sparsity at: 0.05532306536438768 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5584e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39913246 0.27012575] Sparsity at: 0.05532306536438768 Epoch 227/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0367e-05 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39883775 0.27087516] Sparsity at: 0.05532306536438768 Epoch 228/500 235/235 [==============================] - 3s 15ms/step - loss: 1.4848e-05 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3988056 0.2725325 ] Sparsity at: 0.05532306536438768 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1392 - val_accuracy: 0.9797 [-0.04982716 0.05973877 0.01948842 ... 0. 0.40842462 0.23887268] Sparsity at: 0.05532306536438768 Epoch 230/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0048 - accuracy: 0.9984 - val_loss: 0.1409 - val_accuracy: 0.9784 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38386512 0.25505808] Sparsity at: 0.05532306536438768 Epoch 231/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1305 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38836068 0.23818332] Sparsity at: 0.05532306536438768 Epoch 232/500 235/235 [==============================] - 3s 15ms/step - loss: 5.1274e-04 - accuracy: 0.9998 - val_loss: 0.1177 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3843187 0.2410608 ] Sparsity at: 0.05532306536438768 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6390e-04 - accuracy: 0.9999 - val_loss: 0.1106 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.38970822 0.23837051] Sparsity at: 0.05532306536438768 Epoch 234/500 235/235 [==============================] - 4s 16ms/step - loss: 1.2974e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.38986206 0.23669155] Sparsity at: 0.05532306536438768 Epoch 235/500 235/235 [==============================] - 4s 16ms/step - loss: 3.1203e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3903161 0.2366217 ] Sparsity at: 0.05532306536438768 Epoch 236/500 235/235 [==============================] - 4s 15ms/step - loss: 2.5502e-05 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.390672 0.23684685] Sparsity at: 0.05532306536438768 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4492e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39097905 0.23810863] Sparsity at: 0.05532306536438768 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9914e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3902535 0.23874174] Sparsity at: 0.05532306536438768 Epoch 239/500 235/235 [==============================] - 4s 16ms/step - loss: 2.0800e-05 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39045194 0.23896268] Sparsity at: 0.05532306536438768 Epoch 240/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6603e-05 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39058223 0.23966385] Sparsity at: 0.05532306536438768 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2503e-05 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39109987 0.24005397] Sparsity at: 0.05532306536438768 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 9.9101e-06 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.391521 0.24002573] Sparsity at: 0.05532306536438768 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2949e-06 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3917925 0.2402741 ] Sparsity at: 0.05532306536438768 Epoch 244/500 235/235 [==============================] - 3s 13ms/step - loss: 9.7633e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39200884 0.24112676] Sparsity at: 0.05532306536438768 Epoch 245/500 235/235 [==============================] - 3s 15ms/step - loss: 8.6581e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39197296 0.24104488] Sparsity at: 0.05532306536438768 Epoch 246/500 235/235 [==============================] - 3s 15ms/step - loss: 8.3628e-06 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39195555 0.2424246 ] Sparsity at: 0.05532306536438768 Epoch 247/500 235/235 [==============================] - 3s 15ms/step - loss: 7.1205e-06 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39227766 0.24267031] Sparsity at: 0.05532306536438768 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2559e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39278734 0.2428052 ] Sparsity at: 0.05532306536438768 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1828e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... 0. 0.392932 0.24333303] Sparsity at: 0.05532306536438768 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6370e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39355507 0.24397428] Sparsity at: 0.05532306536438768 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.38901842718566115 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.3690081494183204 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.704682643034964 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 184s 12ms/step - loss: 4.6624e-06 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39399922 0.24433331] Sparsity at: 0.05532306536438768 Epoch 252/500 235/235 [==============================] - 4s 15ms/step - loss: 4.5225e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39438352 0.24483493] Sparsity at: 0.05532306536438768 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6172e-06 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39470148 0.24563989] Sparsity at: 0.05532306536438768 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9525e-06 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.39506933 0.24761824] Sparsity at: 0.05532306536438768 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0964e-06 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39580423 0.24818167] Sparsity at: 0.05532306536438768 Epoch 256/500 235/235 [==============================] - 3s 15ms/step - loss: 3.3780e-06 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39745253 0.2483204 ] Sparsity at: 0.05532306536438768 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0328e-06 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.3989004 0.24767922] Sparsity at: 0.05532306536438768 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5808e-06 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.39935505 0.248646 ] Sparsity at: 0.05532306536438768 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1451e-06 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.3994727 0.24877688] Sparsity at: 0.05532306536438768 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1474 - val_accuracy: 0.9775 [-0.04982716 0.05973877 0.01948842 ... 0. 0.43925628 0.23038983] Sparsity at: 0.05532306536438768 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.1386 - val_accuracy: 0.9816 [-0.04982716 0.05973877 0.01948842 ... 0. 0.43241113 0.22370207] Sparsity at: 0.05532306536438768 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1231 - val_accuracy: 0.9831 [-0.04982716 0.05973877 0.01948842 ... 0. 0.44124004 0.22493237] Sparsity at: 0.05532306536438768 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4441e-04 - accuracy: 0.9999 - val_loss: 0.1180 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.44021973 0.22509414] Sparsity at: 0.05532306536438768 Epoch 264/500 235/235 [==============================] - 3s 15ms/step - loss: 6.4876e-05 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4388117 0.22675472] Sparsity at: 0.05532306536438768 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2562e-05 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4385144 0.2272301 ] Sparsity at: 0.05532306536438768 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8112e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.44100788 0.22737163] Sparsity at: 0.05532306536438768 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3427e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44065437 0.22735251] Sparsity at: 0.05532306536438768 Epoch 268/500 235/235 [==============================] - 4s 15ms/step - loss: 2.2499e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4401549 0.22795591] Sparsity at: 0.05532306536438768 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0745e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43582156 0.2311895 ] Sparsity at: 0.05532306536438768 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4265e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44216588 0.22676004] Sparsity at: 0.05532306536438768 Epoch 271/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7078e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.44173884 0.22685778] Sparsity at: 0.05532306536438768 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3408e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.441773 0.22733301] Sparsity at: 0.05532306536438768 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1131e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44132373 0.22780071] Sparsity at: 0.05532306536438768 Epoch 274/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2240e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44188026 0.22799905] Sparsity at: 0.05532306536438768 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5328e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.43608895 0.22751404] Sparsity at: 0.05532306536438768 Epoch 276/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1311 - val_accuracy: 0.9810 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45520017 0.22810377] Sparsity at: 0.05532306536438768 Epoch 277/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.1329 - val_accuracy: 0.9821 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4388881 0.24626863] Sparsity at: 0.05532306536438768 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 9.0893e-04 - accuracy: 0.9998 - val_loss: 0.1237 - val_accuracy: 0.9829 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4383832 0.25822568] Sparsity at: 0.05532306536438768 Epoch 279/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1183e-04 - accuracy: 0.9999 - val_loss: 0.1168 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4312693 0.25402737] Sparsity at: 0.05532306536438768 Epoch 280/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0138e-04 - accuracy: 0.9999 - val_loss: 0.1157 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43283874 0.25281906] Sparsity at: 0.05532306536438768 Epoch 281/500 235/235 [==============================] - 4s 15ms/step - loss: 5.6276e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.433847 0.251294 ] Sparsity at: 0.05532306536438768 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9593e-05 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43413007 0.25291407] Sparsity at: 0.05532306536438768 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2759e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43340915 0.25460523] Sparsity at: 0.05532306536438768 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7539e-04 - accuracy: 0.9999 - val_loss: 0.1310 - val_accuracy: 0.9816 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43619737 0.2634769 ] Sparsity at: 0.05532306536438768 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0379e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43192607 0.25996777] Sparsity at: 0.05532306536438768 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1066e-05 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4352768 0.2639697 ] Sparsity at: 0.05532306536438768 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9368e-04 - accuracy: 0.9999 - val_loss: 0.1156 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43396473 0.2695453 ] Sparsity at: 0.05532306536438768 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8665e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43616894 0.2690207 ] Sparsity at: 0.05532306536438768 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2537e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43618777 0.26778606] Sparsity at: 0.05532306536438768 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0318e-04 - accuracy: 0.9999 - val_loss: 0.1200 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.43182656 0.2724257 ] Sparsity at: 0.05532306536438768 Epoch 291/500 235/235 [==============================] - 3s 15ms/step - loss: 2.8614e-04 - accuracy: 0.9999 - val_loss: 0.1269 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4494583 0.25073293] Sparsity at: 0.05532306536438768 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1412 - val_accuracy: 0.9799 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44460893 0.25590864] Sparsity at: 0.05532306536438768 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1391 - val_accuracy: 0.9798 [-0.04982716 0.05973877 0.01948842 ... -0. 0.42628253 0.24971262] Sparsity at: 0.05532306536438768 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5822e-04 - accuracy: 0.9998 - val_loss: 0.1276 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4492327 0.24540307] Sparsity at: 0.05532306536438768 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2023e-04 - accuracy: 0.9999 - val_loss: 0.1212 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... 0. 0.45012707 0.2438707 ] Sparsity at: 0.05532306536438768 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5284e-05 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4508162 0.24332595] Sparsity at: 0.05532306536438768 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3450e-05 - accuracy: 1.0000 - val_loss: 0.1201 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45131227 0.2430325 ] Sparsity at: 0.05532306536438768 Epoch 298/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3861e-05 - accuracy: 1.0000 - val_loss: 0.1193 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... 0. 0.45095432 0.24257272] Sparsity at: 0.05532306536438768 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7440e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45129728 0.24261521] Sparsity at: 0.05532306536438768 Epoch 300/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6134e-04 - accuracy: 0.9999 - val_loss: 0.1268 - val_accuracy: 0.9819 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45140854 0.24249601] Sparsity at: 0.05532306536438768 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.5008987904229016 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.46366963504290837 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.8045685335823194 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 180s 12ms/step - loss: 2.9841e-04 - accuracy: 0.9999 - val_loss: 0.1252 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... 0. 0.46742654 0.23531465] Sparsity at: 0.05532306536438768 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5936e-04 - accuracy: 0.9999 - val_loss: 0.1265 - val_accuracy: 0.9822 [-0.04982716 0.05973877 0.01948842 ... 0. 0.46432424 0.23241413] Sparsity at: 0.05532306536438768 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5776e-04 - accuracy: 0.9997 - val_loss: 0.1266 - val_accuracy: 0.9826 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4590296 0.22740059] Sparsity at: 0.05532306536438768 Epoch 304/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1302 - val_accuracy: 0.9823 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4367955 0.22090368] Sparsity at: 0.05532306536438768 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6629e-04 - accuracy: 0.9999 - val_loss: 0.1303 - val_accuracy: 0.9829 [-0.04982716 0.05973877 0.01948842 ... 0. 0.44480368 0.20039834] Sparsity at: 0.05532306536438768 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8771e-04 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45374176 0.19618611] Sparsity at: 0.05532306536438768 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6920e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44415548 0.19838066] Sparsity at: 0.05532306536438768 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8158e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4427757 0.19979377] Sparsity at: 0.05532306536438768 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2974e-04 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.44225204 0.19922069] Sparsity at: 0.05532306536438768 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7099e-05 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44080323 0.20220506] Sparsity at: 0.05532306536438768 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3144e-05 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4400816 0.20287384] Sparsity at: 0.05532306536438768 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4191e-04 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44017732 0.23184298] Sparsity at: 0.05532306536438768 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3923e-05 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43265793 0.23424512] Sparsity at: 0.05532306536438768 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2402e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43256992 0.2350495 ] Sparsity at: 0.05532306536438768 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3194e-06 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43308738 0.23507847] Sparsity at: 0.05532306536438768 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7944e-06 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43320674 0.23811181] Sparsity at: 0.05532306536438768 Epoch 317/500 235/235 [==============================] - 3s 15ms/step - loss: 5.6418e-06 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4334326 0.23700659] Sparsity at: 0.05532306536438768 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5521e-06 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43340346 0.23649652] Sparsity at: 0.05532306536438768 Epoch 319/500 235/235 [==============================] - 3s 15ms/step - loss: 4.4834e-06 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43361148 0.23708822] Sparsity at: 0.05532306536438768 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7622e-06 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.43667713 0.23680735] Sparsity at: 0.05532306536438768 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3420e-06 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.43666345 0.23772842] Sparsity at: 0.05532306536438768 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1520 - val_accuracy: 0.9801 [-0.04982716 0.05973877 0.01948842 ... -0. 0.46464515 0.2563095 ] Sparsity at: 0.05532306536438768 Epoch 323/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1294 - val_accuracy: 0.9817 [-0.04982716 0.05973877 0.01948842 ... -0. 0.46302938 0.25890985] Sparsity at: 0.05532306536438768 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2691e-04 - accuracy: 0.9997 - val_loss: 0.1244 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.46912757 0.26121917] Sparsity at: 0.05532306536438768 Epoch 325/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5424e-04 - accuracy: 0.9999 - val_loss: 0.1192 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47171718 0.26136354] Sparsity at: 0.05532306536438768 Epoch 326/500 235/235 [==============================] - 3s 15ms/step - loss: 7.6093e-05 - accuracy: 1.0000 - val_loss: 0.1212 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... 0. 0.47282887 0.26285452] Sparsity at: 0.05532306536438768 Epoch 327/500 235/235 [==============================] - 3s 15ms/step - loss: 3.9379e-05 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47229168 0.2630963 ] Sparsity at: 0.05532306536438768 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4873e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47178632 0.2631623 ] Sparsity at: 0.05532306536438768 Epoch 329/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7219e-05 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47163317 0.26386172] Sparsity at: 0.05532306536438768 Epoch 330/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1338e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47158778 0.26396286] Sparsity at: 0.05532306536438768 Epoch 331/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0947e-05 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47172984 0.2656687 ] Sparsity at: 0.05532306536438768 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3600e-06 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4716729 0.26599285] Sparsity at: 0.05532306536438768 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5895e-06 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47172114 0.26574817] Sparsity at: 0.05532306536438768 Epoch 334/500 235/235 [==============================] - 4s 15ms/step - loss: 7.2983e-06 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47200662 0.26659006] Sparsity at: 0.05532306536438768 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 8.7673e-06 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47216564 0.26749355] Sparsity at: 0.05532306536438768 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4191e-06 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47236577 0.26906198] Sparsity at: 0.05532306536438768 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2357e-06 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47219855 0.26897925] Sparsity at: 0.05532306536438768 Epoch 338/500 235/235 [==============================] - 4s 15ms/step - loss: 9.5693e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.47209817 0.26926926] Sparsity at: 0.05532306536438768 Epoch 339/500 235/235 [==============================] - 3s 12ms/step - loss: 5.9020e-04 - accuracy: 0.9999 - val_loss: 0.1299 - val_accuracy: 0.9825 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4634504 0.2685126 ] Sparsity at: 0.05532306536438768 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6404e-04 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9828 [-0.04982716 0.05973877 0.01948842 ... -0. 0.46114507 0.26692095] Sparsity at: 0.05532306536438768 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1286 - val_accuracy: 0.9819 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4559907 0.2572591 ] Sparsity at: 0.05532306536438768 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.1319 - val_accuracy: 0.9820 [-0.04982716 0.05973877 0.01948842 ... -0. 0.44147778 0.281688 ] Sparsity at: 0.05532306536438768 Epoch 343/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1218 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... 0. 0.45822462 0.27933848] Sparsity at: 0.05532306536438768 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7428e-04 - accuracy: 0.9998 - val_loss: 0.1268 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45594788 0.26766077] Sparsity at: 0.05532306536438768 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1717e-04 - accuracy: 0.9999 - val_loss: 0.1226 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45915067 0.2869062 ] Sparsity at: 0.05532306536438768 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5478e-05 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4590872 0.2841896 ] Sparsity at: 0.05532306536438768 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2858e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4596594 0.279463 ] Sparsity at: 0.05532306536438768 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6759e-05 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45698085 0.28273842] Sparsity at: 0.05532306536438768 Epoch 349/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2350e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4580125 0.2816334 ] Sparsity at: 0.05532306536438768 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0951e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.45822996 0.28127468] Sparsity at: 0.05532306536438768 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.6140129645016401 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.5528144922561111 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.8750882307586423 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 187s 11ms/step - loss: 7.6440e-06 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4581577 0.2817292 ] Sparsity at: 0.05532306536438768 Epoch 352/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7103e-06 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45839173 0.2817931 ] Sparsity at: 0.05532306536438768 Epoch 353/500 235/235 [==============================] - 3s 13ms/step - loss: 7.5068e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9853 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45917845 0.2820227 ] Sparsity at: 0.05532306536438768 Epoch 354/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0055e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9854 [-0.04982716 0.05973877 0.01948842 ... 0. 0.45933667 0.28271312] Sparsity at: 0.05532306536438768 Epoch 355/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3440e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9855 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4591993 0.2824572 ] Sparsity at: 0.05532306536438768 Epoch 356/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5487e-06 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9854 [-0.04982716 0.05973877 0.01948842 ... 0. 0.4592792 0.28230175] Sparsity at: 0.05532306536438768 Epoch 357/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3086e-06 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... 0. 0.45821822 0.28283468] Sparsity at: 0.05532306536438768 Epoch 358/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1505e-06 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45851034 0.28274634] Sparsity at: 0.05532306536438768 Epoch 359/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7418e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4581587 0.28342044] Sparsity at: 0.05532306536438768 Epoch 360/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5167e-06 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9852 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4584856 0.28279388] Sparsity at: 0.05532306536438768 Epoch 361/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0606e-06 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9856 [-0.04982716 0.05973877 0.01948842 ... 0. 0.458797 0.2833478 ] Sparsity at: 0.05532306536438768 Epoch 362/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5018e-06 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.45989436 0.28209025] Sparsity at: 0.05532306536438768 Epoch 363/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9720e-04 - accuracy: 0.9998 - val_loss: 0.1418 - val_accuracy: 0.9811 [-0.04982716 0.05973877 0.01948842 ... -0. 0.4657365 0.28327924] Sparsity at: 0.05532306536438768 Epoch 364/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.1416 - val_accuracy: 0.9818 [-0.04982716 0.05973877 0.01948842 ... 0. 0.473192 0.3003992 ] Sparsity at: 0.05532306536438768 Epoch 365/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1171 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5007538 0.30398205] Sparsity at: 0.05532306536438768 Epoch 366/500 235/235 [==============================] - 3s 13ms/step - loss: 9.0517e-04 - accuracy: 0.9997 - val_loss: 0.1256 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.49749655 0.29741535] Sparsity at: 0.05532306536438768 Epoch 367/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5740e-04 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5010914 0.29638526] Sparsity at: 0.05532306536438768 Epoch 368/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5423e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5061297 0.29758725] Sparsity at: 0.05532306536438768 Epoch 369/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1312e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50753206 0.30142626] Sparsity at: 0.05532306536438768 Epoch 370/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1123e-05 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50824636 0.30123538] Sparsity at: 0.05532306536438768 Epoch 371/500 235/235 [==============================] - 3s 13ms/step - loss: 5.5512e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5053327 0.30245307] Sparsity at: 0.05532306536438768 Epoch 372/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9210e-05 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5054767 0.30247843] Sparsity at: 0.05532306536438768 Epoch 373/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2359e-05 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9850 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50523615 0.30327407] Sparsity at: 0.05532306536438768 Epoch 374/500 235/235 [==============================] - 3s 13ms/step - loss: 7.7801e-06 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... 0. 0.50510925 0.30375057] Sparsity at: 0.05532306536438768 Epoch 375/500 235/235 [==============================] - 3s 13ms/step - loss: 7.8674e-06 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9851 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50459 0.30456388] Sparsity at: 0.05532306536438768 Epoch 376/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4866e-04 - accuracy: 0.9999 - val_loss: 0.1212 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50153035 0.31111473] Sparsity at: 0.05532306536438768 Epoch 377/500 235/235 [==============================] - 3s 13ms/step - loss: 7.5106e-05 - accuracy: 1.0000 - val_loss: 0.1203 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5016039 0.310885 ] Sparsity at: 0.05532306536438768 Epoch 378/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9823e-05 - accuracy: 1.0000 - val_loss: 0.1192 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... 0. 0.50245667 0.31265646] Sparsity at: 0.05532306536438768 Epoch 379/500 235/235 [==============================] - 3s 13ms/step - loss: 9.4999e-06 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5032469 0.31338224] Sparsity at: 0.05532306536438768 Epoch 380/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5604e-04 - accuracy: 0.9999 - val_loss: 0.1204 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5014787 0.31252626] Sparsity at: 0.05532306536438768 Epoch 381/500 235/235 [==============================] - 3s 13ms/step - loss: 9.1546e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50098723 0.3185105 ] Sparsity at: 0.05532306536438768 Epoch 382/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3154e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5006329 0.31674966] Sparsity at: 0.05532306536438768 Epoch 383/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0362e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5003671 0.31977242] Sparsity at: 0.05532306536438768 Epoch 384/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8784e-06 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5009117 0.31957304] Sparsity at: 0.05532306536438768 Epoch 385/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4874e-06 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5014185 0.3194861 ] Sparsity at: 0.05532306536438768 Epoch 386/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5017e-06 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.501621 0.31993163] Sparsity at: 0.05532306536438768 Epoch 387/500 235/235 [==============================] - 3s 13ms/step - loss: 7.4749e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9823 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5023707 0.3161628 ] Sparsity at: 0.05532306536438768 Epoch 388/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.1376 - val_accuracy: 0.9818 [-0.04982716 0.05973877 0.01948842 ... -0. 0.52636707 0.28956854] Sparsity at: 0.05532306536438768 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1352 - val_accuracy: 0.9826 [-0.04982716 0.05973877 0.01948842 ... -0. 0.515818 0.30215022] Sparsity at: 0.05532306536438768 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2414e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... 0. 0.51656497 0.29993185] Sparsity at: 0.05532306536438768 Epoch 391/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9249e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5186281 0.30094177] Sparsity at: 0.05532306536438768 Epoch 392/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1163e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... 0. 0.52019125 0.30100995] Sparsity at: 0.05532306536438768 Epoch 393/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5208e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5201942 0.30117047] Sparsity at: 0.05532306536438768 Epoch 394/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2785e-05 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.51965505 0.3012742 ] Sparsity at: 0.05532306536438768 Epoch 395/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0934e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5202651 0.3011564 ] Sparsity at: 0.05532306536438768 Epoch 396/500 235/235 [==============================] - 3s 13ms/step - loss: 8.9156e-06 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.52051437 0.30114996] Sparsity at: 0.05532306536438768 Epoch 397/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1424e-05 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5202042 0.3015433 ] Sparsity at: 0.05532306536438768 Epoch 398/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2730e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.521056 0.30076516] Sparsity at: 0.05532306536438768 Epoch 399/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2268e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5210935 0.29945865] Sparsity at: 0.05532306536438768 Epoch 400/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9194e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... 0. 0.52109504 0.3002102 ] Sparsity at: 0.05532306536438768 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.6955513260454822 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6126025692631742 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.47423333 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 1. 0. ... 0. 0. 0.] [0. 0. 1. ... 1. 0. 1.] ... [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.9414327682117545 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 1. 0. 1. 1. 0.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 0. 0. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 1.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [0. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 180s 11ms/step - loss: 4.2713e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5210336 0.30053818] Sparsity at: 0.05532306536438768 Epoch 402/500 235/235 [==============================] - 3s 12ms/step - loss: 3.5542e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5208555 0.30074525] Sparsity at: 0.05532306536438768 Epoch 403/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9147e-06 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5209104 0.300975 ] Sparsity at: 0.05532306536438768 Epoch 404/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4511e-06 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5207364 0.30101952] Sparsity at: 0.05532306536438768 Epoch 405/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4820e-06 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5223767 0.30045474] Sparsity at: 0.05532306536438768 Epoch 406/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2645e-06 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.52232724 0.30103946] Sparsity at: 0.05532306536438768 Epoch 407/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 0.1832 - val_accuracy: 0.9774 [-0.04982716 0.05973877 0.01948842 ... -0. 0.49645117 0.29623982] Sparsity at: 0.05532306536438768 Epoch 408/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.1512 - val_accuracy: 0.9810 [-0.04982716 0.05973877 0.01948842 ... 0. 0.527881 0.2878974 ] Sparsity at: 0.05532306536438768 Epoch 409/500 235/235 [==============================] - 3s 15ms/step - loss: 9.1773e-04 - accuracy: 0.9997 - val_loss: 0.1365 - val_accuracy: 0.9823 [-0.04982716 0.05973877 0.01948842 ... -0. 0.52309227 0.2774441 ] Sparsity at: 0.05532306536438768 Epoch 410/500 235/235 [==============================] - 4s 15ms/step - loss: 4.6863e-04 - accuracy: 0.9998 - val_loss: 0.1297 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5361131 0.28076628] Sparsity at: 0.05532306536438768 Epoch 411/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3569e-05 - accuracy: 1.0000 - val_loss: 0.1270 - val_accuracy: 0.9827 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5371284 0.27949962] Sparsity at: 0.05532306536438768 Epoch 412/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9570e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9831 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53746456 0.2795313 ] Sparsity at: 0.05532306536438768 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1592e-05 - accuracy: 1.0000 - val_loss: 0.1276 - val_accuracy: 0.9832 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5386377 0.27915117] Sparsity at: 0.05532306536438768 Epoch 414/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4689e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5378048 0.2790982 ] Sparsity at: 0.05532306536438768 Epoch 415/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6818e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.53727275 0.28277516] Sparsity at: 0.05532306536438768 Epoch 416/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0559e-05 - accuracy: 1.0000 - val_loss: 0.1259 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5365682 0.28414074] Sparsity at: 0.05532306536438768 Epoch 417/500 235/235 [==============================] - 3s 13ms/step - loss: 9.7407e-06 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5363638 0.2841104 ] Sparsity at: 0.05532306536438768 Epoch 418/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0180e-06 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.53626776 0.28413 ] Sparsity at: 0.05532306536438768 Epoch 419/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7505e-05 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53651994 0.28355727] Sparsity at: 0.05532306536438768 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1300e-04 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5191347 0.26550815] Sparsity at: 0.05532306536438768 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4271e-05 - accuracy: 1.0000 - val_loss: 0.1282 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... -0. 0.52168643 0.28079024] Sparsity at: 0.05532306536438768 Epoch 422/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5510e-04 - accuracy: 0.9999 - val_loss: 0.1283 - val_accuracy: 0.9826 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53663653 0.26957372] Sparsity at: 0.05532306536438768 Epoch 423/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3769e-04 - accuracy: 0.9999 - val_loss: 0.1248 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.52908003 0.27156737] Sparsity at: 0.05532306536438768 Epoch 424/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0717e-04 - accuracy: 0.9998 - val_loss: 0.1443 - val_accuracy: 0.9829 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53554624 0.29214096] Sparsity at: 0.05532306536438768 Epoch 425/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1321 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5330179 0.27747452] Sparsity at: 0.05532306536438768 Epoch 426/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3595e-04 - accuracy: 0.9999 - val_loss: 0.1308 - val_accuracy: 0.9830 [-0.04982716 0.05973877 0.01948842 ... 0. 0.52952915 0.2802243 ] Sparsity at: 0.05532306536438768 Epoch 427/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1631e-04 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5309915 0.28262174] Sparsity at: 0.05532306536438768 Epoch 428/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3524e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.53200155 0.28156304] Sparsity at: 0.05532306536438768 Epoch 429/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1513e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53230065 0.2819927 ] Sparsity at: 0.05532306536438768 Epoch 430/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3787e-05 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.53197134 0.28296068] Sparsity at: 0.05532306536438768 Epoch 431/500 235/235 [==============================] - 3s 13ms/step - loss: 6.5912e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5325488 0.28088087] Sparsity at: 0.05532306536438768 Epoch 432/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1970e-04 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.54094464 0.28011298] Sparsity at: 0.05532306536438768 Epoch 433/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2602e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5449595 0.2799128 ] Sparsity at: 0.05532306536438768 Epoch 434/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7330e-04 - accuracy: 0.9999 - val_loss: 0.1273 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53867936 0.29310128] Sparsity at: 0.05532306536438768 Epoch 435/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1486 - val_accuracy: 0.9815 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5525666 0.28388616] Sparsity at: 0.05532306536438768 Epoch 436/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1438 - val_accuracy: 0.9829 [-0.04982716 0.05973877 0.01948842 ... -0. 0.55337054 0.3150877 ] Sparsity at: 0.05532306536438768 Epoch 437/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2341e-04 - accuracy: 0.9999 - val_loss: 0.1327 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5263115 0.30942366] Sparsity at: 0.05532306536438768 Epoch 438/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5070e-04 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5289538 0.3103089 ] Sparsity at: 0.05532306536438768 Epoch 439/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4775e-05 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5324671 0.3134772 ] Sparsity at: 0.05532306536438768 Epoch 440/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8365e-05 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53326786 0.31338337] Sparsity at: 0.05532306536438768 Epoch 441/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4052e-05 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53548634 0.3148533 ] Sparsity at: 0.05532306536438768 Epoch 442/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1088e-05 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53562844 0.31470722] Sparsity at: 0.05532306536438768 Epoch 443/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3710e-05 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... -0. 0.536015 0.31658113] Sparsity at: 0.05532306536438768 Epoch 444/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2991e-05 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5364563 0.3167361 ] Sparsity at: 0.05532306536438768 Epoch 445/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2624e-06 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.53677535 0.32057145] Sparsity at: 0.05532306536438768 Epoch 446/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4908e-06 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5383855 0.31846514] Sparsity at: 0.05532306536438768 Epoch 447/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2670e-06 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5389112 0.31941938] Sparsity at: 0.05532306536438768 Epoch 448/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3803e-06 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.53889704 0.32050425] Sparsity at: 0.05532306536438768 Epoch 449/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6598e-06 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5394477 0.32096836] Sparsity at: 0.05532306536438768 Epoch 450/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7682e-06 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9843 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5400168 0.3253128 ] Sparsity at: 0.05532306536438768 Epoch 451/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2756e-06 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... -0. 0.54224855 0.3115583 ] Sparsity at: 0.05532306536438768 Epoch 452/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2170e-06 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... 0. 0.54335266 0.3139447 ] Sparsity at: 0.05532306536438768 Epoch 453/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5040e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9844 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5430496 0.31725064] Sparsity at: 0.05532306536438768 Epoch 454/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8035e-06 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.54108584 0.32385433] Sparsity at: 0.05532306536438768 Epoch 455/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3150e-06 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.53896636 0.32035512] Sparsity at: 0.05532306536438768 Epoch 456/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1707 - val_accuracy: 0.9791 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5504874 0.24591713] Sparsity at: 0.05532306536438768 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1444 - val_accuracy: 0.9831 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5229932 0.2547202 ] Sparsity at: 0.05532306536438768 Epoch 458/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7820e-04 - accuracy: 0.9999 - val_loss: 0.1378 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5266959 0.2523252 ] Sparsity at: 0.05532306536438768 Epoch 459/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8650e-04 - accuracy: 0.9999 - val_loss: 0.1396 - val_accuracy: 0.9840 [-0.04982716 0.05973877 0.01948842 ... 0. 0.52342343 0.25432187] Sparsity at: 0.05532306536438768 Epoch 460/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2779e-04 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... -0. 0.52168137 0.2558187 ] Sparsity at: 0.05532306536438768 Epoch 461/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7131e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5201269 0.25625893] Sparsity at: 0.05532306536438768 Epoch 462/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1591e-05 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9845 [-0.04982716 0.05973877 0.01948842 ... 0. 0.51794636 0.25729594] Sparsity at: 0.05532306536438768 Epoch 463/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1354e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5176448 0.25785604] Sparsity at: 0.05532306536438768 Epoch 464/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2794e-05 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5172574 0.25917947] Sparsity at: 0.05532306536438768 Epoch 465/500 235/235 [==============================] - 3s 13ms/step - loss: 7.7986e-06 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.51677996 0.2597112 ] Sparsity at: 0.05532306536438768 Epoch 466/500 235/235 [==============================] - 3s 13ms/step - loss: 6.3350e-06 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.516446 0.26018885] Sparsity at: 0.05532306536438768 Epoch 467/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0108e-06 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5162901 0.26076654] Sparsity at: 0.05532306536438768 Epoch 468/500 235/235 [==============================] - 3s 13ms/step - loss: 9.6696e-06 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... 0. 0.51587224 0.26094478] Sparsity at: 0.05532306536438768 Epoch 469/500 235/235 [==============================] - 3s 13ms/step - loss: 6.6151e-06 - accuracy: 1.0000 - val_loss: 0.1350 - val_accuracy: 0.9849 [-0.04982716 0.05973877 0.01948842 ... -0. 0.51515514 0.2612386 ] Sparsity at: 0.05532306536438768 Epoch 470/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0970e-06 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5150388 0.26139995] Sparsity at: 0.05532306536438768 Epoch 471/500 235/235 [==============================] - 3s 15ms/step - loss: 4.7767e-06 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.51389974 0.2622641 ] Sparsity at: 0.05532306536438768 Epoch 472/500 235/235 [==============================] - 3s 13ms/step - loss: 4.2047e-06 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9846 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5135113 0.26244745] Sparsity at: 0.05532306536438768 Epoch 473/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6117e-06 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5135251 0.263105 ] Sparsity at: 0.05532306536438768 Epoch 474/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6434e-06 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9847 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5134599 0.26328033] Sparsity at: 0.05532306536438768 Epoch 475/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8866e-05 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9848 [-0.04982716 0.05973877 0.01948842 ... -0. 0.51327163 0.26956156] Sparsity at: 0.05532306536438768 Epoch 476/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1549 - val_accuracy: 0.9814 [-0.04982716 0.05973877 0.01948842 ... 0. 0.48847535 0.29634196] Sparsity at: 0.05532306536438768 Epoch 477/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1510 - val_accuracy: 0.9810 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5041807 0.30275723] Sparsity at: 0.05532306536438768 Epoch 478/500 235/235 [==============================] - 3s 15ms/step - loss: 3.6720e-04 - accuracy: 0.9999 - val_loss: 0.1445 - val_accuracy: 0.9828 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5039699 0.3014864 ] Sparsity at: 0.05532306536438768 Epoch 479/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5922e-04 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5081401 0.30228072] Sparsity at: 0.05532306536438768 Epoch 480/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5361e-05 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.50764865 0.30287766] Sparsity at: 0.05532306536438768 Epoch 481/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7885e-05 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5087834 0.30331257] Sparsity at: 0.05532306536438768 Epoch 482/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7546e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5050485 0.3044693 ] Sparsity at: 0.05532306536438768 Epoch 483/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2830e-05 - accuracy: 1.0000 - val_loss: 0.1356 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.5028577 0.30476004] Sparsity at: 0.05532306536438768 Epoch 484/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1952e-05 - accuracy: 1.0000 - val_loss: 0.1352 - val_accuracy: 0.9835 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5025818 0.3050833 ] Sparsity at: 0.05532306536438768 Epoch 485/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4024e-06 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5017011 0.30694985] Sparsity at: 0.05532306536438768 Epoch 486/500 235/235 [==============================] - 3s 13ms/step - loss: 8.2682e-06 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5011062 0.3066643 ] Sparsity at: 0.05532306536438768 Epoch 487/500 235/235 [==============================] - 3s 13ms/step - loss: 5.6268e-06 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50109154 0.30688888] Sparsity at: 0.05532306536438768 Epoch 488/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6665e-06 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.50150394 0.30690894] Sparsity at: 0.05532306536438768 Epoch 489/500 235/235 [==============================] - 3s 13ms/step - loss: 6.8122e-06 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9837 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5013247 0.30714166] Sparsity at: 0.05532306536438768 Epoch 490/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4690e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9836 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50161153 0.30729237] Sparsity at: 0.05532306536438768 Epoch 491/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9907e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9838 [-0.04982716 0.05973877 0.01948842 ... 0. 0.501716 0.30763265] Sparsity at: 0.05532306536438768 Epoch 492/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1219e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... 0. 0.502364 0.30610272] Sparsity at: 0.05532306536438768 Epoch 493/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3964e-06 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9842 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5022471 0.30661505] Sparsity at: 0.05532306536438768 Epoch 494/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7587e-06 - accuracy: 1.0000 - val_loss: 0.1363 - val_accuracy: 0.9839 [-0.04982716 0.05973877 0.01948842 ... 0. 0.50213057 0.306776 ] Sparsity at: 0.05532306536438768 Epoch 495/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1154e-05 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9841 [-0.04982716 0.05973877 0.01948842 ... 0. 0.50217587 0.288102 ] Sparsity at: 0.05532306536438768 Epoch 496/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6088e-04 - accuracy: 0.9999 - val_loss: 0.1504 - val_accuracy: 0.9815 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5296241 0.24158844] Sparsity at: 0.05532306536438768 Epoch 497/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1696 - val_accuracy: 0.9794 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5090498 0.27930385] Sparsity at: 0.05532306536438768 Epoch 498/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1422 - val_accuracy: 0.9821 [-0.04982716 0.05973877 0.01948842 ... -0. 0.503968 0.2755768 ] Sparsity at: 0.05532306536438768 Epoch 499/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4543e-04 - accuracy: 0.9999 - val_loss: 0.1371 - val_accuracy: 0.9833 [-0.04982716 0.05973877 0.01948842 ... -0. 0.50573874 0.27428147] Sparsity at: 0.05532306536438768 Epoch 500/500 235/235 [==============================] - 3s 13ms/step - loss: 8.2261e-05 - accuracy: 1.0000 - val_loss: 0.1370 - val_accuracy: 0.9834 [-0.04982716 0.05973877 0.01948842 ... 0. 0.5055063 0.2736778 ] Sparsity at: 0.05532306536438768 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.042179541662335396 Thresholhold -0.05633559077978134 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08939405530691147 Thresholhold 0.003573372960090637 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10492659732699394 Thresholhold 0.13731814920902252 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:26 - loss: 4.5594 - accuracy: 0.0469WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0061s vs `on_train_batch_begin` time: 2.4660s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 1.5780 - accuracy: 0.8525 - val_loss: 0.9437 - val_accuracy: 0.9044 [ 4.5475988e-07 9.0963016e-07 -1.0694450e-07 ... -2.1114853e-01 -1.6127768e-01 1.4526787e-01] Sparsity at: 0.013428782188841202 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9004 - accuracy: 0.8984 - val_loss: 0.8537 - val_accuracy: 0.9061 [-2.8473083e-12 1.1483228e-11 5.0856444e-13 ... -1.6856050e-01 -1.4945585e-01 1.6102970e-01] Sparsity at: 0.013428782188841202 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8625 - accuracy: 0.8997 - val_loss: 0.8396 - val_accuracy: 0.9054 [ 1.3545059e-17 4.3443245e-17 1.4456606e-18 ... -1.4215761e-01 -1.4187299e-01 1.8113463e-01] Sparsity at: 0.013428782188841202 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8521 - accuracy: 0.9007 - val_loss: 0.8325 - val_accuracy: 0.9053 [ 4.2891570e-23 -6.4231276e-23 -2.1155007e-24 ... -1.2928811e-01 -1.3472477e-01 1.9978335e-01] Sparsity at: 0.013428782188841202 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8463 - accuracy: 0.9006 - val_loss: 0.8277 - val_accuracy: 0.9054 [-9.5993928e-29 6.4666834e-28 -4.4591661e-29 ... -1.2403209e-01 -1.2691663e-01 2.1329701e-01] Sparsity at: 0.013428782188841202 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8420 - accuracy: 0.9012 - val_loss: 0.8252 - val_accuracy: 0.9046 [-2.4919617e-34 -3.3479611e-33 -4.8786629e-34 ... -1.2253988e-01 -1.1949231e-01 2.2268082e-01] Sparsity at: 0.013428782188841202 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9010 - val_loss: 0.8226 - val_accuracy: 0.9044 [ 3.17312738e-34 5.28513877e-34 -4.87866293e-34 ... -1.22799225e-01 -1.12141855e-01 2.28833780e-01] Sparsity at: 0.013428782188841202 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8371 - accuracy: 0.9009 - val_loss: 0.8204 - val_accuracy: 0.9040 [ 3.17312738e-34 5.28513877e-34 -4.87866293e-34 ... -1.24042794e-01 -1.04983211e-01 2.32911736e-01] Sparsity at: 0.013428782188841202 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8355 - accuracy: 0.9010 - val_loss: 0.8189 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2496045e-01 -9.8333962e-02 2.3376772e-01] Sparsity at: 0.013428782188841202 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8344 - accuracy: 0.9009 - val_loss: 0.8182 - val_accuracy: 0.9032 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2592028e-01 -9.2154019e-02 2.3430060e-01] Sparsity at: 0.013428782188841202 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8335 - accuracy: 0.9009 - val_loss: 0.8166 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2658608e-01 -8.6029150e-02 2.3419960e-01] Sparsity at: 0.013428782188841202 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8325 - accuracy: 0.9006 - val_loss: 0.8169 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2716629e-01 -7.9783775e-02 2.3338556e-01] Sparsity at: 0.013428782188841202 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8320 - accuracy: 0.9006 - val_loss: 0.8157 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2827858e-01 -7.2897233e-02 2.3223698e-01] Sparsity at: 0.013428782188841202 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8313 - accuracy: 0.9009 - val_loss: 0.8164 - val_accuracy: 0.9031 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2870763e-01 -6.6217788e-02 2.2989169e-01] Sparsity at: 0.013428782188841202 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8308 - accuracy: 0.9010 - val_loss: 0.8156 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.2947418e-01 -5.9880666e-02 2.2718878e-01] Sparsity at: 0.013428782188841202 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8303 - accuracy: 0.9007 - val_loss: 0.8161 - val_accuracy: 0.9030 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.3072118e-01 -5.3231589e-02 2.2447559e-01] Sparsity at: 0.013428782188841202 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8302 - accuracy: 0.9006 - val_loss: 0.8147 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.3205472e-01 -4.6203740e-02 2.2203453e-01] Sparsity at: 0.013428782188841202 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8294 - accuracy: 0.9008 - val_loss: 0.8140 - val_accuracy: 0.9029 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.3396858e-01 -3.8817868e-02 2.1891777e-01] Sparsity at: 0.013428782188841202 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8293 - accuracy: 0.9010 - val_loss: 0.8139 - val_accuracy: 0.9030 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.3609716e-01 -3.0903092e-02 2.1660043e-01] Sparsity at: 0.013428782188841202 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8287 - accuracy: 0.9013 - val_loss: 0.8130 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.3891685e-01 -2.1367926e-02 2.1418606e-01] Sparsity at: 0.013428782188841202 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8284 - accuracy: 0.9012 - val_loss: 0.8139 - val_accuracy: 0.9024 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.4271159e-01 -1.0610447e-02 2.1245191e-01] Sparsity at: 0.013428782188841202 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8283 - accuracy: 0.9010 - val_loss: 0.8124 - val_accuracy: 0.9031 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.4768597e-01 2.4324600e-03 2.1122523e-01] Sparsity at: 0.013428782188841202 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8281 - accuracy: 0.9009 - val_loss: 0.8129 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.5270925e-01 1.6758425e-02 2.0936862e-01] Sparsity at: 0.013428782188841202 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8281 - accuracy: 0.9011 - val_loss: 0.8121 - val_accuracy: 0.9027 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.5732294e-01 3.2971688e-02 2.0744301e-01] Sparsity at: 0.013428782188841202 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9017 - val_loss: 0.8124 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.6303615e-01 5.0393268e-02 2.0540491e-01] Sparsity at: 0.013428782188841202 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8276 - accuracy: 0.9012 - val_loss: 0.8124 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.6887848e-01 6.8029106e-02 2.0382623e-01] Sparsity at: 0.013428782188841202 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9014 - val_loss: 0.8119 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.7474389e-01 8.4843218e-02 2.0244543e-01] Sparsity at: 0.013428782188841202 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8272 - accuracy: 0.9012 - val_loss: 0.8125 - val_accuracy: 0.9029 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.7915966e-01 9.9111311e-02 2.0087935e-01] Sparsity at: 0.013428782188841202 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9013 - val_loss: 0.8120 - val_accuracy: 0.9036 [ 3.17312738e-34 5.28513877e-34 -4.87866293e-34 ... -1.83177650e-01 1.11306235e-01 1.99301392e-01] Sparsity at: 0.013428782188841202 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8273 - accuracy: 0.9009 - val_loss: 0.8113 - val_accuracy: 0.9030 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.8661909e-01 1.2168070e-01 1.9879262e-01] Sparsity at: 0.013428782188841202 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8271 - accuracy: 0.9017 - val_loss: 0.8115 - val_accuracy: 0.9031 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.8911327e-01 1.3033628e-01 1.9865364e-01] Sparsity at: 0.013428782188841202 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8269 - accuracy: 0.9015 - val_loss: 0.8113 - val_accuracy: 0.9030 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.9211307e-01 1.3732308e-01 1.9895643e-01] Sparsity at: 0.013428782188841202 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8268 - accuracy: 0.9014 - val_loss: 0.8122 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.9425830e-01 1.4281784e-01 1.9968069e-01] Sparsity at: 0.013428782188841202 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8268 - accuracy: 0.9010 - val_loss: 0.8117 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.9578844e-01 1.4711940e-01 1.9983456e-01] Sparsity at: 0.013428782188841202 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8267 - accuracy: 0.9014 - val_loss: 0.8119 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.9742593e-01 1.5133455e-01 2.0035335e-01] Sparsity at: 0.013428782188841202 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8270 - accuracy: 0.9014 - val_loss: 0.8122 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.9811359e-01 1.5420264e-01 2.0088133e-01] Sparsity at: 0.013428782188841202 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8265 - accuracy: 0.9016 - val_loss: 0.8111 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -1.9940473e-01 1.5691824e-01 2.0143208e-01] Sparsity at: 0.013428782188841202 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8265 - accuracy: 0.9013 - val_loss: 0.8115 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0084132e-01 1.5904133e-01 2.0193990e-01] Sparsity at: 0.013428782188841202 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8267 - accuracy: 0.9014 - val_loss: 0.8118 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0176548e-01 1.6157974e-01 2.0300451e-01] Sparsity at: 0.013428782188841202 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8266 - accuracy: 0.9014 - val_loss: 0.8115 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0229991e-01 1.6367090e-01 2.0333448e-01] Sparsity at: 0.013428782188841202 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8265 - accuracy: 0.9013 - val_loss: 0.8105 - val_accuracy: 0.9031 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0316249e-01 1.6504906e-01 2.0427863e-01] Sparsity at: 0.013428782188841202 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8262 - accuracy: 0.9015 - val_loss: 0.8118 - val_accuracy: 0.9032 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0392489e-01 1.6690196e-01 2.0502971e-01] Sparsity at: 0.013428782188841202 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8263 - accuracy: 0.9011 - val_loss: 0.8106 - val_accuracy: 0.9032 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0507868e-01 1.6848043e-01 2.0541219e-01] Sparsity at: 0.013428782188841202 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8264 - accuracy: 0.9015 - val_loss: 0.8110 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0562387e-01 1.6960602e-01 2.0620069e-01] Sparsity at: 0.013428782188841202 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8264 - accuracy: 0.9014 - val_loss: 0.8110 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0618463e-01 1.7072159e-01 2.0682956e-01] Sparsity at: 0.013428782188841202 Epoch 46/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8262 - accuracy: 0.9021 - val_loss: 0.8110 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0684901e-01 1.7152943e-01 2.0750417e-01] Sparsity at: 0.013428782188841202 Epoch 47/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8259 - accuracy: 0.9014 - val_loss: 0.8108 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0757803e-01 1.7325091e-01 2.0828106e-01] Sparsity at: 0.013428782188841202 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8260 - accuracy: 0.9014 - val_loss: 0.8108 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0820795e-01 1.7399633e-01 2.0915723e-01] Sparsity at: 0.013428782188841202 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8264 - accuracy: 0.9014 - val_loss: 0.8118 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0864348e-01 1.7471792e-01 2.0970197e-01] Sparsity at: 0.013428782188841202 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8262 - accuracy: 0.9015 - val_loss: 0.8109 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0936647e-01 1.7569935e-01 2.1039537e-01] Sparsity at: 0.013428782188841202 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.009441253711567343 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.03559083378736716 Thresholhold -0.02379034087061882 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.11488785808859348 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 0.8259 - accuracy: 0.9017 - val_loss: 0.8108 - val_accuracy: 0.9036 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.0965262e-01 1.7660750e-01 2.1101573e-01] Sparsity at: 0.013428782188841202 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8258 - accuracy: 0.9015 - val_loss: 0.8105 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1057877e-01 1.7766993e-01 2.1179493e-01] Sparsity at: 0.013428782188841202 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9017 - val_loss: 0.8100 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1084429e-01 1.7788151e-01 2.1248877e-01] Sparsity at: 0.013428782188841202 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9015 - val_loss: 0.8107 - val_accuracy: 0.9036 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1129917e-01 1.7879543e-01 2.1280846e-01] Sparsity at: 0.013428782188841202 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9015 - val_loss: 0.8104 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1150069e-01 1.7997697e-01 2.1306658e-01] Sparsity at: 0.013428782188841202 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8260 - accuracy: 0.9015 - val_loss: 0.8109 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1159616e-01 1.8016966e-01 2.1302189e-01] Sparsity at: 0.013428782188841202 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9016 - val_loss: 0.8104 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1159831e-01 1.8111238e-01 2.1379106e-01] Sparsity at: 0.013428782188841202 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9013 - val_loss: 0.8108 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1223912e-01 1.8207943e-01 2.1438715e-01] Sparsity at: 0.013428782188841202 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8260 - accuracy: 0.9013 - val_loss: 0.8098 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1250932e-01 1.8276453e-01 2.1403818e-01] Sparsity at: 0.013428782188841202 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9018 - val_loss: 0.8110 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1265607e-01 1.8376535e-01 2.1399593e-01] Sparsity at: 0.013428782188841202 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8259 - accuracy: 0.9019 - val_loss: 0.8104 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1316163e-01 1.8441734e-01 2.1453735e-01] Sparsity at: 0.013428782188841202 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8258 - accuracy: 0.9014 - val_loss: 0.8112 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1342434e-01 1.8508859e-01 2.1536690e-01] Sparsity at: 0.013428782188841202 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9018 - val_loss: 0.8108 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1366639e-01 1.8553291e-01 2.1547391e-01] Sparsity at: 0.013428782188841202 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9014 - val_loss: 0.8101 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1391803e-01 1.8608092e-01 2.1552931e-01] Sparsity at: 0.013428782188841202 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8257 - accuracy: 0.9014 - val_loss: 0.8113 - val_accuracy: 0.9031 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1395041e-01 1.8615401e-01 2.1506196e-01] Sparsity at: 0.013428782188841202 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9020 - val_loss: 0.8101 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1461868e-01 1.8680488e-01 2.1583962e-01] Sparsity at: 0.013428782188841202 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9020 - val_loss: 0.8102 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1452565e-01 1.8685091e-01 2.1637264e-01] Sparsity at: 0.013428782188841202 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9015 - val_loss: 0.8097 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1492539e-01 1.8703038e-01 2.1622847e-01] Sparsity at: 0.013428782188841202 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9019 - val_loss: 0.8106 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1508636e-01 1.8720873e-01 2.1651430e-01] Sparsity at: 0.013428782188841202 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9014 - val_loss: 0.8104 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1509993e-01 1.8755139e-01 2.1654898e-01] Sparsity at: 0.013428782188841202 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8258 - accuracy: 0.9017 - val_loss: 0.8106 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1531779e-01 1.8756884e-01 2.1704994e-01] Sparsity at: 0.013428782188841202 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8257 - accuracy: 0.9016 - val_loss: 0.8109 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1520807e-01 1.8755570e-01 2.1712416e-01] Sparsity at: 0.013428782188841202 Epoch 73/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8256 - accuracy: 0.9022 - val_loss: 0.8105 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1546698e-01 1.8791614e-01 2.1735533e-01] Sparsity at: 0.013428782188841202 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8099 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1571183e-01 1.8786383e-01 2.1732402e-01] Sparsity at: 0.013428782188841202 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8103 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1583232e-01 1.8817924e-01 2.1724449e-01] Sparsity at: 0.013428782188841202 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9019 - val_loss: 0.8107 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1558972e-01 1.8796206e-01 2.1694465e-01] Sparsity at: 0.013428782188841202 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9018 - val_loss: 0.8103 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1542946e-01 1.8766227e-01 2.1721591e-01] Sparsity at: 0.013428782188841202 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9021 - val_loss: 0.8110 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1581295e-01 1.8799131e-01 2.1745715e-01] Sparsity at: 0.013428782188841202 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9018 - val_loss: 0.8101 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1584940e-01 1.8828377e-01 2.1732926e-01] Sparsity at: 0.013428782188841202 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8258 - accuracy: 0.9019 - val_loss: 0.8102 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1579039e-01 1.8808867e-01 2.1748200e-01] Sparsity at: 0.013428782188841202 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9017 - val_loss: 0.8101 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1579254e-01 1.8772689e-01 2.1789362e-01] Sparsity at: 0.013428782188841202 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9019 - val_loss: 0.8098 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1608305e-01 1.8840347e-01 2.1798377e-01] Sparsity at: 0.013428782188841202 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9019 - val_loss: 0.8099 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1589050e-01 1.8860726e-01 2.1785197e-01] Sparsity at: 0.013428782188841202 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9020 - val_loss: 0.8099 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1647447e-01 1.8862234e-01 2.1810566e-01] Sparsity at: 0.013428782188841202 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8101 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1621238e-01 1.8888083e-01 2.1785569e-01] Sparsity at: 0.013428782188841202 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8257 - accuracy: 0.9017 - val_loss: 0.8107 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1600997e-01 1.8843308e-01 2.1785162e-01] Sparsity at: 0.013428782188841202 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9014 - val_loss: 0.8105 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1628718e-01 1.8815732e-01 2.1817762e-01] Sparsity at: 0.013428782188841202 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9020 - val_loss: 0.8103 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1639244e-01 1.8846615e-01 2.1817213e-01] Sparsity at: 0.013428782188841202 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9016 - val_loss: 0.8095 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1641313e-01 1.8842827e-01 2.1786851e-01] Sparsity at: 0.013428782188841202 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9021 - val_loss: 0.8103 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1672736e-01 1.8889549e-01 2.1785705e-01] Sparsity at: 0.013428782188841202 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8103 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1645734e-01 1.8916592e-01 2.1797971e-01] Sparsity at: 0.013428782188841202 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9018 - val_loss: 0.8107 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1660419e-01 1.8909994e-01 2.1792488e-01] Sparsity at: 0.013428782188841202 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9019 - val_loss: 0.8094 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1672015e-01 1.8861338e-01 2.1794356e-01] Sparsity at: 0.013428782188841202 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9021 - val_loss: 0.8108 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1694936e-01 1.8928029e-01 2.1819331e-01] Sparsity at: 0.013428782188841202 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8254 - accuracy: 0.9020 - val_loss: 0.8106 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1693899e-01 1.8890829e-01 2.1862958e-01] Sparsity at: 0.013428782188841202 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9020 - val_loss: 0.8099 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1660189e-01 1.8887699e-01 2.1852274e-01] Sparsity at: 0.013428782188841202 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9022 - val_loss: 0.8097 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1708454e-01 1.8926629e-01 2.1876776e-01] Sparsity at: 0.013428782188841202 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9017 - val_loss: 0.8092 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1728937e-01 1.8936679e-01 2.1862167e-01] Sparsity at: 0.013428782188841202 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8099 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1701878e-01 1.8890822e-01 2.1881764e-01] Sparsity at: 0.013428782188841202 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9020 - val_loss: 0.8100 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1732220e-01 1.8900216e-01 2.1887980e-01] Sparsity at: 0.013428782188841202 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.015024891875651813 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.045309476913746316 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.14850107733482965 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 52s 7ms/step - loss: 0.8256 - accuracy: 0.9015 - val_loss: 0.8106 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1691750e-01 1.8893623e-01 2.1873592e-01] Sparsity at: 0.013428782188841202 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8253 - accuracy: 0.9018 - val_loss: 0.8102 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1730509e-01 1.8905446e-01 2.1916999e-01] Sparsity at: 0.013428782188841202 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1708423e-01 1.8898202e-01 2.1877083e-01] Sparsity at: 0.013428782188841202 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9018 - val_loss: 0.8115 - val_accuracy: 0.9030 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1753532e-01 1.8918510e-01 2.1929243e-01] Sparsity at: 0.013428782188841202 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9017 - val_loss: 0.8104 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1758722e-01 1.8914010e-01 2.1874397e-01] Sparsity at: 0.013428782188841202 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9022 - val_loss: 0.8098 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1752451e-01 1.8903551e-01 2.1890281e-01] Sparsity at: 0.013428782188841202 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9015 - val_loss: 0.8097 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1751237e-01 1.8861207e-01 2.1919568e-01] Sparsity at: 0.013428782188841202 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9020 - val_loss: 0.8106 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1751499e-01 1.8857031e-01 2.1907878e-01] Sparsity at: 0.013428782188841202 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9014 - val_loss: 0.8095 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1744214e-01 1.8821216e-01 2.1922256e-01] Sparsity at: 0.013428782188841202 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9018 - val_loss: 0.8103 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1786608e-01 1.8836269e-01 2.1973003e-01] Sparsity at: 0.013428782188841202 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9014 - val_loss: 0.8098 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1810207e-01 1.8837088e-01 2.1995316e-01] Sparsity at: 0.013428782188841202 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9018 - val_loss: 0.8099 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1805945e-01 1.8856473e-01 2.1999797e-01] Sparsity at: 0.013428782188841202 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8089 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1837850e-01 1.8855773e-01 2.1995427e-01] Sparsity at: 0.013428782188841202 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9019 - val_loss: 0.8093 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1818906e-01 1.8835481e-01 2.2043721e-01] Sparsity at: 0.013428782188841202 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1830077e-01 1.8831664e-01 2.2045733e-01] Sparsity at: 0.013428782188841202 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9021 - val_loss: 0.8095 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1863565e-01 1.8823230e-01 2.2066894e-01] Sparsity at: 0.013428782188841202 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8100 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1847680e-01 1.8845226e-01 2.2066788e-01] Sparsity at: 0.013428782188841202 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9022 - val_loss: 0.8098 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1872316e-01 1.8790263e-01 2.2087130e-01] Sparsity at: 0.013428782188841202 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8254 - accuracy: 0.9019 - val_loss: 0.8105 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1904820e-01 1.8822761e-01 2.2125420e-01] Sparsity at: 0.013428782188841202 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9022 - val_loss: 0.8100 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1920653e-01 1.8792649e-01 2.2122265e-01] Sparsity at: 0.013428782188841202 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8256 - accuracy: 0.9018 - val_loss: 0.8093 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1953017e-01 1.8759955e-01 2.2166930e-01] Sparsity at: 0.013428782188841202 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9018 - val_loss: 0.8101 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1953610e-01 1.8799391e-01 2.2177435e-01] Sparsity at: 0.013428782188841202 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9018 - val_loss: 0.8102 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1956435e-01 1.8782035e-01 2.2155693e-01] Sparsity at: 0.013428782188841202 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9015 - val_loss: 0.8097 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1912192e-01 1.8740067e-01 2.2146490e-01] Sparsity at: 0.013428782188841202 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9020 - val_loss: 0.8098 - val_accuracy: 0.9036 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1938056e-01 1.8762331e-01 2.2166619e-01] Sparsity at: 0.013428782188841202 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9019 - val_loss: 0.8093 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1946156e-01 1.8759070e-01 2.2146851e-01] Sparsity at: 0.013428782188841202 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8094 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1957368e-01 1.8752669e-01 2.2172451e-01] Sparsity at: 0.013428782188841202 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9024 - val_loss: 0.8095 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1980201e-01 1.8817912e-01 2.2157042e-01] Sparsity at: 0.013428782188841202 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1998744e-01 1.8802285e-01 2.2170831e-01] Sparsity at: 0.013428782188841202 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8253 - accuracy: 0.9021 - val_loss: 0.8103 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1983947e-01 1.8817185e-01 2.2181414e-01] Sparsity at: 0.013428782188841202 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9024 - val_loss: 0.8096 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1975648e-01 1.8815584e-01 2.2155789e-01] Sparsity at: 0.013428782188841202 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8255 - accuracy: 0.9023 - val_loss: 0.8096 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1942802e-01 1.8780729e-01 2.2130731e-01] Sparsity at: 0.013428782188841202 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1948864e-01 1.8789469e-01 2.2165261e-01] Sparsity at: 0.013428782188841202 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8096 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1928988e-01 1.8786409e-01 2.2158840e-01] Sparsity at: 0.013428782188841202 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8089 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1977217e-01 1.8814011e-01 2.2188319e-01] Sparsity at: 0.013428782188841202 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1932432e-01 1.8787715e-01 2.2187150e-01] Sparsity at: 0.013428782188841202 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1957201e-01 1.8818991e-01 2.2189413e-01] Sparsity at: 0.013428782188841202 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8094 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1952100e-01 1.8835042e-01 2.2181131e-01] Sparsity at: 0.013428782188841202 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9020 - val_loss: 0.8099 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1921436e-01 1.8807252e-01 2.2209415e-01] Sparsity at: 0.013428782188841202 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8253 - accuracy: 0.9017 - val_loss: 0.8084 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1981990e-01 1.8825257e-01 2.2213607e-01] Sparsity at: 0.013428782188841202 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1979170e-01 1.8868835e-01 2.2230649e-01] Sparsity at: 0.013428782188841202 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2028460e-01 1.8890183e-01 2.2244377e-01] Sparsity at: 0.013428782188841202 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8096 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1957169e-01 1.8871959e-01 2.2228521e-01] Sparsity at: 0.013428782188841202 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1979278e-01 1.8862616e-01 2.2215046e-01] Sparsity at: 0.013428782188841202 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9027 - val_loss: 0.8088 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2030315e-01 1.8856107e-01 2.2209099e-01] Sparsity at: 0.013428782188841202 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8083 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2062553e-01 1.8887448e-01 2.2240750e-01] Sparsity at: 0.013428782188841202 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2003874e-01 1.8867487e-01 2.2192641e-01] Sparsity at: 0.013428782188841202 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2018965e-01 1.8927270e-01 2.2217008e-01] Sparsity at: 0.013428782188841202 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9035 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2000231e-01 1.8896244e-01 2.2216129e-01] Sparsity at: 0.013428782188841202 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2002183e-01 1.8919499e-01 2.2174932e-01] Sparsity at: 0.013428782188841202 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.020792046287780308 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.05585218787255908 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.1789812290466699 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8253 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1991199e-01 1.8881467e-01 2.2163633e-01] Sparsity at: 0.013428782188841202 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1990427e-01 1.8916716e-01 2.2171541e-01] Sparsity at: 0.013428782188841202 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8095 - val_accuracy: 0.9036 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2021005e-01 1.8920575e-01 2.2178040e-01] Sparsity at: 0.013428782188841202 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9016 - val_loss: 0.8089 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2035377e-01 1.8927330e-01 2.2239132e-01] Sparsity at: 0.013428782188841202 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9026 - val_loss: 0.8095 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1977003e-01 1.8908103e-01 2.2162078e-01] Sparsity at: 0.013428782188841202 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1971317e-01 1.8917902e-01 2.2176202e-01] Sparsity at: 0.013428782188841202 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2006735e-01 1.8925540e-01 2.2139469e-01] Sparsity at: 0.013428782188841202 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8252 - accuracy: 0.9025 - val_loss: 0.8092 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1963045e-01 1.8917167e-01 2.2136983e-01] Sparsity at: 0.013428782188841202 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1980971e-01 1.8947741e-01 2.2186331e-01] Sparsity at: 0.013428782188841202 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8088 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1964361e-01 1.8970221e-01 2.2164269e-01] Sparsity at: 0.013428782188841202 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2029231e-01 1.8955842e-01 2.2126974e-01] Sparsity at: 0.013428782188841202 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9019 - val_loss: 0.8092 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2012214e-01 1.8939570e-01 2.2176006e-01] Sparsity at: 0.013428782188841202 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8089 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1998785e-01 1.8937102e-01 2.2150403e-01] Sparsity at: 0.013428782188841202 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9038 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1995552e-01 1.8943179e-01 2.2161783e-01] Sparsity at: 0.013428782188841202 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8089 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2002983e-01 1.8938637e-01 2.2153018e-01] Sparsity at: 0.013428782188841202 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8084 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1987577e-01 1.8941106e-01 2.2141796e-01] Sparsity at: 0.013428782188841202 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1987103e-01 1.8942073e-01 2.2157770e-01] Sparsity at: 0.013428782188841202 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8095 - val_accuracy: 0.9034 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2027795e-01 1.8930638e-01 2.2131224e-01] Sparsity at: 0.013428782188841202 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9026 - val_loss: 0.8092 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1974969e-01 1.8940632e-01 2.2121806e-01] Sparsity at: 0.013428782188841202 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2018303e-01 1.8969157e-01 2.2108877e-01] Sparsity at: 0.013428782188841202 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8098 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2015695e-01 1.8953590e-01 2.2122557e-01] Sparsity at: 0.013428782188841202 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8103 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1993667e-01 1.8967526e-01 2.2101218e-01] Sparsity at: 0.013428782188841202 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8082 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2028992e-01 1.8957663e-01 2.2152053e-01] Sparsity at: 0.013428782188841202 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2000399e-01 1.8933606e-01 2.2133254e-01] Sparsity at: 0.013428782188841202 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8094 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1987593e-01 1.8951559e-01 2.2121239e-01] Sparsity at: 0.013428782188841202 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9027 - val_loss: 0.8093 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2002859e-01 1.8961023e-01 2.2128963e-01] Sparsity at: 0.013428782188841202 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8252 - accuracy: 0.9020 - val_loss: 0.8088 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2017328e-01 1.8939088e-01 2.2150552e-01] Sparsity at: 0.013428782188841202 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8096 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2004776e-01 1.8997234e-01 2.2118588e-01] Sparsity at: 0.013428782188841202 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2020438e-01 1.8936287e-01 2.2140606e-01] Sparsity at: 0.013428782188841202 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1987115e-01 1.8950178e-01 2.2115724e-01] Sparsity at: 0.013428782188841202 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2032031e-01 1.8935455e-01 2.2152431e-01] Sparsity at: 0.013428782188841202 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1980034e-01 1.8929297e-01 2.2079025e-01] Sparsity at: 0.013428782188841202 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1988244e-01 1.8937515e-01 2.2110310e-01] Sparsity at: 0.013428782188841202 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2011313e-01 1.8940745e-01 2.2127511e-01] Sparsity at: 0.013428782188841202 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8085 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1964718e-01 1.8902153e-01 2.2106732e-01] Sparsity at: 0.013428782188841202 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1980679e-01 1.8939045e-01 2.2137694e-01] Sparsity at: 0.013428782188841202 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8081 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2026271e-01 1.8955331e-01 2.2160232e-01] Sparsity at: 0.013428782188841202 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2005320e-01 1.8956378e-01 2.2172320e-01] Sparsity at: 0.013428782188841202 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1991584e-01 1.8923758e-01 2.2122888e-01] Sparsity at: 0.013428782188841202 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8095 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1986294e-01 1.8938507e-01 2.2094774e-01] Sparsity at: 0.013428782188841202 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1985416e-01 1.8948595e-01 2.2108071e-01] Sparsity at: 0.013428782188841202 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8090 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2047018e-01 1.8943965e-01 2.2143391e-01] Sparsity at: 0.013428782188841202 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8092 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2053917e-01 1.8956949e-01 2.2108352e-01] Sparsity at: 0.013428782188841202 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8084 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2008599e-01 1.8961443e-01 2.2110626e-01] Sparsity at: 0.013428782188841202 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8096 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2031750e-01 1.8945520e-01 2.2134954e-01] Sparsity at: 0.013428782188841202 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2030391e-01 1.8999153e-01 2.2126873e-01] Sparsity at: 0.013428782188841202 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9039 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2011054e-01 1.8951766e-01 2.2109798e-01] Sparsity at: 0.013428782188841202 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2017413e-01 1.8978642e-01 2.2119749e-01] Sparsity at: 0.013428782188841202 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1972160e-01 1.8921435e-01 2.2124641e-01] Sparsity at: 0.013428782188841202 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2021963e-01 1.8967004e-01 2.2127877e-01] Sparsity at: 0.013428782188841202 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.027723946008678446 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.06840466487041752 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.2025289757641069 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1993877e-01 1.8949914e-01 2.2111784e-01] Sparsity at: 0.013428782188841202 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1991411e-01 1.8950154e-01 2.2149597e-01] Sparsity at: 0.013428782188841202 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9026 - val_loss: 0.8097 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2021282e-01 1.8961611e-01 2.2150679e-01] Sparsity at: 0.013428782188841202 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8093 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1970625e-01 1.8929419e-01 2.2080384e-01] Sparsity at: 0.013428782188841202 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8097 - val_accuracy: 0.9037 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2032419e-01 1.8952590e-01 2.2139420e-01] Sparsity at: 0.013428782188841202 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1993960e-01 1.8951701e-01 2.2126244e-01] Sparsity at: 0.013428782188841202 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8099 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1996902e-01 1.8955404e-01 2.2135922e-01] Sparsity at: 0.013428782188841202 Epoch 208/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8102 - val_accuracy: 0.9033 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2024183e-01 1.8933730e-01 2.2148785e-01] Sparsity at: 0.013428782188841202 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8097 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1982183e-01 1.8909380e-01 2.2116485e-01] Sparsity at: 0.013428782188841202 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8093 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2034442e-01 1.8926063e-01 2.2132108e-01] Sparsity at: 0.013428782188841202 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2000444e-01 1.8945417e-01 2.2129019e-01] Sparsity at: 0.013428782188841202 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1975483e-01 1.8926625e-01 2.2077030e-01] Sparsity at: 0.013428782188841202 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8102 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2026406e-01 1.8967707e-01 2.2103231e-01] Sparsity at: 0.013428782188841202 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2012763e-01 1.8941410e-01 2.2160186e-01] Sparsity at: 0.013428782188841202 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2002825e-01 1.8949190e-01 2.2139734e-01] Sparsity at: 0.013428782188841202 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2003846e-01 1.8954104e-01 2.2115301e-01] Sparsity at: 0.013428782188841202 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8097 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1982609e-01 1.8934388e-01 2.2109407e-01] Sparsity at: 0.013428782188841202 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1990599e-01 1.8940960e-01 2.2070596e-01] Sparsity at: 0.013428782188841202 Epoch 219/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1975961e-01 1.8911813e-01 2.2066469e-01] Sparsity at: 0.013428782188841202 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9019 - val_loss: 0.8100 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1972491e-01 1.8902649e-01 2.2069360e-01] Sparsity at: 0.013428782188841202 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1978401e-01 1.8921059e-01 2.2059932e-01] Sparsity at: 0.013428782188841202 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2003371e-01 1.8899971e-01 2.2095919e-01] Sparsity at: 0.013428782188841202 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2011574e-01 1.8926242e-01 2.2094275e-01] Sparsity at: 0.013428782188841202 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1992840e-01 1.8944018e-01 2.2096606e-01] Sparsity at: 0.013428782188841202 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1990557e-01 1.8933024e-01 2.2125554e-01] Sparsity at: 0.013428782188841202 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1993418e-01 1.8894550e-01 2.2077751e-01] Sparsity at: 0.013428782188841202 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8092 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2018702e-01 1.8927360e-01 2.2097670e-01] Sparsity at: 0.013428782188841202 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1976155e-01 1.8915358e-01 2.2078781e-01] Sparsity at: 0.013428782188841202 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8085 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1946758e-01 1.8882763e-01 2.2063670e-01] Sparsity at: 0.013428782188841202 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1981329e-01 1.8903263e-01 2.2096179e-01] Sparsity at: 0.013428782188841202 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1982016e-01 1.8895155e-01 2.2080916e-01] Sparsity at: 0.013428782188841202 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8083 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2006185e-01 1.8934600e-01 2.2100881e-01] Sparsity at: 0.013428782188841202 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1985021e-01 1.8926944e-01 2.2117533e-01] Sparsity at: 0.013428782188841202 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1961419e-01 1.8880652e-01 2.2113413e-01] Sparsity at: 0.013428782188841202 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8094 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1981913e-01 1.8897901e-01 2.2074836e-01] Sparsity at: 0.013428782188841202 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8092 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1940784e-01 1.8899132e-01 2.2038297e-01] Sparsity at: 0.013428782188841202 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1976997e-01 1.8944161e-01 2.2049545e-01] Sparsity at: 0.013428782188841202 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8094 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1985760e-01 1.8924576e-01 2.2101867e-01] Sparsity at: 0.013428782188841202 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9056 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2017215e-01 1.8924311e-01 2.2069940e-01] Sparsity at: 0.013428782188841202 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1999571e-01 1.8924224e-01 2.2092319e-01] Sparsity at: 0.013428782188841202 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8084 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1947457e-01 1.8885359e-01 2.2084881e-01] Sparsity at: 0.013428782188841202 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8087 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1983674e-01 1.8916094e-01 2.2093205e-01] Sparsity at: 0.013428782188841202 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8096 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1974470e-01 1.8902208e-01 2.2121526e-01] Sparsity at: 0.013428782188841202 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1971346e-01 1.8860355e-01 2.2091362e-01] Sparsity at: 0.013428782188841202 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1984121e-01 1.8867676e-01 2.2060543e-01] Sparsity at: 0.013428782188841202 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1975967e-01 1.8881582e-01 2.2061694e-01] Sparsity at: 0.013428782188841202 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9025 - val_loss: 0.8097 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1961404e-01 1.8900225e-01 2.2094241e-01] Sparsity at: 0.013428782188841202 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1992327e-01 1.8928017e-01 2.2077848e-01] Sparsity at: 0.013428782188841202 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1961409e-01 1.8928488e-01 2.2082821e-01] Sparsity at: 0.013428782188841202 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1970195e-01 1.8936919e-01 2.2095348e-01] Sparsity at: 0.013428782188841202 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.03561964924113026 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.08273246205477491 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.22271870241878666 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 50s 7ms/step - loss: 0.8251 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1944305e-01 1.8890239e-01 2.2072834e-01] Sparsity at: 0.013428782188841202 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8094 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1971230e-01 1.8894389e-01 2.2091855e-01] Sparsity at: 0.013428782188841202 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1995465e-01 1.8915661e-01 2.2078009e-01] Sparsity at: 0.013428782188841202 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2003731e-01 1.8941976e-01 2.2064103e-01] Sparsity at: 0.013428782188841202 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9025 - val_loss: 0.8089 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1999148e-01 1.8918093e-01 2.2080322e-01] Sparsity at: 0.013428782188841202 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2024502e-01 1.8942998e-01 2.2115825e-01] Sparsity at: 0.013428782188841202 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8083 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2005776e-01 1.8902192e-01 2.2104517e-01] Sparsity at: 0.013428782188841202 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9025 - val_loss: 0.8082 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2010730e-01 1.8934314e-01 2.2093843e-01] Sparsity at: 0.013428782188841202 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2015008e-01 1.8932320e-01 2.2075191e-01] Sparsity at: 0.013428782188841202 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9020 - val_loss: 0.8098 - val_accuracy: 0.9036 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1996781e-01 1.8898408e-01 2.2074127e-01] Sparsity at: 0.013428782188841202 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8095 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1936001e-01 1.8894596e-01 2.2044410e-01] Sparsity at: 0.013428782188841202 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2014165e-01 1.8896648e-01 2.2104502e-01] Sparsity at: 0.013428782188841202 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8095 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1975105e-01 1.8906645e-01 2.2088400e-01] Sparsity at: 0.013428782188841202 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8082 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1991175e-01 1.8924665e-01 2.2048570e-01] Sparsity at: 0.013428782188841202 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8098 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1969028e-01 1.8894884e-01 2.2032125e-01] Sparsity at: 0.013428782188841202 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8097 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1978229e-01 1.8906568e-01 2.2051747e-01] Sparsity at: 0.013428782188841202 Epoch 267/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2044790e-01 1.8940170e-01 2.2084177e-01] Sparsity at: 0.013428782188841202 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8251 - accuracy: 0.9020 - val_loss: 0.8089 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1980360e-01 1.8937655e-01 2.2039616e-01] Sparsity at: 0.013428782188841202 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1999250e-01 1.8929774e-01 2.2053701e-01] Sparsity at: 0.013428782188841202 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8089 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1982609e-01 1.8892100e-01 2.2075731e-01] Sparsity at: 0.013428782188841202 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1993777e-01 1.8916160e-01 2.2046846e-01] Sparsity at: 0.013428782188841202 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9018 - val_loss: 0.8085 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1989323e-01 1.8857199e-01 2.2065207e-01] Sparsity at: 0.013428782188841202 Epoch 273/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8093 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1989933e-01 1.8869862e-01 2.2068991e-01] Sparsity at: 0.013428782188841202 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8085 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1978649e-01 1.8846150e-01 2.2023103e-01] Sparsity at: 0.013428782188841202 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1955009e-01 1.8851633e-01 2.2064289e-01] Sparsity at: 0.013428782188841202 Epoch 276/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1995509e-01 1.8889435e-01 2.2068359e-01] Sparsity at: 0.013428782188841202 Epoch 277/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8250 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1974617e-01 1.8850484e-01 2.2067928e-01] Sparsity at: 0.013428782188841202 Epoch 278/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1980377e-01 1.8845768e-01 2.2053728e-01] Sparsity at: 0.013428782188841202 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9027 - val_loss: 0.8089 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2040485e-01 1.8894394e-01 2.2094204e-01] Sparsity at: 0.013428782188841202 Epoch 280/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9018 - val_loss: 0.8090 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1973167e-01 1.8877593e-01 2.2041328e-01] Sparsity at: 0.013428782188841202 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9020 - val_loss: 0.8084 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2007392e-01 1.8859774e-01 2.2037424e-01] Sparsity at: 0.013428782188841202 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8097 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1958351e-01 1.8878685e-01 2.2049147e-01] Sparsity at: 0.013428782188841202 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8082 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2003034e-01 1.8873803e-01 2.2077684e-01] Sparsity at: 0.013428782188841202 Epoch 284/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8084 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1995902e-01 1.8875501e-01 2.2049251e-01] Sparsity at: 0.013428782188841202 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8084 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2002561e-01 1.8895823e-01 2.2036865e-01] Sparsity at: 0.013428782188841202 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8094 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1967314e-01 1.8875374e-01 2.2047907e-01] Sparsity at: 0.013428782188841202 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8090 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1962282e-01 1.8867871e-01 2.2060379e-01] Sparsity at: 0.013428782188841202 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8093 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2000699e-01 1.8883280e-01 2.2047977e-01] Sparsity at: 0.013428782188841202 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1982931e-01 1.8866557e-01 2.2004627e-01] Sparsity at: 0.013428782188841202 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8083 - val_accuracy: 0.9056 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1992235e-01 1.8886524e-01 2.2034410e-01] Sparsity at: 0.013428782188841202 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2017328e-01 1.8899363e-01 2.2058488e-01] Sparsity at: 0.013428782188841202 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.2001714e-01 1.8899842e-01 2.2081670e-01] Sparsity at: 0.013428782188841202 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8101 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1966176e-01 1.8870482e-01 2.2087073e-01] Sparsity at: 0.013428782188841202 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9024 - val_loss: 0.8090 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1971893e-01 1.8835396e-01 2.2054780e-01] Sparsity at: 0.013428782188841202 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1978314e-01 1.8862723e-01 2.2063772e-01] Sparsity at: 0.013428782188841202 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8087 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1977782e-01 1.8836731e-01 2.2046098e-01] Sparsity at: 0.013428782188841202 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1943246e-01 1.8831220e-01 2.2001575e-01] Sparsity at: 0.013428782188841202 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1961206e-01 1.8846945e-01 2.2028606e-01] Sparsity at: 0.013428782188841202 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9027 - val_loss: 0.8088 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1948543e-01 1.8816493e-01 2.2012886e-01] Sparsity at: 0.013428782188841202 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1941717e-01 1.8855116e-01 2.2045572e-01] Sparsity at: 0.013428782188841202 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.04380975309229651 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.09488863894561472 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.24104228113534631 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8082 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1981218e-01 1.8809834e-01 2.2056501e-01] Sparsity at: 0.013428782188841202 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1970063e-01 1.8824397e-01 2.2004765e-01] Sparsity at: 0.013428782188841202 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1942891e-01 1.8819499e-01 2.1975784e-01] Sparsity at: 0.013428782188841202 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1962026e-01 1.8837743e-01 2.2035764e-01] Sparsity at: 0.013428782188841202 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1962148e-01 1.8864402e-01 2.2012231e-01] Sparsity at: 0.013428782188841202 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9059 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1954399e-01 1.8865621e-01 2.2016093e-01] Sparsity at: 0.013428782188841202 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1951197e-01 1.8851759e-01 2.2031657e-01] Sparsity at: 0.013428782188841202 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1963798e-01 1.8845934e-01 2.2026348e-01] Sparsity at: 0.013428782188841202 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1918280e-01 1.8826146e-01 2.2011593e-01] Sparsity at: 0.013428782188841202 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1923429e-01 1.8819202e-01 2.1978363e-01] Sparsity at: 0.013428782188841202 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8251 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1918496e-01 1.8802394e-01 2.1977438e-01] Sparsity at: 0.013428782188841202 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1937272e-01 1.8805118e-01 2.2004890e-01] Sparsity at: 0.013428782188841202 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1893659e-01 1.8767448e-01 2.1993987e-01] Sparsity at: 0.013428782188841202 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1898493e-01 1.8796663e-01 2.2003047e-01] Sparsity at: 0.013428782188841202 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1879031e-01 1.8808232e-01 2.1971251e-01] Sparsity at: 0.013428782188841202 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1958262e-01 1.8817119e-01 2.1969901e-01] Sparsity at: 0.013428782188841202 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9018 - val_loss: 0.8091 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1930464e-01 1.8790632e-01 2.1954504e-01] Sparsity at: 0.013428782188841202 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1920796e-01 1.8794324e-01 2.1977848e-01] Sparsity at: 0.013428782188841202 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9055 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1889052e-01 1.8765022e-01 2.1949656e-01] Sparsity at: 0.013428782188841202 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8077 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1896493e-01 1.8770143e-01 2.1979393e-01] Sparsity at: 0.013428782188841202 Epoch 321/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8087 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1936275e-01 1.8801986e-01 2.1980606e-01] Sparsity at: 0.013428782188841202 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1930154e-01 1.8776955e-01 2.2003715e-01] Sparsity at: 0.013428782188841202 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1892338e-01 1.8778042e-01 2.2004178e-01] Sparsity at: 0.013428782188841202 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1938255e-01 1.8786441e-01 2.1937868e-01] Sparsity at: 0.013428782188841202 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8090 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1920905e-01 1.8777835e-01 2.1956690e-01] Sparsity at: 0.013428782188841202 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8096 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1967995e-01 1.8786703e-01 2.2005415e-01] Sparsity at: 0.013428782188841202 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9019 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1925557e-01 1.8782739e-01 2.1988761e-01] Sparsity at: 0.013428782188841202 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8096 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1921463e-01 1.8809320e-01 2.2023028e-01] Sparsity at: 0.013428782188841202 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1970180e-01 1.8812802e-01 2.2003673e-01] Sparsity at: 0.013428782188841202 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1947920e-01 1.8790688e-01 2.1954072e-01] Sparsity at: 0.013428782188841202 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8086 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1956597e-01 1.8830946e-01 2.1946670e-01] Sparsity at: 0.013428782188841202 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1891636e-01 1.8776880e-01 2.1961860e-01] Sparsity at: 0.013428782188841202 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8080 - val_accuracy: 0.9056 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1892539e-01 1.8730499e-01 2.1979678e-01] Sparsity at: 0.013428782188841202 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8083 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1926357e-01 1.8749920e-01 2.1957660e-01] Sparsity at: 0.013428782188841202 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1839365e-01 1.8742250e-01 2.1913566e-01] Sparsity at: 0.013428782188841202 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1844718e-01 1.8730378e-01 2.1882646e-01] Sparsity at: 0.013428782188841202 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8098 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1850280e-01 1.8779813e-01 2.1905443e-01] Sparsity at: 0.013428782188841202 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9018 - val_loss: 0.8087 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1878502e-01 1.8743201e-01 2.1912998e-01] Sparsity at: 0.013428782188841202 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8089 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1896920e-01 1.8774855e-01 2.1915790e-01] Sparsity at: 0.013428782188841202 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1877247e-01 1.8763603e-01 2.1909802e-01] Sparsity at: 0.013428782188841202 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1886037e-01 1.8750276e-01 2.1934691e-01] Sparsity at: 0.013428782188841202 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1875484e-01 1.8760169e-01 2.1936096e-01] Sparsity at: 0.013428782188841202 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1870136e-01 1.8755350e-01 2.1922807e-01] Sparsity at: 0.013428782188841202 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8250 - accuracy: 0.9015 - val_loss: 0.8093 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1872808e-01 1.8740493e-01 2.1914642e-01] Sparsity at: 0.013428782188841202 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8079 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1838789e-01 1.8700802e-01 2.1920450e-01] Sparsity at: 0.013428782188841202 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8091 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1843718e-01 1.8718612e-01 2.1867914e-01] Sparsity at: 0.013428782188841202 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9028 - val_loss: 0.8088 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1855341e-01 1.8758440e-01 2.1881008e-01] Sparsity at: 0.013428782188841202 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1839955e-01 1.8776670e-01 2.1837036e-01] Sparsity at: 0.013428782188841202 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9019 - val_loss: 0.8090 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1807794e-01 1.8716247e-01 2.1863133e-01] Sparsity at: 0.013428782188841202 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1838747e-01 1.8745445e-01 2.1902086e-01] Sparsity at: 0.013428782188841202 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.051436077562252436 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.10767383222726767 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.2567379317195666 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8090 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1762061e-01 1.8722188e-01 2.1845147e-01] Sparsity at: 0.013428782188841202 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1795852e-01 1.8698703e-01 2.1855639e-01] Sparsity at: 0.013428782188841202 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1778373e-01 1.8713641e-01 2.1825841e-01] Sparsity at: 0.013428782188841202 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1780285e-01 1.8710981e-01 2.1813346e-01] Sparsity at: 0.013428782188841202 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8090 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1739219e-01 1.8684207e-01 2.1795422e-01] Sparsity at: 0.013428782188841202 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8094 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1691717e-01 1.8699683e-01 2.1788156e-01] Sparsity at: 0.013428782188841202 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9026 - val_loss: 0.8086 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1703675e-01 1.8723828e-01 2.1785863e-01] Sparsity at: 0.013428782188841202 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8090 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1659151e-01 1.8694058e-01 2.1751326e-01] Sparsity at: 0.013428782188841202 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8100 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1631376e-01 1.8712033e-01 2.1733025e-01] Sparsity at: 0.013428782188841202 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1624607e-01 1.8718956e-01 2.1734606e-01] Sparsity at: 0.013428782188841202 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8094 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1562153e-01 1.8695483e-01 2.1711177e-01] Sparsity at: 0.013428782188841202 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1565640e-01 1.8720305e-01 2.1710661e-01] Sparsity at: 0.013428782188841202 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1579705e-01 1.8675593e-01 2.1716076e-01] Sparsity at: 0.013428782188841202 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8098 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1566996e-01 1.8710071e-01 2.1692102e-01] Sparsity at: 0.013428782188841202 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1560460e-01 1.8692438e-01 2.1658406e-01] Sparsity at: 0.013428782188841202 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1508086e-01 1.8690562e-01 2.1649967e-01] Sparsity at: 0.013428782188841202 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1526821e-01 1.8685026e-01 2.1653479e-01] Sparsity at: 0.013428782188841202 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9021 - val_loss: 0.8097 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1479852e-01 1.8666197e-01 2.1603902e-01] Sparsity at: 0.013428782188841202 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1515261e-01 1.8695730e-01 2.1645203e-01] Sparsity at: 0.013428782188841202 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8089 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1536201e-01 1.8722501e-01 2.1659574e-01] Sparsity at: 0.013428782188841202 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8085 - val_accuracy: 0.9055 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1509950e-01 1.8701999e-01 2.1654318e-01] Sparsity at: 0.013428782188841202 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8097 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1504855e-01 1.8705827e-01 2.1629865e-01] Sparsity at: 0.013428782188841202 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1532209e-01 1.8720311e-01 2.1683164e-01] Sparsity at: 0.013428782188841202 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8091 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1509168e-01 1.8711156e-01 2.1657753e-01] Sparsity at: 0.013428782188841202 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9055 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1515152e-01 1.8680337e-01 2.1644086e-01] Sparsity at: 0.013428782188841202 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1557193e-01 1.8671665e-01 2.1663509e-01] Sparsity at: 0.013428782188841202 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8086 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1571282e-01 1.8711688e-01 2.1710549e-01] Sparsity at: 0.013428782188841202 Epoch 378/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9055 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1574858e-01 1.8710266e-01 2.1694203e-01] Sparsity at: 0.013428782188841202 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1533662e-01 1.8650608e-01 2.1673575e-01] Sparsity at: 0.013428782188841202 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8089 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1520686e-01 1.8666007e-01 2.1682781e-01] Sparsity at: 0.013428782188841202 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1525373e-01 1.8650870e-01 2.1661696e-01] Sparsity at: 0.013428782188841202 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9020 - val_loss: 0.8094 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1549810e-01 1.8662596e-01 2.1670234e-01] Sparsity at: 0.013428782188841202 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8086 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1549864e-01 1.8651287e-01 2.1687245e-01] Sparsity at: 0.013428782188841202 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9020 - val_loss: 0.8083 - val_accuracy: 0.9055 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1538508e-01 1.8685134e-01 2.1677396e-01] Sparsity at: 0.013428782188841202 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8079 - val_accuracy: 0.9058 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1524662e-01 1.8669352e-01 2.1684790e-01] Sparsity at: 0.013428782188841202 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1578360e-01 1.8700577e-01 2.1689986e-01] Sparsity at: 0.013428782188841202 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8079 - val_accuracy: 0.9055 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1582144e-01 1.8692987e-01 2.1651964e-01] Sparsity at: 0.013428782188841202 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1581095e-01 1.8718390e-01 2.1659426e-01] Sparsity at: 0.013428782188841202 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8098 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1532865e-01 1.8711269e-01 2.1631406e-01] Sparsity at: 0.013428782188841202 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8103 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1566254e-01 1.8709457e-01 2.1627435e-01] Sparsity at: 0.013428782188841202 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1572262e-01 1.8681280e-01 2.1686384e-01] Sparsity at: 0.013428782188841202 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1519797e-01 1.8632090e-01 2.1688895e-01] Sparsity at: 0.013428782188841202 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9023 - val_loss: 0.8081 - val_accuracy: 0.9058 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1519440e-01 1.8651238e-01 2.1680224e-01] Sparsity at: 0.013428782188841202 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9025 - val_loss: 0.8093 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1543199e-01 1.8653014e-01 2.1687053e-01] Sparsity at: 0.013428782188841202 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9022 - val_loss: 0.8080 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1538907e-01 1.8680707e-01 2.1705277e-01] Sparsity at: 0.013428782188841202 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8085 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1556865e-01 1.8625399e-01 2.1694416e-01] Sparsity at: 0.013428782188841202 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8243 - accuracy: 0.9024 - val_loss: 0.8080 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1569505e-01 1.8687463e-01 2.1671921e-01] Sparsity at: 0.013428782188841202 Epoch 398/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1523504e-01 1.8677776e-01 2.1690390e-01] Sparsity at: 0.013428782188841202 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1564639e-01 1.8691644e-01 2.1695516e-01] Sparsity at: 0.013428782188841202 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8091 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1591075e-01 1.8681724e-01 2.1719533e-01] Sparsity at: 0.013428782188841202 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.05637868867256124 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.11695918551944917 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.2671557808276752 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8091 - val_accuracy: 0.9059 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1557805e-01 1.8664294e-01 2.1697123e-01] Sparsity at: 0.013428782188841202 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1613102e-01 1.8688075e-01 2.1711615e-01] Sparsity at: 0.013428782188841202 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9019 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1560346e-01 1.8662621e-01 2.1711856e-01] Sparsity at: 0.013428782188841202 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8087 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1562442e-01 1.8632098e-01 2.1698421e-01] Sparsity at: 0.013428782188841202 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1617462e-01 1.8687001e-01 2.1766649e-01] Sparsity at: 0.013428782188841202 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1549730e-01 1.8671276e-01 2.1710916e-01] Sparsity at: 0.013428782188841202 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.9024 - val_loss: 0.8098 - val_accuracy: 0.9041 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1582660e-01 1.8661918e-01 2.1705933e-01] Sparsity at: 0.013428782188841202 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9019 - val_loss: 0.8089 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1610625e-01 1.8632816e-01 2.1686175e-01] Sparsity at: 0.013428782188841202 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8091 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1583025e-01 1.8646628e-01 2.1669018e-01] Sparsity at: 0.013428782188841202 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1596757e-01 1.8638910e-01 2.1687196e-01] Sparsity at: 0.013428782188841202 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1576862e-01 1.8638015e-01 2.1685511e-01] Sparsity at: 0.013428782188841202 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8083 - val_accuracy: 0.9053 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1658617e-01 1.8655561e-01 2.1718721e-01] Sparsity at: 0.013428782188841202 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1634474e-01 1.8650711e-01 2.1704403e-01] Sparsity at: 0.013428782188841202 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8243 - accuracy: 0.9024 - val_loss: 0.8084 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1664622e-01 1.8680727e-01 2.1743231e-01] Sparsity at: 0.013428782188841202 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1658838e-01 1.8684055e-01 2.1778204e-01] Sparsity at: 0.013428782188841202 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1636407e-01 1.8660708e-01 2.1740048e-01] Sparsity at: 0.013428782188841202 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8082 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1663457e-01 1.8661863e-01 2.1753581e-01] Sparsity at: 0.013428782188841202 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9027 - val_loss: 0.8088 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1630764e-01 1.8644385e-01 2.1723606e-01] Sparsity at: 0.013428782188841202 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8087 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1685036e-01 1.8649296e-01 2.1729586e-01] Sparsity at: 0.013428782188841202 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1635532e-01 1.8638326e-01 2.1745746e-01] Sparsity at: 0.013428782188841202 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8250 - accuracy: 0.9019 - val_loss: 0.8094 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1588612e-01 1.8628250e-01 2.1753040e-01] Sparsity at: 0.013428782188841202 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1637550e-01 1.8641600e-01 2.1715343e-01] Sparsity at: 0.013428782188841202 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1636909e-01 1.8668395e-01 2.1716903e-01] Sparsity at: 0.013428782188841202 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9027 - val_loss: 0.8079 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1653786e-01 1.8670361e-01 2.1728393e-01] Sparsity at: 0.013428782188841202 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8086 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1656689e-01 1.8683933e-01 2.1723074e-01] Sparsity at: 0.013428782188841202 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9028 - val_loss: 0.8084 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1640152e-01 1.8693578e-01 2.1722770e-01] Sparsity at: 0.013428782188841202 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9027 - val_loss: 0.8090 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1616691e-01 1.8696913e-01 2.1724266e-01] Sparsity at: 0.013428782188841202 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1642646e-01 1.8646422e-01 2.1708453e-01] Sparsity at: 0.013428782188841202 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8090 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1636097e-01 1.8656762e-01 2.1711488e-01] Sparsity at: 0.013428782188841202 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1690458e-01 1.8655169e-01 2.1790306e-01] Sparsity at: 0.013428782188841202 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8090 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1664286e-01 1.8670195e-01 2.1740584e-01] Sparsity at: 0.013428782188841202 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1663997e-01 1.8681528e-01 2.1720389e-01] Sparsity at: 0.013428782188841202 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8092 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1596566e-01 1.8622647e-01 2.1702780e-01] Sparsity at: 0.013428782188841202 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9019 - val_loss: 0.8090 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1611071e-01 1.8618940e-01 2.1699849e-01] Sparsity at: 0.013428782188841202 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1618146e-01 1.8553138e-01 2.1677530e-01] Sparsity at: 0.013428782188841202 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8096 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1629345e-01 1.8663336e-01 2.1733594e-01] Sparsity at: 0.013428782188841202 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8249 - accuracy: 0.9020 - val_loss: 0.8101 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1647087e-01 1.8623435e-01 2.1710762e-01] Sparsity at: 0.013428782188841202 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9022 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1686602e-01 1.8661262e-01 2.1744506e-01] Sparsity at: 0.013428782188841202 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1698961e-01 1.8651502e-01 2.1710502e-01] Sparsity at: 0.013428782188841202 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1692415e-01 1.8651094e-01 2.1725184e-01] Sparsity at: 0.013428782188841202 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9028 - val_loss: 0.8085 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1642426e-01 1.8615086e-01 2.1722904e-01] Sparsity at: 0.013428782188841202 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9022 - val_loss: 0.8092 - val_accuracy: 0.9052 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1682362e-01 1.8655106e-01 2.1752010e-01] Sparsity at: 0.013428782188841202 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8095 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1684821e-01 1.8663618e-01 2.1752967e-01] Sparsity at: 0.013428782188841202 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8079 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1694435e-01 1.8643752e-01 2.1728410e-01] Sparsity at: 0.013428782188841202 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9019 - val_loss: 0.8096 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1710083e-01 1.8689947e-01 2.1720956e-01] Sparsity at: 0.013428782188841202 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9020 - val_loss: 0.8087 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1667975e-01 1.8683153e-01 2.1732639e-01] Sparsity at: 0.013428782188841202 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1670364e-01 1.8689214e-01 2.1738324e-01] Sparsity at: 0.013428782188841202 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8093 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1665311e-01 1.8662186e-01 2.1709421e-01] Sparsity at: 0.013428782188841202 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1704017e-01 1.8652424e-01 2.1718226e-01] Sparsity at: 0.013428782188841202 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8088 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1707648e-01 1.8667175e-01 2.1736464e-01] Sparsity at: 0.013428782188841202 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8241 - accuracy: 0.9024 - val_loss: 0.8085 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1758030e-01 1.8700248e-01 2.1759413e-01] Sparsity at: 0.013428782188841202 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8088 - val_accuracy: 0.9042 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1772984e-01 1.8706839e-01 2.1777911e-01] Sparsity at: 0.013428782188841202 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8087 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1747126e-01 1.8684459e-01 2.1774939e-01] Sparsity at: 0.013428782188841202 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8092 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1711551e-01 1.8656528e-01 2.1771207e-01] Sparsity at: 0.013428782188841202 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8248 - accuracy: 0.9016 - val_loss: 0.8094 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1726687e-01 1.8659027e-01 2.1774685e-01] Sparsity at: 0.013428782188841202 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8084 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1754271e-01 1.8664283e-01 2.1808924e-01] Sparsity at: 0.013428782188841202 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8091 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1767873e-01 1.8667029e-01 2.1820119e-01] Sparsity at: 0.013428782188841202 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9022 - val_loss: 0.8094 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1763179e-01 1.8657953e-01 2.1794692e-01] Sparsity at: 0.013428782188841202 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1752886e-01 1.8664946e-01 2.1757913e-01] Sparsity at: 0.013428782188841202 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8093 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1752773e-01 1.8638298e-01 2.1811888e-01] Sparsity at: 0.013428782188841202 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8078 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1822433e-01 1.8663824e-01 2.1787530e-01] Sparsity at: 0.013428782188841202 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8082 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1799141e-01 1.8672135e-01 2.1765782e-01] Sparsity at: 0.013428782188841202 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9025 - val_loss: 0.8092 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1805352e-01 1.8695231e-01 2.1779819e-01] Sparsity at: 0.013428782188841202 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8098 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1760605e-01 1.8655524e-01 2.1751258e-01] Sparsity at: 0.013428782188841202 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9026 - val_loss: 0.8090 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1770091e-01 1.8704477e-01 2.1735442e-01] Sparsity at: 0.013428782188841202 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1807921e-01 1.8662696e-01 2.1793881e-01] Sparsity at: 0.013428782188841202 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8243 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1764575e-01 1.8654934e-01 2.1735848e-01] Sparsity at: 0.013428782188841202 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8093 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1792762e-01 1.8670154e-01 2.1753275e-01] Sparsity at: 0.013428782188841202 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1772878e-01 1.8667828e-01 2.1710508e-01] Sparsity at: 0.013428782188841202 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1742488e-01 1.8692984e-01 2.1705100e-01] Sparsity at: 0.013428782188841202 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1808857e-01 1.8688983e-01 2.1764354e-01] Sparsity at: 0.013428782188841202 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8242 - accuracy: 0.9024 - val_loss: 0.8087 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1777602e-01 1.8694788e-01 2.1726358e-01] Sparsity at: 0.013428782188841202 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9022 - val_loss: 0.8086 - val_accuracy: 0.9054 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1805546e-01 1.8654378e-01 2.1789116e-01] Sparsity at: 0.013428782188841202 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9020 - val_loss: 0.8088 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1793267e-01 1.8670490e-01 2.1750720e-01] Sparsity at: 0.013428782188841202 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8094 - val_accuracy: 0.9046 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1758056e-01 1.8652563e-01 2.1760991e-01] Sparsity at: 0.013428782188841202 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8247 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1766305e-01 1.8657550e-01 2.1771425e-01] Sparsity at: 0.013428782188841202 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1758491e-01 1.8622246e-01 2.1719523e-01] Sparsity at: 0.013428782188841202 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9021 - val_loss: 0.8086 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1776079e-01 1.8657561e-01 2.1756572e-01] Sparsity at: 0.013428782188841202 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8084 - val_accuracy: 0.9049 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1797785e-01 1.8707438e-01 2.1718381e-01] Sparsity at: 0.013428782188841202 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9021 - val_loss: 0.8092 - val_accuracy: 0.9043 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1812248e-01 1.8692477e-01 2.1726820e-01] Sparsity at: 0.013428782188841202 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9024 - val_loss: 0.8089 - val_accuracy: 0.9044 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1833940e-01 1.8714640e-01 2.1762131e-01] Sparsity at: 0.013428782188841202 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9022 - val_loss: 0.8083 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1787642e-01 1.8671899e-01 2.1737672e-01] Sparsity at: 0.013428782188841202 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9020 - val_loss: 0.8091 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1797058e-01 1.8697110e-01 2.1743509e-01] Sparsity at: 0.013428782188841202 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9023 - val_loss: 0.8086 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1786638e-01 1.8683952e-01 2.1755375e-01] Sparsity at: 0.013428782188841202 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9021 - val_loss: 0.8090 - val_accuracy: 0.9040 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1835682e-01 1.8697698e-01 2.1754761e-01] Sparsity at: 0.013428782188841202 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9025 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1776596e-01 1.8684800e-01 2.1769130e-01] Sparsity at: 0.013428782188841202 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9018 - val_loss: 0.8085 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1783997e-01 1.8682839e-01 2.1784271e-01] Sparsity at: 0.013428782188841202 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8241 - accuracy: 0.9025 - val_loss: 0.8087 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1824314e-01 1.8728532e-01 2.1800563e-01] Sparsity at: 0.013428782188841202 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9025 - val_loss: 0.8090 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1780233e-01 1.8671966e-01 2.1759672e-01] Sparsity at: 0.013428782188841202 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8089 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1768540e-01 1.8665405e-01 2.1762733e-01] Sparsity at: 0.013428782188841202 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9051 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1723703e-01 1.8621251e-01 2.1688876e-01] Sparsity at: 0.013428782188841202 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8244 - accuracy: 0.9024 - val_loss: 0.8080 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1782574e-01 1.8633138e-01 2.1738504e-01] Sparsity at: 0.013428782188841202 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8243 - accuracy: 0.9019 - val_loss: 0.8084 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1816485e-01 1.8680918e-01 2.1727230e-01] Sparsity at: 0.013428782188841202 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8093 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1760714e-01 1.8667766e-01 2.1769615e-01] Sparsity at: 0.013428782188841202 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9020 - val_loss: 0.8093 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1754229e-01 1.8643820e-01 2.1771030e-01] Sparsity at: 0.013428782188841202 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8246 - accuracy: 0.9021 - val_loss: 0.8088 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1779652e-01 1.8645643e-01 2.1758531e-01] Sparsity at: 0.013428782188841202 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9024 - val_loss: 0.8086 - val_accuracy: 0.9045 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1773438e-01 1.8657161e-01 2.1738376e-01] Sparsity at: 0.013428782188841202 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.9023 - val_loss: 0.8085 - val_accuracy: 0.9048 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1786505e-01 1.8674621e-01 2.1755655e-01] Sparsity at: 0.013428782188841202 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8248 - accuracy: 0.9021 - val_loss: 0.8084 - val_accuracy: 0.9047 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1714009e-01 1.8602277e-01 2.1740323e-01] Sparsity at: 0.013428782188841202 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8245 - accuracy: 0.9021 - val_loss: 0.8083 - val_accuracy: 0.9050 [ 3.1731274e-34 5.2851388e-34 -4.8786629e-34 ... -2.1743812e-01 1.8628700e-01 2.1714284e-01] Sparsity at: 0.013428782188841202 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.042179541662335396 Thresholhold -0.05633559077978134 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08939405530691147 Thresholhold 0.003573372960090637 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10492659732699394 Thresholhold 0.13731814920902252 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 1:00:01 - loss: 2.3651 - accuracy: 0.0430WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_begin` time: 2.5044s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 0.4918 - accuracy: 0.8660 - val_loss: 0.2549 - val_accuracy: 0.9281 [-0.05633559 0.06754186 0.022034 ... -0.26633793 -0.23490816 0.21950084] Sparsity at: 0.013428782188841202 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2297 - accuracy: 0.9337 - val_loss: 0.1887 - val_accuracy: 0.9439 [-0.05633559 0.06754186 0.022034 ... -0.28480592 -0.25065205 0.25275692] Sparsity at: 0.013428782188841202 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1725 - accuracy: 0.9501 - val_loss: 0.1548 - val_accuracy: 0.9537 [-0.05633559 0.06754186 0.022034 ... -0.29795736 -0.25991264 0.28055167] Sparsity at: 0.013428782188841202 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1378 - accuracy: 0.9598 - val_loss: 0.1351 - val_accuracy: 0.9585 [-0.05633559 0.06754186 0.022034 ... -0.3078569 -0.26523432 0.302555 ] Sparsity at: 0.013428782188841202 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1140 - accuracy: 0.9670 - val_loss: 0.1227 - val_accuracy: 0.9609 [-0.05633559 0.06754186 0.022034 ... -0.31615216 -0.26896536 0.32104367] Sparsity at: 0.013428782188841202 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0963 - accuracy: 0.9719 - val_loss: 0.1135 - val_accuracy: 0.9634 [-0.05633559 0.06754186 0.022034 ... -0.323512 -0.2708599 0.33693382] Sparsity at: 0.013428782188841202 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0825 - accuracy: 0.9762 - val_loss: 0.1089 - val_accuracy: 0.9648 [-0.05633559 0.06754186 0.022034 ... -0.33014584 -0.2723221 0.35208684] Sparsity at: 0.013428782188841202 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0713 - accuracy: 0.9793 - val_loss: 0.1050 - val_accuracy: 0.9665 [-0.05633559 0.06754186 0.022034 ... -0.33716258 -0.27280957 0.36541376] Sparsity at: 0.013428782188841202 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0616 - accuracy: 0.9823 - val_loss: 0.1020 - val_accuracy: 0.9679 [-0.05633559 0.06754186 0.022034 ... -0.343987 -0.27344334 0.3783662 ] Sparsity at: 0.013428782188841202 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0536 - accuracy: 0.9846 - val_loss: 0.1005 - val_accuracy: 0.9697 [-0.05633559 0.06754186 0.022034 ... -0.35165486 -0.27456674 0.391246 ] Sparsity at: 0.013428782188841202 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0470 - accuracy: 0.9870 - val_loss: 0.0992 - val_accuracy: 0.9699 [-0.05633559 0.06754186 0.022034 ... -0.36039373 -0.2756228 0.40290472] Sparsity at: 0.013428782188841202 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0409 - accuracy: 0.9891 - val_loss: 0.1006 - val_accuracy: 0.9697 [-0.05633559 0.06754186 0.022034 ... -0.3685916 -0.2765783 0.41513744] Sparsity at: 0.013428782188841202 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0356 - accuracy: 0.9908 - val_loss: 0.1006 - val_accuracy: 0.9704 [-0.05633559 0.06754186 0.022034 ... -0.3777779 -0.27748364 0.42662534] Sparsity at: 0.013428782188841202 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0308 - accuracy: 0.9922 - val_loss: 0.1018 - val_accuracy: 0.9705 [-0.05633559 0.06754186 0.022034 ... -0.38673005 -0.2786867 0.43802193] Sparsity at: 0.013428782188841202 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0266 - accuracy: 0.9937 - val_loss: 0.1029 - val_accuracy: 0.9715 [-0.05633559 0.06754186 0.022034 ... -0.3944127 -0.27941284 0.44916144] Sparsity at: 0.013428782188841202 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0229 - accuracy: 0.9949 - val_loss: 0.1040 - val_accuracy: 0.9717 [-0.05633559 0.06754186 0.022034 ... -0.40205157 -0.28059885 0.4595701 ] Sparsity at: 0.013428782188841202 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0197 - accuracy: 0.9963 - val_loss: 0.1070 - val_accuracy: 0.9708 [-0.05633559 0.06754186 0.022034 ... -0.40841088 -0.28249142 0.4703986 ] Sparsity at: 0.013428782188841202 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0170 - accuracy: 0.9970 - val_loss: 0.1088 - val_accuracy: 0.9714 [-0.05633559 0.06754186 0.022034 ... -0.41430452 -0.28465152 0.4806524 ] Sparsity at: 0.013428782188841202 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0148 - accuracy: 0.9975 - val_loss: 0.1118 - val_accuracy: 0.9712 [-0.05633559 0.06754186 0.022034 ... -0.42126396 -0.2863751 0.49031523] Sparsity at: 0.013428782188841202 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0130 - accuracy: 0.9982 - val_loss: 0.1142 - val_accuracy: 0.9716 [-0.05633559 0.06754186 0.022034 ... -0.42939818 -0.28862867 0.49718717] Sparsity at: 0.013428782188841202 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0115 - accuracy: 0.9984 - val_loss: 0.1154 - val_accuracy: 0.9716 [-0.05633559 0.06754186 0.022034 ... -0.4356343 -0.29041043 0.5042449 ] Sparsity at: 0.013428782188841202 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0104 - accuracy: 0.9986 - val_loss: 0.1152 - val_accuracy: 0.9717 [-0.05633559 0.06754186 0.022034 ... -0.44143128 -0.2931453 0.5105457 ] Sparsity at: 0.013428782188841202 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0091 - accuracy: 0.9987 - val_loss: 0.1172 - val_accuracy: 0.9727 [-0.05633559 0.06754186 0.022034 ... -0.44674623 -0.29600975 0.51846385] Sparsity at: 0.013428782188841202 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9987 - val_loss: 0.1232 - val_accuracy: 0.9712 [-0.05633559 0.06754186 0.022034 ... -0.4536321 -0.3027641 0.5253391 ] Sparsity at: 0.013428782188841202 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.1257 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -0.4595385 -0.3014701 0.5326898 ] Sparsity at: 0.013428782188841202 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9981 - val_loss: 0.1302 - val_accuracy: 0.9724 [-0.05633559 0.06754186 0.022034 ... -0.46224034 -0.29487988 0.5365355 ] Sparsity at: 0.013428782188841202 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9981 - val_loss: 0.1404 - val_accuracy: 0.9710 [-0.05633559 0.06754186 0.022034 ... -0.45769608 -0.31198946 0.54282606] Sparsity at: 0.013428782188841202 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9979 - val_loss: 0.1570 - val_accuracy: 0.9671 [-0.05633559 0.06754186 0.022034 ... -0.47193122 -0.30742115 0.5482475 ] Sparsity at: 0.013428782188841202 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9981 - val_loss: 0.1517 - val_accuracy: 0.9686 [-0.05633559 0.06754186 0.022034 ... -0.476216 -0.3049791 0.5490274 ] Sparsity at: 0.013428782188841202 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0082 - accuracy: 0.9978 - val_loss: 0.1489 - val_accuracy: 0.9699 [-0.05633559 0.06754186 0.022034 ... -0.482712 -0.30353972 0.5520507 ] Sparsity at: 0.013428782188841202 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.1422 - val_accuracy: 0.9714 [-0.05633559 0.06754186 0.022034 ... -0.49313915 -0.29772556 0.5661293 ] Sparsity at: 0.013428782188841202 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9992 - val_loss: 0.1360 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -0.49840492 -0.28638247 0.5671187 ] Sparsity at: 0.013428782188841202 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.1358 - val_accuracy: 0.9741 [-0.05633559 0.06754186 0.022034 ... -0.5041393 -0.29051673 0.5691587 ] Sparsity at: 0.013428782188841202 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.1409 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -0.5046102 -0.2940186 0.576729 ] Sparsity at: 0.013428782188841202 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1508 - val_accuracy: 0.9722 [-0.05633559 0.06754186 0.022034 ... -0.51392454 -0.29258806 0.57948154] Sparsity at: 0.013428782188841202 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9995 - val_loss: 0.1478 - val_accuracy: 0.9717 [-0.05633559 0.06754186 0.022034 ... -0.5150788 -0.2936962 0.5796223 ] Sparsity at: 0.013428782188841202 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 0.9997 - val_loss: 0.1457 - val_accuracy: 0.9727 [-0.05633559 0.06754186 0.022034 ... -0.52052563 -0.29689276 0.59093803] Sparsity at: 0.013428782188841202 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.1645 - val_accuracy: 0.9695 [-0.05633559 0.06754186 0.022034 ... -0.53709114 -0.30214968 0.5932121 ] Sparsity at: 0.013428782188841202 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9997 - val_loss: 0.1588 - val_accuracy: 0.9697 [-0.05633559 0.06754186 0.022034 ... -0.5435376 -0.3006364 0.5994601 ] Sparsity at: 0.013428782188841202 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.1618 - val_accuracy: 0.9715 [-0.05633559 0.06754186 0.022034 ... -0.54564255 -0.2972169 0.59858155] Sparsity at: 0.013428782188841202 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.1530 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -0.55633557 -0.30488682 0.6035942 ] Sparsity at: 0.013428782188841202 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 0.9997 - val_loss: 0.1523 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -0.5646002 -0.30382395 0.6063423 ] Sparsity at: 0.013428782188841202 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1563 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -0.5751787 -0.3047188 0.6156139 ] Sparsity at: 0.013428782188841202 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1713 - val_accuracy: 0.9714 [-0.05633559 0.06754186 0.022034 ... -0.5867486 -0.30517986 0.62387997] Sparsity at: 0.013428782188841202 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0091 - accuracy: 0.9972 - val_loss: 0.1881 - val_accuracy: 0.9664 [-0.05633559 0.06754186 0.022034 ... -0.5754035 -0.32278758 0.6232624 ] Sparsity at: 0.013428782188841202 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9966 - val_loss: 0.1766 - val_accuracy: 0.9705 [-0.05633559 0.06754186 0.022034 ... -0.5978637 -0.30325073 0.61905575] Sparsity at: 0.013428782188841202 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.1495 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -0.58456576 -0.3006236 0.6074177 ] Sparsity at: 0.013428782188841202 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1513 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -0.59591955 -0.30828127 0.61861753] Sparsity at: 0.013428782188841202 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 6.7111e-04 - accuracy: 1.0000 - val_loss: 0.1543 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -0.5971873 -0.30942565 0.62039953] Sparsity at: 0.013428782188841202 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 9.9695e-04 - accuracy: 0.9999 - val_loss: 0.1509 - val_accuracy: 0.9748 [-0.05633559 0.06754186 0.022034 ... -0.5989052 -0.31404325 0.6195683 ] Sparsity at: 0.013428782188841202 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.1286661979682755 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.21580730399485404 Thresholhold -0.0714910477399826 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.40890553717479605 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 4.0937e-04 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 0.9756 [-0.05633559 0.06754186 0.022034 ... -0.6010669 -0.31855294 0.62328684] Sparsity at: 0.013428782188841202 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 2.8579e-04 - accuracy: 1.0000 - val_loss: 0.1502 - val_accuracy: 0.9753 [-0.05633559 0.06754186 0.022034 ... -0.6032623 -0.32047427 0.6256451 ] Sparsity at: 0.013428782188841202 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3529e-04 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9755 [-0.05633559 0.06754186 0.022034 ... -0.605843 -0.32192737 0.62783355] Sparsity at: 0.013428782188841202 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0416e-04 - accuracy: 1.0000 - val_loss: 0.1515 - val_accuracy: 0.9754 [-0.05633559 0.06754186 0.022034 ... -0.60847706 -0.32350296 0.6299726 ] Sparsity at: 0.013428782188841202 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8212e-04 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.61114323 -0.3250241 0.63212246] Sparsity at: 0.013428782188841202 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6453e-04 - accuracy: 1.0000 - val_loss: 0.1530 - val_accuracy: 0.9760 [-0.05633559 0.06754186 0.022034 ... -0.613936 -0.326587 0.6343415 ] Sparsity at: 0.013428782188841202 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4943e-04 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9759 [-0.05633559 0.06754186 0.022034 ... -0.61676735 -0.32809937 0.6365775 ] Sparsity at: 0.013428782188841202 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3627e-04 - accuracy: 1.0000 - val_loss: 0.1547 - val_accuracy: 0.9760 [-0.05633559 0.06754186 0.022034 ... -0.6198347 -0.32967988 0.6389321 ] Sparsity at: 0.013428782188841202 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2460e-04 - accuracy: 1.0000 - val_loss: 0.1557 - val_accuracy: 0.9761 [-0.05633559 0.06754186 0.022034 ... -0.62295926 -0.331299 0.6413803 ] Sparsity at: 0.013428782188841202 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1371e-04 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9762 [-0.05633559 0.06754186 0.022034 ... -0.6262433 -0.3329095 0.64390975] Sparsity at: 0.013428782188841202 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0420e-04 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9761 [-0.05633559 0.06754186 0.022034 ... -0.6296892 -0.33456734 0.6465586 ] Sparsity at: 0.013428782188841202 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5432e-05 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9760 [-0.05633559 0.06754186 0.022034 ... -0.63327694 -0.33629557 0.64936686] Sparsity at: 0.013428782188841202 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7259e-05 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9760 [-0.05633559 0.06754186 0.022034 ... -0.6370913 -0.33807105 0.6522588 ] Sparsity at: 0.013428782188841202 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9661e-05 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9760 [-0.05633559 0.06754186 0.022034 ... -0.6411099 -0.3398783 0.6552557 ] Sparsity at: 0.013428782188841202 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2771e-05 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9758 [-0.05633559 0.06754186 0.022034 ... -0.6452972 -0.3417332 0.6583733 ] Sparsity at: 0.013428782188841202 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6442e-05 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.6496516 -0.34365332 0.66162926] Sparsity at: 0.013428782188841202 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0547e-05 - accuracy: 1.0000 - val_loss: 0.1645 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.6540842 -0.34558222 0.6650179 ] Sparsity at: 0.013428782188841202 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5055e-05 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.6586902 -0.34760848 0.66856354] Sparsity at: 0.013428782188841202 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0006e-05 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.66357964 -0.34971488 0.67220914] Sparsity at: 0.013428782188841202 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5444e-05 - accuracy: 1.0000 - val_loss: 0.1686 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.66848075 -0.3518639 0.67600536] Sparsity at: 0.013428782188841202 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1138e-05 - accuracy: 1.0000 - val_loss: 0.1700 - val_accuracy: 0.9758 [-0.05633559 0.06754186 0.022034 ... -0.67367166 -0.35408926 0.6798579 ] Sparsity at: 0.013428782188841202 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7210e-05 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9758 [-0.05633559 0.06754186 0.022034 ... -0.67903805 -0.35637048 0.68387246] Sparsity at: 0.013428782188841202 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3646e-05 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9756 [-0.05633559 0.06754186 0.022034 ... -0.6844542 -0.35869354 0.68798506] Sparsity at: 0.013428782188841202 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0307e-05 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9755 [-0.05633559 0.06754186 0.022034 ... -0.68984157 -0.3610477 0.6922244 ] Sparsity at: 0.013428782188841202 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7309e-05 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9756 [-0.05633559 0.06754186 0.022034 ... -0.6955406 -0.36342543 0.6965442 ] Sparsity at: 0.013428782188841202 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4544e-05 - accuracy: 1.0000 - val_loss: 0.1777 - val_accuracy: 0.9755 [-0.05633559 0.06754186 0.022034 ... -0.7011287 -0.36583966 0.70100665] Sparsity at: 0.013428782188841202 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2093e-05 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9755 [-0.05633559 0.06754186 0.022034 ... -0.70684737 -0.3683552 0.7055873 ] Sparsity at: 0.013428782188841202 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9755e-05 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9755 [-0.05633559 0.06754186 0.022034 ... -0.7127711 -0.37094063 0.7101944 ] Sparsity at: 0.013428782188841202 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7712e-05 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9756 [-0.05633559 0.06754186 0.022034 ... -0.7185977 -0.37349585 0.71492994] Sparsity at: 0.013428782188841202 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5858e-05 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.72451484 -0.37612066 0.7197477 ] Sparsity at: 0.013428782188841202 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4169e-05 - accuracy: 1.0000 - val_loss: 0.1861 - val_accuracy: 0.9759 [-0.05633559 0.06754186 0.022034 ... -0.7303979 -0.37876552 0.7246014 ] Sparsity at: 0.013428782188841202 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2658e-05 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9759 [-0.05633559 0.06754186 0.022034 ... -0.73659146 -0.38135976 0.729486 ] Sparsity at: 0.013428782188841202 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1299e-05 - accuracy: 1.0000 - val_loss: 0.1896 - val_accuracy: 0.9758 [-0.05633559 0.06754186 0.022034 ... -0.7426247 -0.38403293 0.73450917] Sparsity at: 0.013428782188841202 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0066e-05 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9757 [-0.05633559 0.06754186 0.022034 ... -0.7486887 -0.3867805 0.7396179 ] Sparsity at: 0.013428782188841202 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 8.9523e-06 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9758 [-0.05633559 0.06754186 0.022034 ... -0.75469095 -0.38952965 0.7447386 ] Sparsity at: 0.013428782188841202 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9509e-06 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9756 [-0.05633559 0.06754186 0.022034 ... -0.76076746 -0.3922144 0.74995196] Sparsity at: 0.013428782188841202 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0915e-06 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9756 [-0.05633559 0.06754186 0.022034 ... -0.76676595 -0.39493862 0.75508934] Sparsity at: 0.013428782188841202 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3027e-06 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9755 [-0.05633559 0.06754186 0.022034 ... -0.77314925 -0.3976347 0.7601814 ] Sparsity at: 0.013428782188841202 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5944e-06 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9754 [-0.05633559 0.06754186 0.022034 ... -0.77915096 -0.40042806 0.76541704] Sparsity at: 0.013428782188841202 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9684e-06 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9754 [-0.05633559 0.06754186 0.022034 ... -0.7852307 -0.4031282 0.7706772 ] Sparsity at: 0.013428782188841202 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4070e-06 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9754 [-0.05633559 0.06754186 0.022034 ... -0.79150957 -0.4059412 0.7758543 ] Sparsity at: 0.013428782188841202 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9130e-06 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9754 [-0.05633559 0.06754186 0.022034 ... -0.79763323 -0.4086928 0.7810083 ] Sparsity at: 0.013428782188841202 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4668e-06 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9751 [-0.05633559 0.06754186 0.022034 ... -0.80354035 -0.4115484 0.78627086] Sparsity at: 0.013428782188841202 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0773e-06 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9751 [-0.05633559 0.06754186 0.022034 ... -0.80960137 -0.41435784 0.79142904] Sparsity at: 0.013428782188841202 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7323e-06 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9749 [-0.05633559 0.06754186 0.022034 ... -0.8154423 -0.41720292 0.7967211 ] Sparsity at: 0.013428782188841202 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4185e-06 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9748 [-0.05633559 0.06754186 0.022034 ... -0.8213672 -0.41989234 0.80195564] Sparsity at: 0.013428782188841202 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1458e-06 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9748 [-0.05633559 0.06754186 0.022034 ... -0.8274858 -0.42262283 0.80710137] Sparsity at: 0.013428782188841202 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9038e-06 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9748 [-0.05633559 0.06754186 0.022034 ... -0.83351713 -0.4254581 0.812327 ] Sparsity at: 0.013428782188841202 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6847e-06 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9748 [-0.05633559 0.06754186 0.022034 ... -0.83943385 -0.42822215 0.8174839 ] Sparsity at: 0.013428782188841202 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4968e-06 - accuracy: 1.0000 - val_loss: 0.2209 - val_accuracy: 0.9747 [-0.05633559 0.06754186 0.022034 ... -0.84534377 -0.4308703 0.82266194] Sparsity at: 0.013428782188841202 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.18326435435228028 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.29567940505848966 Thresholhold -0.09674298763275146 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.7449820694404892 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 46s 7ms/step - loss: 1.3272e-06 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9746 [-0.05633559 0.06754186 0.022034 ... -0.8511702 -0.4335675 0.8278099 ] Sparsity at: 0.013428782188841202 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 1.1788e-06 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.85686666 -0.4363847 0.83297074] Sparsity at: 0.013428782188841202 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0478e-06 - accuracy: 1.0000 - val_loss: 0.2263 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.8625784 -0.4390938 0.83807963] Sparsity at: 0.013428782188841202 Epoch 104/500 235/235 [==============================] - 2s 10ms/step - loss: 9.2988e-07 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.86812675 -0.44185814 0.8432559 ] Sparsity at: 0.013428782188841202 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2586e-07 - accuracy: 1.0000 - val_loss: 0.2299 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.8736012 -0.44462267 0.848396 ] Sparsity at: 0.013428782188841202 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3452e-07 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.8791176 -0.44729233 0.85354346] Sparsity at: 0.013428782188841202 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5409e-07 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.88473636 -0.44994158 0.8584795 ] Sparsity at: 0.013428782188841202 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8219e-07 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9746 [-0.05633559 0.06754186 0.022034 ... -0.89013773 -0.452586 0.86342025] Sparsity at: 0.013428782188841202 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1837e-07 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9747 [-0.05633559 0.06754186 0.022034 ... -0.8953078 -0.45520103 0.8684237 ] Sparsity at: 0.013428782188841202 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6226e-07 - accuracy: 1.0000 - val_loss: 0.2389 - val_accuracy: 0.9747 [-0.05633559 0.06754186 0.022034 ... -0.9004565 -0.45784882 0.8734141 ] Sparsity at: 0.013428782188841202 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1366e-07 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9746 [-0.05633559 0.06754186 0.022034 ... -0.90555996 -0.46046188 0.87833005] Sparsity at: 0.013428782188841202 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7062e-07 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9746 [-0.05633559 0.06754186 0.022034 ... -0.91082555 -0.46303982 0.8830844 ] Sparsity at: 0.013428782188841202 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3103e-07 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.91591704 -0.4656211 0.88782793] Sparsity at: 0.013428782188841202 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9702e-07 - accuracy: 1.0000 - val_loss: 0.2456 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.9208823 -0.4681336 0.8925893 ] Sparsity at: 0.013428782188841202 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6636e-07 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.9255703 -0.470668 0.89729536] Sparsity at: 0.013428782188841202 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3907e-07 - accuracy: 1.0000 - val_loss: 0.2487 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.9301279 -0.47303513 0.90193117] Sparsity at: 0.013428782188841202 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1539e-07 - accuracy: 1.0000 - val_loss: 0.2503 - val_accuracy: 0.9747 [-0.05633559 0.06754186 0.022034 ... -0.9346598 -0.47540334 0.90658057] Sparsity at: 0.013428782188841202 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9430e-07 - accuracy: 1.0000 - val_loss: 0.2518 - val_accuracy: 0.9747 [-0.05633559 0.06754186 0.022034 ... -0.9390739 -0.4777911 0.91107935] Sparsity at: 0.013428782188841202 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7587e-07 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9747 [-0.05633559 0.06754186 0.022034 ... -0.9433782 -0.48011795 0.9155076 ] Sparsity at: 0.013428782188841202 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5887e-07 - accuracy: 1.0000 - val_loss: 0.2547 - val_accuracy: 0.9746 [-0.05633559 0.06754186 0.022034 ... -0.9478668 -0.4824114 0.9197812 ] Sparsity at: 0.013428782188841202 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4414e-07 - accuracy: 1.0000 - val_loss: 0.2561 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.9520277 -0.48462495 0.92401826] Sparsity at: 0.013428782188841202 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3084e-07 - accuracy: 1.0000 - val_loss: 0.2575 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.95591325 -0.4868079 0.92821795] Sparsity at: 0.013428782188841202 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1937e-07 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9743 [-0.05633559 0.06754186 0.022034 ... -0.95975107 -0.488917 0.93237764] Sparsity at: 0.013428782188841202 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0900e-07 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -0.9634477 -0.4909778 0.9364204 ] Sparsity at: 0.013428782188841202 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9444e-08 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9743 [-0.05633559 0.06754186 0.022034 ... -0.96704346 -0.49299693 0.94034487] Sparsity at: 0.013428782188841202 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 9.1277e-08 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9743 [-0.05633559 0.06754186 0.022034 ... -0.97047955 -0.49493715 0.9442157 ] Sparsity at: 0.013428782188841202 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3808e-08 - accuracy: 1.0000 - val_loss: 0.2640 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.9738342 -0.49684662 0.9479262 ] Sparsity at: 0.013428782188841202 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7228e-08 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.9772304 -0.49868393 0.95153075] Sparsity at: 0.013428782188841202 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1168e-08 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.98024726 -0.50045395 0.95502913] Sparsity at: 0.013428782188841202 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5901e-08 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.98319644 -0.5021759 0.95848745] Sparsity at: 0.013428782188841202 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1154e-08 - accuracy: 1.0000 - val_loss: 0.2681 - val_accuracy: 0.9745 [-0.05633559 0.06754186 0.022034 ... -0.98620117 -0.50384986 0.9617311 ] Sparsity at: 0.013428782188841202 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6821e-08 - accuracy: 1.0000 - val_loss: 0.2691 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.98905206 -0.5054923 0.9649589 ] Sparsity at: 0.013428782188841202 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2849e-08 - accuracy: 1.0000 - val_loss: 0.2702 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.99163055 -0.50703 0.96807665] Sparsity at: 0.013428782188841202 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9404e-08 - accuracy: 1.0000 - val_loss: 0.2709 - val_accuracy: 0.9744 [-0.05633559 0.06754186 0.022034 ... -0.9942317 -0.50853425 0.97106457] Sparsity at: 0.013428782188841202 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6231e-08 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -0.99658495 -0.5100042 0.9739724 ] Sparsity at: 0.013428782188841202 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3348e-08 - accuracy: 1.0000 - val_loss: 0.2727 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -0.9989903 -0.5113921 0.9767433 ] Sparsity at: 0.013428782188841202 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0724e-08 - accuracy: 1.0000 - val_loss: 0.2735 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -1.0012252 -0.5127869 0.979449 ] Sparsity at: 0.013428782188841202 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8326e-08 - accuracy: 1.0000 - val_loss: 0.2745 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -1.003309 -0.5140634 0.9820943 ] Sparsity at: 0.013428782188841202 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6226e-08 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -1.0053384 -0.5153146 0.9845796 ] Sparsity at: 0.013428782188841202 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4209e-08 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -1.0072207 -0.5165245 0.98701525] Sparsity at: 0.013428782188841202 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2377e-08 - accuracy: 1.0000 - val_loss: 0.2764 - val_accuracy: 0.9742 [-0.05633559 0.06754186 0.022034 ... -1.0090288 -0.5177041 0.9893772 ] Sparsity at: 0.013428782188841202 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0796e-08 - accuracy: 1.0000 - val_loss: 0.2771 - val_accuracy: 0.9741 [-0.05633559 0.06754186 0.022034 ... -1.0107769 -0.5188047 0.99167633] Sparsity at: 0.013428782188841202 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9405e-08 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.9740 [-0.05633559 0.06754186 0.022034 ... -1.0125543 -0.5198955 0.99386847] Sparsity at: 0.013428782188841202 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7907e-08 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.014247 -0.5208662 0.9960046 ] Sparsity at: 0.013428782188841202 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6580e-08 - accuracy: 1.0000 - val_loss: 0.2790 - val_accuracy: 0.9739 [-0.05633559 0.06754186 0.022034 ... -1.0158894 -0.52183783 0.99804133] Sparsity at: 0.013428782188841202 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5499e-08 - accuracy: 1.0000 - val_loss: 0.2794 - val_accuracy: 0.9740 [-0.05633559 0.06754186 0.022034 ... -1.0174714 -0.5227763 1.0000162 ] Sparsity at: 0.013428782188841202 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4406e-08 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9739 [-0.05633559 0.06754186 0.022034 ... -1.018961 -0.5236896 1.0019255 ] Sparsity at: 0.013428782188841202 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3409e-08 - accuracy: 1.0000 - val_loss: 0.2806 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0204213 -0.524557 1.0037844 ] Sparsity at: 0.013428782188841202 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2431e-08 - accuracy: 1.0000 - val_loss: 0.2811 - val_accuracy: 0.9739 [-0.05633559 0.06754186 0.022034 ... -1.0217309 -0.525416 1.005599 ] Sparsity at: 0.013428782188841202 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1605e-08 - accuracy: 1.0000 - val_loss: 0.2816 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0230007 -0.5262262 1.007368 ] Sparsity at: 0.013428782188841202 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.2415795205951845 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.3770834527345741 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 1.0449872193910679 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 2.0742e-08 - accuracy: 1.0000 - val_loss: 0.2822 - val_accuracy: 0.9740 [-0.05633559 0.06754186 0.022034 ... -1.0243046 -0.527005 1.0090699 ] Sparsity at: 0.013428782188841202 Epoch 152/500 235/235 [==============================] - 2s 7ms/step - loss: 1.9956e-08 - accuracy: 1.0000 - val_loss: 0.2827 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0255127 -0.5277754 1.0107232 ] Sparsity at: 0.013428782188841202 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9221e-08 - accuracy: 1.0000 - val_loss: 0.2833 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0266932 -0.52851945 1.0123428 ] Sparsity at: 0.013428782188841202 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8523e-08 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0278345 -0.52921075 1.0139028 ] Sparsity at: 0.013428782188841202 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7889e-08 - accuracy: 1.0000 - val_loss: 0.2840 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0289878 -0.52988535 1.0153589 ] Sparsity at: 0.013428782188841202 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7293e-08 - accuracy: 1.0000 - val_loss: 0.2844 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0300411 -0.53053784 1.0167962 ] Sparsity at: 0.013428782188841202 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6652e-08 - accuracy: 1.0000 - val_loss: 0.2847 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0310479 -0.53115034 1.018183 ] Sparsity at: 0.013428782188841202 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6244e-08 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0320712 -0.53175336 1.0195091 ] Sparsity at: 0.013428782188841202 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5748e-08 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0330979 -0.53237724 1.0208342 ] Sparsity at: 0.013428782188841202 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5261e-08 - accuracy: 1.0000 - val_loss: 0.2858 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0340749 -0.5329366 1.0221074 ] Sparsity at: 0.013428782188841202 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4830e-08 - accuracy: 1.0000 - val_loss: 0.2861 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0350227 -0.53349847 1.0233537 ] Sparsity at: 0.013428782188841202 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4393e-08 - accuracy: 1.0000 - val_loss: 0.2865 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0359616 -0.5340087 1.0245754 ] Sparsity at: 0.013428782188841202 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3999e-08 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0368762 -0.53452396 1.0257882 ] Sparsity at: 0.013428782188841202 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3580e-08 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0377088 -0.53502285 1.026964 ] Sparsity at: 0.013428782188841202 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3262e-08 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0385438 -0.5354904 1.0281081 ] Sparsity at: 0.013428782188841202 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2944e-08 - accuracy: 1.0000 - val_loss: 0.2877 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0393531 -0.5359215 1.0292581 ] Sparsity at: 0.013428782188841202 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2644e-08 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0401715 -0.53633404 1.0303756 ] Sparsity at: 0.013428782188841202 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2298e-08 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.040934 -0.53675365 1.0314819 ] Sparsity at: 0.013428782188841202 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2044e-08 - accuracy: 1.0000 - val_loss: 0.2885 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0416769 -0.53716636 1.0325567 ] Sparsity at: 0.013428782188841202 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1762e-08 - accuracy: 1.0000 - val_loss: 0.2887 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0423969 -0.5375251 1.033626 ] Sparsity at: 0.013428782188841202 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1426e-08 - accuracy: 1.0000 - val_loss: 0.2890 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0431185 -0.53789175 1.0346382 ] Sparsity at: 0.013428782188841202 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1194e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0438416 -0.5382668 1.0356227 ] Sparsity at: 0.013428782188841202 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0949e-08 - accuracy: 1.0000 - val_loss: 0.2893 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0445572 -0.53862035 1.0366026 ] Sparsity at: 0.013428782188841202 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0653e-08 - accuracy: 1.0000 - val_loss: 0.2896 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0452425 -0.5389753 1.0375633 ] Sparsity at: 0.013428782188841202 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0482e-08 - accuracy: 1.0000 - val_loss: 0.2899 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0458951 -0.5393373 1.0385144 ] Sparsity at: 0.013428782188841202 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0200e-08 - accuracy: 1.0000 - val_loss: 0.2900 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.046572 -0.53964907 1.0393994 ] Sparsity at: 0.013428782188841202 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9818e-09 - accuracy: 1.0000 - val_loss: 0.2902 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0472268 -0.53995013 1.0402573 ] Sparsity at: 0.013428782188841202 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7791e-09 - accuracy: 1.0000 - val_loss: 0.2904 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0478694 -0.5402644 1.0411122 ] Sparsity at: 0.013428782188841202 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5805e-09 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0485021 -0.5405748 1.0419587 ] Sparsity at: 0.013428782188841202 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3818e-09 - accuracy: 1.0000 - val_loss: 0.2907 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0491046 -0.5408673 1.0428065 ] Sparsity at: 0.013428782188841202 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 9.1890e-09 - accuracy: 1.0000 - val_loss: 0.2908 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0496713 -0.541131 1.0436116 ] Sparsity at: 0.013428782188841202 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0162e-09 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.050229 -0.5414186 1.0444313 ] Sparsity at: 0.013428782188841202 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 8.8195e-09 - accuracy: 1.0000 - val_loss: 0.2912 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0507895 -0.541692 1.0452209 ] Sparsity at: 0.013428782188841202 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 8.6427e-09 - accuracy: 1.0000 - val_loss: 0.2914 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0513501 -0.54195094 1.045973 ] Sparsity at: 0.013428782188841202 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 8.4798e-09 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0518947 -0.5422173 1.0467236 ] Sparsity at: 0.013428782188841202 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3288e-09 - accuracy: 1.0000 - val_loss: 0.2917 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.052405 -0.5424797 1.0474819 ] Sparsity at: 0.013428782188841202 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2056e-09 - accuracy: 1.0000 - val_loss: 0.2919 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0529338 -0.5427118 1.0482135 ] Sparsity at: 0.013428782188841202 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0466e-09 - accuracy: 1.0000 - val_loss: 0.2919 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0534321 -0.5429559 1.0489494 ] Sparsity at: 0.013428782188841202 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9314e-09 - accuracy: 1.0000 - val_loss: 0.2922 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0539299 -0.5431974 1.0496535 ] Sparsity at: 0.013428782188841202 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7883e-09 - accuracy: 1.0000 - val_loss: 0.2923 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0544062 -0.54343486 1.050365 ] Sparsity at: 0.013428782188841202 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6294e-09 - accuracy: 1.0000 - val_loss: 0.2923 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0548873 -0.54366857 1.0510466 ] Sparsity at: 0.013428782188841202 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 7.5201e-09 - accuracy: 1.0000 - val_loss: 0.2925 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.055369 -0.5438792 1.0517174 ] Sparsity at: 0.013428782188841202 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4307e-09 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0558525 -0.5441046 1.0523689 ] Sparsity at: 0.013428782188841202 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2340e-09 - accuracy: 1.0000 - val_loss: 0.2927 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0563036 -0.5442977 1.0530139 ] Sparsity at: 0.013428782188841202 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1565e-09 - accuracy: 1.0000 - val_loss: 0.2927 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0567434 -0.5445168 1.0536458 ] Sparsity at: 0.013428782188841202 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0353e-09 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0571518 -0.5447017 1.054279 ] Sparsity at: 0.013428782188841202 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 6.9320e-09 - accuracy: 1.0000 - val_loss: 0.2929 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0575655 -0.5449015 1.0548942 ] Sparsity at: 0.013428782188841202 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8148e-09 - accuracy: 1.0000 - val_loss: 0.2929 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0579693 -0.54507416 1.0555091 ] Sparsity at: 0.013428782188841202 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7671e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0583631 -0.54527676 1.0561134 ] Sparsity at: 0.013428782188841202 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6479e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0587392 -0.5454832 1.0567143 ] Sparsity at: 0.013428782188841202 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.3022082178638925 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.4496724135634338 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 1.2477209067633623 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 47s 7ms/step - loss: 6.5863e-09 - accuracy: 1.0000 - val_loss: 0.2932 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0591125 -0.5456722 1.0573184 ] Sparsity at: 0.013428782188841202 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 6.4035e-09 - accuracy: 1.0000 - val_loss: 0.2933 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0594854 -0.5458568 1.0578749 ] Sparsity at: 0.013428782188841202 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3539e-09 - accuracy: 1.0000 - val_loss: 0.2934 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0598408 -0.54604596 1.0584519 ] Sparsity at: 0.013428782188841202 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2664e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0601937 -0.5462027 1.0589999 ] Sparsity at: 0.013428782188841202 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1830e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0605372 -0.54638743 1.0595446 ] Sparsity at: 0.013428782188841202 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 6.0896e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0608796 -0.5465484 1.0600678 ] Sparsity at: 0.013428782188841202 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0181e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0612164 -0.54671925 1.0605888 ] Sparsity at: 0.013428782188841202 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9823e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.061545 -0.54687 1.0611254 ] Sparsity at: 0.013428782188841202 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8711e-09 - accuracy: 1.0000 - val_loss: 0.2938 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.061865 -0.5470361 1.0616374 ] Sparsity at: 0.013428782188841202 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 5.8115e-09 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0621754 -0.54719454 1.0621694 ] Sparsity at: 0.013428782188841202 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7042e-09 - accuracy: 1.0000 - val_loss: 0.2940 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0624912 -0.5473169 1.0626872 ] Sparsity at: 0.013428782188841202 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6227e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0627964 -0.5474322 1.0631889 ] Sparsity at: 0.013428782188841202 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6406e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0630938 -0.5475777 1.0636849 ] Sparsity at: 0.013428782188841202 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5114e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.063376 -0.5477309 1.0641999 ] Sparsity at: 0.013428782188841202 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 5.4936e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.063662 -0.5478796 1.0646768 ] Sparsity at: 0.013428782188841202 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 5.4200e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0639541 -0.5480004 1.0651562 ] Sparsity at: 0.013428782188841202 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3028e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0642537 -0.5481089 1.0656278 ] Sparsity at: 0.013428782188841202 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3485e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0645223 -0.5482378 1.0661006 ] Sparsity at: 0.013428782188841202 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 5.2174e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0647815 -0.54838353 1.0665672 ] Sparsity at: 0.013428782188841202 Epoch 220/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1796e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0650483 -0.5484913 1.067021 ] Sparsity at: 0.013428782188841202 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0704e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0652937 -0.54860663 1.0674517 ] Sparsity at: 0.013428782188841202 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0366e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0655464 -0.5487248 1.0679228 ] Sparsity at: 0.013428782188841202 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9412e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0657823 -0.54883695 1.0683628 ] Sparsity at: 0.013428782188841202 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8856e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0660478 -0.5489589 1.0687714 ] Sparsity at: 0.013428782188841202 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8836e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.066274 -0.5491078 1.0692064 ] Sparsity at: 0.013428782188841202 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7922e-09 - accuracy: 1.0000 - val_loss: 0.2944 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0665255 -0.549216 1.0696324 ] Sparsity at: 0.013428782188841202 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7664e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0667574 -0.5493064 1.0700636 ] Sparsity at: 0.013428782188841202 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7108e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.066983 -0.5494307 1.0704778 ] Sparsity at: 0.013428782188841202 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6810e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0672128 -0.549562 1.0708963 ] Sparsity at: 0.013428782188841202 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6213e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0674496 -0.54969054 1.071295 ] Sparsity at: 0.013428782188841202 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5896e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0676813 -0.54981554 1.0717046 ] Sparsity at: 0.013428782188841202 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5141e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.067873 -0.5499374 1.072103 ] Sparsity at: 0.013428782188841202 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5617e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0680895 -0.5500682 1.0725214 ] Sparsity at: 0.013428782188841202 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4922e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0682998 -0.55018085 1.0729235 ] Sparsity at: 0.013428782188841202 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4207e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0684992 -0.5502716 1.0732911 ] Sparsity at: 0.013428782188841202 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3909e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0687017 -0.5503781 1.073692 ] Sparsity at: 0.013428782188841202 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3472e-09 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0688787 -0.5504653 1.0740886 ] Sparsity at: 0.013428782188841202 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3154e-09 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0690846 -0.5505604 1.0744824 ] Sparsity at: 0.013428782188841202 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2737e-09 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.069279 -0.5506633 1.0748698 ] Sparsity at: 0.013428782188841202 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2836e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0694746 -0.55077094 1.075258 ] Sparsity at: 0.013428782188841202 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2558e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0696431 -0.550868 1.0756494 ] Sparsity at: 0.013428782188841202 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2439e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0698408 -0.55097467 1.0760163 ] Sparsity at: 0.013428782188841202 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1803e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0700303 -0.5510774 1.0763818 ] Sparsity at: 0.013428782188841202 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2021e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0702239 -0.55118823 1.0767487 ] Sparsity at: 0.013428782188841202 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0809e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0704082 -0.5512641 1.0771127 ] Sparsity at: 0.013428782188841202 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0889e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.070596 -0.5513523 1.0774815 ] Sparsity at: 0.013428782188841202 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0829e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.070788 -0.5514322 1.0778255 ] Sparsity at: 0.013428782188841202 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0909e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0709784 -0.55153596 1.0781844 ] Sparsity at: 0.013428782188841202 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9697e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.071152 -0.55160785 1.078555 ] Sparsity at: 0.013428782188841202 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9836e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0713245 -0.5516715 1.0789213 ] Sparsity at: 0.013428782188841202 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.36929382424799684 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.5139876747259535 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 1.417772480245631 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 3.9697e-09 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0715237 -0.5517544 1.0792676 ] Sparsity at: 0.013428782188841202 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 3.9478e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0716921 -0.5518411 1.0796167 ] Sparsity at: 0.013428782188841202 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8862e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0718504 -0.5519133 1.0799482 ] Sparsity at: 0.013428782188841202 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9180e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0720376 -0.55199444 1.0802859 ] Sparsity at: 0.013428782188841202 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8902e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0721992 -0.5520875 1.0806527 ] Sparsity at: 0.013428782188841202 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8187e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0723754 -0.5521393 1.0810019 ] Sparsity at: 0.013428782188841202 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8167e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0725332 -0.5522161 1.081314 ] Sparsity at: 0.013428782188841202 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7789e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.072715 -0.5522853 1.0816363 ] Sparsity at: 0.013428782188841202 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8127e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0728868 -0.55231804 1.0819876 ] Sparsity at: 0.013428782188841202 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7611e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0730474 -0.5523813 1.0823241 ] Sparsity at: 0.013428782188841202 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7372e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0732039 -0.5524437 1.0826315 ] Sparsity at: 0.013428782188841202 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7591e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0733681 -0.552534 1.0829564 ] Sparsity at: 0.013428782188841202 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6796e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0735127 -0.5525761 1.0832627 ] Sparsity at: 0.013428782188841202 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6498e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9738 [-0.05633559 0.06754186 0.022034 ... -1.0736666 -0.55262893 1.083581 ] Sparsity at: 0.013428782188841202 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6577e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0738224 -0.5527044 1.0839045 ] Sparsity at: 0.013428782188841202 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6339e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0739535 -0.5527631 1.0842488 ] Sparsity at: 0.013428782188841202 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5842e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0740936 -0.55282456 1.0845733 ] Sparsity at: 0.013428782188841202 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6339e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0742531 -0.5528916 1.0848911 ] Sparsity at: 0.013428782188841202 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5624e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0744164 -0.5529584 1.0852196 ] Sparsity at: 0.013428782188841202 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5683e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0745513 -0.5530021 1.0855271 ] Sparsity at: 0.013428782188841202 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5683e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0746874 -0.55306816 1.0858365 ] Sparsity at: 0.013428782188841202 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5663e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0748256 -0.5531154 1.0861722 ] Sparsity at: 0.013428782188841202 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5147e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0749557 -0.55318004 1.0865016 ] Sparsity at: 0.013428782188841202 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5266e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0750937 -0.55323726 1.0868301 ] Sparsity at: 0.013428782188841202 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4750e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0752294 -0.5532787 1.0871124 ] Sparsity at: 0.013428782188841202 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4869e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0753773 -0.5533494 1.0874112 ] Sparsity at: 0.013428782188841202 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4750e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0755224 -0.55337983 1.0877173 ] Sparsity at: 0.013428782188841202 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4531e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0756483 -0.55341136 1.0880392 ] Sparsity at: 0.013428782188841202 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4332e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9737 [-0.05633559 0.06754186 0.022034 ... -1.0758038 -0.55346584 1.0883397 ] Sparsity at: 0.013428782188841202 Epoch 280/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4273e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0759407 -0.5535129 1.088646 ] Sparsity at: 0.013428782188841202 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4233e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0760624 -0.55355656 1.0889182 ] Sparsity at: 0.013428782188841202 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3677e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0761676 -0.55358624 1.0892183 ] Sparsity at: 0.013428782188841202 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3736e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0763241 -0.55363023 1.089523 ] Sparsity at: 0.013428782188841202 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3736e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.076455 -0.55365705 1.0898328 ] Sparsity at: 0.013428782188841202 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3538e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0765811 -0.5537136 1.0901015 ] Sparsity at: 0.013428782188841202 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3458e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0767124 -0.5537499 1.0903846 ] Sparsity at: 0.013428782188841202 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3538e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0768273 -0.5538018 1.0906746 ] Sparsity at: 0.013428782188841202 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3418e-09 - accuracy: 1.0000 - val_loss: 0.2955 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0769676 -0.5538472 1.090971 ] Sparsity at: 0.013428782188841202 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2882e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.07711 -0.55388147 1.0912235 ] Sparsity at: 0.013428782188841202 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3021e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0772243 -0.5539069 1.0915099 ] Sparsity at: 0.013428782188841202 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3339e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0773516 -0.55398506 1.091781 ] Sparsity at: 0.013428782188841202 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3041e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0774622 -0.5540205 1.092057 ] Sparsity at: 0.013428782188841202 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2643e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0775805 -0.5540608 1.0923457 ] Sparsity at: 0.013428782188841202 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2425e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0777196 -0.5541216 1.09262 ] Sparsity at: 0.013428782188841202 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2365e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0778157 -0.55413234 1.0928867 ] Sparsity at: 0.013428782188841202 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2683e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0779366 -0.5541998 1.0931971 ] Sparsity at: 0.013428782188841202 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2266e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0780588 -0.55422485 1.09347 ] Sparsity at: 0.013428782188841202 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2485e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9736 [-0.05633559 0.06754186 0.022034 ... -1.0781823 -0.5542677 1.09375 ] Sparsity at: 0.013428782188841202 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2266e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.078305 -0.5543017 1.0940162 ] Sparsity at: 0.013428782188841202 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2306e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0784281 -0.5543414 1.0942988 ] Sparsity at: 0.013428782188841202 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.43996519941289236 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.5727221716983735 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 1.603471847124041 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 3.2604e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0785398 -0.5543748 1.0946075 ] Sparsity at: 0.013428782188841202 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 3.1213e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0786326 -0.5543894 1.0948521 ] Sparsity at: 0.013428782188841202 Epoch 303/500 235/235 [==============================] - 2s 7ms/step - loss: 3.1988e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0787642 -0.55439734 1.0951487 ] Sparsity at: 0.013428782188841202 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1730e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0788906 -0.55441797 1.0954133 ] Sparsity at: 0.013428782188841202 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1730e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0790163 -0.5544899 1.095691 ] Sparsity at: 0.013428782188841202 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1332e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.079133 -0.55452365 1.0959729 ] Sparsity at: 0.013428782188841202 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1352e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0792505 -0.5545478 1.0962436 ] Sparsity at: 0.013428782188841202 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1213e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.079337 -0.5545703 1.0965333 ] Sparsity at: 0.013428782188841202 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1610e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0794402 -0.55458635 1.0968225 ] Sparsity at: 0.013428782188841202 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1193e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0795479 -0.5546215 1.0970931 ] Sparsity at: 0.013428782188841202 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1034e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0796435 -0.55464476 1.097349 ] Sparsity at: 0.013428782188841202 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0994e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0797596 -0.5546882 1.0976152 ] Sparsity at: 0.013428782188841202 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0617e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0798457 -0.55469376 1.0979127 ] Sparsity at: 0.013428782188841202 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0776e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9735 [-0.05633559 0.06754186 0.022034 ... -1.0799402 -0.5547027 1.0981953 ] Sparsity at: 0.013428782188841202 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1074e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0800431 -0.55474025 1.098474 ] Sparsity at: 0.013428782188841202 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1193e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0801332 -0.5547743 1.098768 ] Sparsity at: 0.013428782188841202 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0796e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0802522 -0.5548106 1.0990688 ] Sparsity at: 0.013428782188841202 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0803477 -0.5548069 1.0993515 ] Sparsity at: 0.013428782188841202 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0994e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0804354 -0.55481565 1.0996249 ] Sparsity at: 0.013428782188841202 Epoch 320/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0279e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0805384 -0.554815 1.0998961 ] Sparsity at: 0.013428782188841202 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0915e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0806353 -0.55485636 1.1001829 ] Sparsity at: 0.013428782188841202 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.2962 - val_accuracy: 0.9734 [-0.05633559 0.06754186 0.022034 ... -1.0807254 -0.5548825 1.1004514 ] Sparsity at: 0.013428782188841202 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0160e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0808197 -0.55490273 1.1007334 ] Sparsity at: 0.013428782188841202 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0319e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0809133 -0.5549055 1.1010038 ] Sparsity at: 0.013428782188841202 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9802e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0810157 -0.5549107 1.1012746 ] Sparsity at: 0.013428782188841202 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9922e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9733 [-0.05633559 0.06754186 0.022034 ... -1.0811251 -0.5549459 1.1015509 ] Sparsity at: 0.013428782188841202 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0537e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0812094 -0.55499494 1.1018109 ] Sparsity at: 0.013428782188841202 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0080e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0812945 -0.5550107 1.1020998 ] Sparsity at: 0.013428782188841202 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0080e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0813905 -0.55501103 1.1023529 ] Sparsity at: 0.013428782188841202 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9484e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0814731 -0.55502987 1.1026222 ] Sparsity at: 0.013428782188841202 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0319e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0815684 -0.5550552 1.1029053 ] Sparsity at: 0.013428782188841202 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9743e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0816787 -0.55504996 1.1031814 ] Sparsity at: 0.013428782188841202 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9922e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0817572 -0.5550664 1.1034521 ] Sparsity at: 0.013428782188841202 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9922e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0818626 -0.55509764 1.1037171 ] Sparsity at: 0.013428782188841202 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9743e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0819473 -0.55511206 1.1039956 ] Sparsity at: 0.013428782188841202 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9604e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0820236 -0.55511 1.1042879 ] Sparsity at: 0.013428782188841202 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9584e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0821093 -0.5551383 1.1045696 ] Sparsity at: 0.013428782188841202 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9663e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.082219 -0.5551519 1.1048255 ] Sparsity at: 0.013428782188841202 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9385e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0823312 -0.5551563 1.1050894 ] Sparsity at: 0.013428782188841202 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0823916 -0.5551985 1.105361 ] Sparsity at: 0.013428782188841202 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9445e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0824841 -0.5552175 1.1056228 ] Sparsity at: 0.013428782188841202 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9147e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0825711 -0.5552028 1.105901 ] Sparsity at: 0.013428782188841202 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9325e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0826535 -0.55525047 1.1061584 ] Sparsity at: 0.013428782188841202 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9643e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0827233 -0.55525285 1.1064085 ] Sparsity at: 0.013428782188841202 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9524e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0828197 -0.55525935 1.1066902 ] Sparsity at: 0.013428782188841202 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8869e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0828975 -0.5552798 1.1069564 ] Sparsity at: 0.013428782188841202 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8928e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0829629 -0.55528384 1.1072185 ] Sparsity at: 0.013428782188841202 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0830433 -0.5552933 1.1074861 ] Sparsity at: 0.013428782188841202 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8928e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0831338 -0.5553126 1.1077353 ] Sparsity at: 0.013428782188841202 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0832231 -0.5553386 1.1079985 ] Sparsity at: 0.013428782188841202 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.5039231081551279 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.6269931421098747 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 1.759873582266323 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 2.8531e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0833069 -0.55533326 1.108277 ] Sparsity at: 0.013428782188841202 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 2.9246e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0833925 -0.55534077 1.1085579 ] Sparsity at: 0.013428782188841202 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9167e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0834608 -0.55533946 1.1088241 ] Sparsity at: 0.013428782188841202 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9266e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.083549 -0.5553865 1.1090789 ] Sparsity at: 0.013428782188841202 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8630e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.083604 -0.5554011 1.109314 ] Sparsity at: 0.013428782188841202 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8471e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0836761 -0.555417 1.1095808 ] Sparsity at: 0.013428782188841202 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8849e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0837684 -0.55543345 1.1098304 ] Sparsity at: 0.013428782188841202 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8789e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0838306 -0.55541676 1.1100824 ] Sparsity at: 0.013428782188841202 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9027e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0839455 -0.55544156 1.1103584 ] Sparsity at: 0.013428782188841202 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0840185 -0.555456 1.1105871 ] Sparsity at: 0.013428782188841202 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8690e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0841138 -0.555458 1.1108342 ] Sparsity at: 0.013428782188841202 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0841864 -0.555461 1.1110994 ] Sparsity at: 0.013428782188841202 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0842693 -0.55549437 1.1113434 ] Sparsity at: 0.013428782188841202 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8729e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0843704 -0.55552316 1.11158 ] Sparsity at: 0.013428782188841202 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8710e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0844591 -0.55550635 1.1118448 ] Sparsity at: 0.013428782188841202 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8531e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0845243 -0.5555163 1.1120701 ] Sparsity at: 0.013428782188841202 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8292e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0846028 -0.5555445 1.1123238 ] Sparsity at: 0.013428782188841202 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0846797 -0.55554014 1.1125789 ] Sparsity at: 0.013428782188841202 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8551e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0847605 -0.55556107 1.1128119 ] Sparsity at: 0.013428782188841202 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8392e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0848389 -0.55558616 1.1130466 ] Sparsity at: 0.013428782188841202 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8869e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0849096 -0.5556088 1.1133163 ] Sparsity at: 0.013428782188841202 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8193e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0850022 -0.5556504 1.1135339 ] Sparsity at: 0.013428782188841202 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8511e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0850708 -0.55567974 1.1137956 ] Sparsity at: 0.013428782188841202 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8431e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0851513 -0.5556991 1.1140441 ] Sparsity at: 0.013428782188841202 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8272e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0852171 -0.55572456 1.1142969 ] Sparsity at: 0.013428782188841202 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8451e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0853025 -0.55574924 1.114538 ] Sparsity at: 0.013428782188841202 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8114e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0853703 -0.5557616 1.1147838 ] Sparsity at: 0.013428782188841202 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0854343 -0.5557939 1.115038 ] Sparsity at: 0.013428782188841202 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8213e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0855118 -0.5558097 1.1152811 ] Sparsity at: 0.013428782188841202 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8014e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0855944 -0.55581707 1.1155328 ] Sparsity at: 0.013428782188841202 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0856888 -0.5558424 1.1157781 ] Sparsity at: 0.013428782188841202 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8074e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0857422 -0.5558689 1.1160227 ] Sparsity at: 0.013428782188841202 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7955e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0858175 -0.5558906 1.1162298 ] Sparsity at: 0.013428782188841202 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8233e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0858923 -0.5559091 1.1164931 ] Sparsity at: 0.013428782188841202 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0859535 -0.55590755 1.1167482 ] Sparsity at: 0.013428782188841202 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8054e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0860294 -0.55596876 1.1169885 ] Sparsity at: 0.013428782188841202 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8054e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0860847 -0.55598265 1.1172296 ] Sparsity at: 0.013428782188841202 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0861596 -0.55599785 1.1174722 ] Sparsity at: 0.013428782188841202 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7994e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0862279 -0.5560078 1.1177093 ] Sparsity at: 0.013428782188841202 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7676e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.086295 -0.55604 1.117931 ] Sparsity at: 0.013428782188841202 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8074e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0863781 -0.55605066 1.1181844 ] Sparsity at: 0.013428782188841202 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7776e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0864397 -0.556062 1.1184121 ] Sparsity at: 0.013428782188841202 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0865049 -0.5560786 1.1186612 ] Sparsity at: 0.013428782188841202 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7955e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0865731 -0.55612504 1.118881 ] Sparsity at: 0.013428782188841202 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7955e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0866623 -0.5561498 1.1191235 ] Sparsity at: 0.013428782188841202 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7974e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0867125 -0.55616033 1.1193783 ] Sparsity at: 0.013428782188841202 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0867658 -0.5561871 1.1196122 ] Sparsity at: 0.013428782188841202 Epoch 398/500 235/235 [==============================] - 2s 7ms/step - loss: 2.7299e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0868374 -0.55620074 1.1198349 ] Sparsity at: 0.013428782188841202 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0869151 -0.55621296 1.1200838 ] Sparsity at: 0.013428782188841202 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7815e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.086965 -0.5562297 1.1202987 ] Sparsity at: 0.013428782188841202 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.5411585481782311 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6513711527622945 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.01965332 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 1.8498217601982532 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 1.] [1. 1. 0. ... 1. 1. 0.] ... [1. 1. 0. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 44s 7ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0870285 -0.55624634 1.1205351 ] Sparsity at: 0.013428782188841202 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 2.7835e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0871032 -0.5562805 1.1207824 ] Sparsity at: 0.013428782188841202 Epoch 403/500 235/235 [==============================] - 2s 7ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0871662 -0.5562848 1.1210102 ] Sparsity at: 0.013428782188841202 Epoch 404/500 235/235 [==============================] - 2s 7ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0872446 -0.5563179 1.1212797 ] Sparsity at: 0.013428782188841202 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.087328 -0.55631924 1.1214939 ] Sparsity at: 0.013428782188841202 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0874 -0.55635047 1.1217571 ] Sparsity at: 0.013428782188841202 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7974e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0874665 -0.5563733 1.1220063 ] Sparsity at: 0.013428782188841202 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7279e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0875151 -0.5564063 1.122216 ] Sparsity at: 0.013428782188841202 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7637e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0875914 -0.55642843 1.1224529 ] Sparsity at: 0.013428782188841202 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7200e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0876075 -0.5564342 1.1227118 ] Sparsity at: 0.013428782188841202 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7637e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0876719 -0.5564558 1.1229247 ] Sparsity at: 0.013428782188841202 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0877494 -0.5564585 1.1231625 ] Sparsity at: 0.013428782188841202 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7835e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0877979 -0.5564677 1.1233989 ] Sparsity at: 0.013428782188841202 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0878704 -0.55645484 1.1236312 ] Sparsity at: 0.013428782188841202 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0879478 -0.55649495 1.1238286 ] Sparsity at: 0.013428782188841202 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0880061 -0.55649245 1.1240873 ] Sparsity at: 0.013428782188841202 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7259e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0880781 -0.55650824 1.1242968 ] Sparsity at: 0.013428782188841202 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0881425 -0.5565371 1.1245513 ] Sparsity at: 0.013428782188841202 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7597e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0882279 -0.55654913 1.1247876 ] Sparsity at: 0.013428782188841202 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0882796 -0.5565858 1.1250209 ] Sparsity at: 0.013428782188841202 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6981e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0883331 -0.55660486 1.125241 ] Sparsity at: 0.013428782188841202 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7517e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0884043 -0.55660033 1.1254902 ] Sparsity at: 0.013428782188841202 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0884436 -0.55662143 1.1257184 ] Sparsity at: 0.013428782188841202 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7418e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0885072 -0.5566235 1.1259737 ] Sparsity at: 0.013428782188841202 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0885729 -0.55661196 1.1262157 ] Sparsity at: 0.013428782188841202 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7418e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0886418 -0.55665547 1.1264428 ] Sparsity at: 0.013428782188841202 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0887078 -0.5566548 1.1266809 ] Sparsity at: 0.013428782188841202 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0887749 -0.55664665 1.1269158 ] Sparsity at: 0.013428782188841202 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0888324 -0.556691 1.1271358 ] Sparsity at: 0.013428782188841202 Epoch 430/500 235/235 [==============================] - 2s 10ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0889066 -0.55671346 1.1273682 ] Sparsity at: 0.013428782188841202 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.088986 -0.55672926 1.1276238 ] Sparsity at: 0.013428782188841202 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0890634 -0.5567552 1.1278421 ] Sparsity at: 0.013428782188841202 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7378e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0891514 -0.55677915 1.1280792 ] Sparsity at: 0.013428782188841202 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0892091 -0.556811 1.1283112 ] Sparsity at: 0.013428782188841202 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0892348 -0.55680865 1.1285455 ] Sparsity at: 0.013428782188841202 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7279e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.089298 -0.5568379 1.1287674 ] Sparsity at: 0.013428782188841202 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0893402 -0.5568598 1.1289958 ] Sparsity at: 0.013428782188841202 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7160e-09 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0893786 -0.5568779 1.1292325 ] Sparsity at: 0.013428782188841202 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0894212 -0.5568776 1.1294671 ] Sparsity at: 0.013428782188841202 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0894994 -0.5568873 1.1296865 ] Sparsity at: 0.013428782188841202 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0895692 -0.5569252 1.1299376 ] Sparsity at: 0.013428782188841202 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7061e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0896306 -0.55694807 1.1301652 ] Sparsity at: 0.013428782188841202 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0897048 -0.5569563 1.1303707 ] Sparsity at: 0.013428782188841202 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6703e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0897541 -0.5569446 1.1306064 ] Sparsity at: 0.013428782188841202 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0898013 -0.5569555 1.1308707 ] Sparsity at: 0.013428782188841202 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0898765 -0.5569737 1.1311173 ] Sparsity at: 0.013428782188841202 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0899426 -0.5570142 1.1313438 ] Sparsity at: 0.013428782188841202 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7239e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0899928 -0.55702025 1.1315781 ] Sparsity at: 0.013428782188841202 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0900477 -0.557049 1.1318076 ] Sparsity at: 0.013428782188841202 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0901029 -0.55704075 1.1320393 ] Sparsity at: 0.013428782188841202 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0901617 -0.5570621 1.1322806 ] Sparsity at: 0.013428782188841202 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7061e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.090206 -0.55703294 1.1325065 ] Sparsity at: 0.013428782188841202 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7200e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0902579 -0.5570578 1.1327393 ] Sparsity at: 0.013428782188841202 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6663e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0903295 -0.557057 1.1329612 ] Sparsity at: 0.013428782188841202 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0903847 -0.557086 1.1332154 ] Sparsity at: 0.013428782188841202 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0904622 -0.55711716 1.1334356 ] Sparsity at: 0.013428782188841202 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0905455 -0.55715126 1.1336706 ] Sparsity at: 0.013428782188841202 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6703e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0905725 -0.5571704 1.1338882 ] Sparsity at: 0.013428782188841202 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7120e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9727 [-0.05633559 0.06754186 0.022034 ... -1.0906335 -0.5572192 1.1341232 ] Sparsity at: 0.013428782188841202 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7120e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0906909 -0.55722684 1.1343614 ] Sparsity at: 0.013428782188841202 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0907413 -0.55726063 1.1345965 ] Sparsity at: 0.013428782188841202 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6882e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9727 [-0.05633559 0.06754186 0.022034 ... -1.0907769 -0.55726975 1.1348163 ] Sparsity at: 0.013428782188841202 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7120e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9727 [-0.05633559 0.06754186 0.022034 ... -1.0908308 -0.5572906 1.1350617 ] Sparsity at: 0.013428782188841202 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7001e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0908751 -0.55730855 1.1353005 ] Sparsity at: 0.013428782188841202 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0909166 -0.5573388 1.1355215 ] Sparsity at: 0.013428782188841202 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6524e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0909685 -0.55733424 1.1357626 ] Sparsity at: 0.013428782188841202 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7200e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0910352 -0.5573588 1.1359683 ] Sparsity at: 0.013428782188841202 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9728 [-0.05633559 0.06754186 0.022034 ... -1.0911063 -0.55739665 1.1361955 ] Sparsity at: 0.013428782188841202 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6643e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0911616 -0.55743957 1.1364033 ] Sparsity at: 0.013428782188841202 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6623e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0912232 -0.5574477 1.1366552 ] Sparsity at: 0.013428782188841202 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7299e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0912917 -0.5574726 1.1368909 ] Sparsity at: 0.013428782188841202 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6604e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0913473 -0.55748147 1.137124 ] Sparsity at: 0.013428782188841202 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6743e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0914009 -0.55751425 1.137374 ] Sparsity at: 0.013428782188841202 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0914673 -0.5575143 1.1376035 ] Sparsity at: 0.013428782188841202 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6564e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0915253 -0.55752796 1.1378359 ] Sparsity at: 0.013428782188841202 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7021e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0915709 -0.55754733 1.1380644 ] Sparsity at: 0.013428782188841202 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6544e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0916281 -0.55756 1.1382915 ] Sparsity at: 0.013428782188841202 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0916711 -0.5575664 1.1385361 ] Sparsity at: 0.013428782188841202 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6743e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0917081 -0.55761594 1.1387622 ] Sparsity at: 0.013428782188841202 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6902e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.0917737 -0.5575997 1.1390038 ] Sparsity at: 0.013428782188841202 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.091804 -0.5576359 1.1392337 ] Sparsity at: 0.013428782188841202 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6425e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0918503 -0.5576337 1.139458 ] Sparsity at: 0.013428782188841202 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.091901 -0.55765975 1.1396949 ] Sparsity at: 0.013428782188841202 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6643e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9730 [-0.05633559 0.06754186 0.022034 ... -1.091977 -0.5576624 1.139935 ] Sparsity at: 0.013428782188841202 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6385e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0920218 -0.55766255 1.140163 ] Sparsity at: 0.013428782188841202 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6683e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0920823 -0.5576811 1.140378 ] Sparsity at: 0.013428782188841202 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6762e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0921499 -0.5577089 1.1406192 ] Sparsity at: 0.013428782188841202 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6643e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0921764 -0.557727 1.1408468 ] Sparsity at: 0.013428782188841202 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6266e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0922095 -0.5577249 1.1410632 ] Sparsity at: 0.013428782188841202 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0922575 -0.55775285 1.1412922 ] Sparsity at: 0.013428782188841202 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7041e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0922844 -0.5577603 1.1415203 ] Sparsity at: 0.013428782188841202 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6484e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0923324 -0.5577629 1.1417478 ] Sparsity at: 0.013428782188841202 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6941e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0923764 -0.55779433 1.1419835 ] Sparsity at: 0.013428782188841202 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6882e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0924398 -0.5578017 1.1422155 ] Sparsity at: 0.013428782188841202 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5988e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0924804 -0.5577908 1.1424385 ] Sparsity at: 0.013428782188841202 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.2980 - val_accuracy: 0.9731 [-0.05633559 0.06754186 0.022034 ... -1.0925244 -0.5578289 1.1426855 ] Sparsity at: 0.013428782188841202 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6246e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9729 [-0.05633559 0.06754186 0.022034 ... -1.0925472 -0.5578102 1.1428955 ] Sparsity at: 0.013428782188841202 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.092601 -0.5577968 1.1431425 ] Sparsity at: 0.013428782188841202 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6405e-09 - accuracy: 1.0000 - val_loss: 0.2980 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0926409 -0.5577978 1.1433743 ] Sparsity at: 0.013428782188841202 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7080e-09 - accuracy: 1.0000 - val_loss: 0.2980 - val_accuracy: 0.9732 [-0.05633559 0.06754186 0.022034 ... -1.0927055 -0.55784476 1.1436137 ] Sparsity at: 0.013428782188841202
c:\users\amir ahmed\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:2191: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
warnings.warn('`layer.add_variable` is deprecated and '
Epoch 1/500 WARNING:tensorflow:From c:\users\amir ahmed\appdata\local\programs\python\python39\lib\site-packages\tensorflow\python\ops\array_ops.py:5043: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version. Instructions for updating: The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU. 235/235 [==============================] - 6s 14ms/step - loss: 0.1395 - accuracy: 0.9788 - val_loss: 0.2117 - val_accuracy: 0.9600 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1360 - accuracy: 0.9797 - val_loss: 0.2131 - val_accuracy: 0.9610 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.1912 - val_accuracy: 0.9654 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9793 - val_loss: 0.1948 - val_accuracy: 0.9637 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9793 - val_loss: 0.2059 - val_accuracy: 0.9607 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9784 - val_loss: 0.1934 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9782 - val_loss: 0.2020 - val_accuracy: 0.9616 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.1986 - val_accuracy: 0.9619 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9787 - val_loss: 0.1840 - val_accuracy: 0.9666 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9794 - val_loss: 0.1910 - val_accuracy: 0.9645 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9797 - val_loss: 0.1829 - val_accuracy: 0.9676 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9793 - val_loss: 0.1861 - val_accuracy: 0.9662 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9786 - val_loss: 0.1899 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.1895 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9792 - val_loss: 0.1951 - val_accuracy: 0.9632 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9780 - val_loss: 0.1957 - val_accuracy: 0.9639 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9800 - val_loss: 0.1807 - val_accuracy: 0.9669 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9787 - val_loss: 0.2186 - val_accuracy: 0.9575 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9806 - val_loss: 0.1822 - val_accuracy: 0.9686 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9774 - val_loss: 0.1882 - val_accuracy: 0.9660 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9800 - val_loss: 0.2093 - val_accuracy: 0.9601 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9793 - val_loss: 0.2084 - val_accuracy: 0.9624 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9772 - val_loss: 0.1930 - val_accuracy: 0.9640 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9789 - val_loss: 0.1982 - val_accuracy: 0.9613 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9798 - val_loss: 0.1868 - val_accuracy: 0.9634 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9789 - val_loss: 0.2097 - val_accuracy: 0.9572 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1357 - accuracy: 0.9793 - val_loss: 0.1827 - val_accuracy: 0.9687 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9778 - val_loss: 0.2002 - val_accuracy: 0.9623- loss: 0.1408 - accuracy: 0.97 - ETA: 0s - loss: 0.1405 - accura [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9796 - val_loss: 0.2176 - val_accuracy: 0.9554 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9788 - val_loss: 0.1924 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9797 - val_loss: 0.2247 - val_accuracy: 0.9528 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9790 - val_loss: 0.2091 - val_accuracy: 0.9607 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.2066 - val_accuracy: 0.9606 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1370 - accuracy: 0.9797 - val_loss: 0.2188 - val_accuracy: 0.9558 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.1780 - val_accuracy: 0.9706 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9796 - val_loss: 0.1906 - val_accuracy: 0.9650 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.2348 - val_accuracy: 0.9499 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9783 - val_loss: 0.1844 - val_accuracy: 0.9666 accu - [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9789 - val_loss: 0.1793 - val_accuracy: 0.9685 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9804 - val_loss: 0.1958 - val_accuracy: 0.9621 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9782 - val_loss: 0.2201 - val_accuracy: 0.9571 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9784 - val_loss: 0.2110 - val_accuracy: 0.9581 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9795 - val_loss: 0.1959 - val_accuracy: 0.9632 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9803 - val_loss: 0.2057 - val_accuracy: 0.9604 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.1828 - val_accuracy: 0.9661 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9801 - val_loss: 0.1807 - val_accuracy: 0.9677 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9794 - val_loss: 0.2107 - val_accuracy: 0.9599 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9792 - val_loss: 0.2084 - val_accuracy: 0.9570 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9794 - val_loss: 0.2158 - val_accuracy: 0.9596 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9797 - val_loss: 0.2086 - val_accuracy: 0.9608 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9794 - val_loss: 0.2036 - val_accuracy: 0.9610 0s - loss: 0.1373 - accu [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9791 - val_loss: 0.1984 - val_accuracy: 0.9624 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9791 - val_loss: 0.2115 - val_accuracy: 0.9585 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9800 - val_loss: 0.1907 - val_accuracy: 0.9652 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9799 - val_loss: 0.1958 - val_accuracy: 0.9643 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9787 - val_loss: 0.1986 - val_accuracy: 0.9627 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9797 - val_loss: 0.2527 - val_accuracy: 0.9471 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9790 - val_loss: 0.1903 - val_accuracy: 0.9640 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9795 - val_loss: 0.1978 - val_accuracy: 0.9617 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1336 - accuracy: 0.9798 - val_loss: 0.1920 - val_accuracy: 0.9646 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9777 - val_loss: 0.1814 - val_accuracy: 0.9675 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9796 - val_loss: 0.1976 - val_accuracy: 0.9613 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9790 - val_loss: 0.1981 - val_accuracy: 0.9627 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9802 - val_loss: 0.1986 - val_accuracy: 0.9647 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1357 - accuracy: 0.9796 - val_loss: 0.1862 - val_accuracy: 0.9673 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1343 - accuracy: 0.9796 - val_loss: 0.2100 - val_accuracy: 0.9579 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1352 - accuracy: 0.9791 - val_loss: 0.1949 - val_accuracy: 0.9641 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9798 - val_loss: 0.1796 - val_accuracy: 0.9687 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9785 - val_loss: 0.1900 - val_accuracy: 0.9672 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9792 - val_loss: 0.1971 - val_accuracy: 0.9627 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 12ms/step - loss: 0.1398 - accuracy: 0.9779 - val_loss: 0.1980 - val_accuracy: 0.9617 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1342 - accuracy: 0.9794 - val_loss: 0.1928 - val_accuracy: 0.9643 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9793 - val_loss: 0.2039 - val_accuracy: 0.9602 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9796 - val_loss: 0.1788 - val_accuracy: 0.9669 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9784 - val_loss: 0.2082 - val_accuracy: 0.9607 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9791 - val_loss: 0.2080 - val_accuracy: 0.9589 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2016 - val_accuracy: 0.9635 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9794 - val_loss: 0.2169 - val_accuracy: 0.9556 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9803 - val_loss: 0.1851 - val_accuracy: 0.9658 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9797 - val_loss: 0.1866 - val_accuracy: 0.9655 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9799 - val_loss: 0.2135 - val_accuracy: 0.9584 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1392 - accuracy: 0.9787 - val_loss: 0.1973 - val_accuracy: 0.9647 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9803 - val_loss: 0.2066 - val_accuracy: 0.9596 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9789 - val_loss: 0.1986 - val_accuracy: 0.9620 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9792 - val_loss: 0.2352 - val_accuracy: 0.9531 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9793 - val_loss: 0.1940 - val_accuracy: 0.9635 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9801 - val_loss: 0.2152 - val_accuracy: 0.9580 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9797 - val_loss: 0.1929 - val_accuracy: 0.9622 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9793 - val_loss: 0.1693 - val_accuracy: 0.9704 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9797 - val_loss: 0.1905 - val_accuracy: 0.9640 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9782 - val_loss: 0.1913 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1381 - accuracy: 0.9783 - val_loss: 0.1961 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.1945 - val_accuracy: 0.9644 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9787 - val_loss: 0.2161 - val_accuracy: 0.9551 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.1824 - val_accuracy: 0.9646 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9789 - val_loss: 0.2057 - val_accuracy: 0.9594 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2050 - val_accuracy: 0.9591 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9790 - val_loss: 0.1982 - val_accuracy: 0.9648 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9785 - val_loss: 0.1734 - val_accuracy: 0.9704 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9803 - val_loss: 0.1802 - val_accuracy: 0.9666 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9787 - val_loss: 0.2176 - val_accuracy: 0.9531 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.1982 - val_accuracy: 0.9609 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1370 - accuracy: 0.9792 - val_loss: 0.2109 - val_accuracy: 0.9597 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9789 - val_loss: 0.2010 - val_accuracy: 0.9617 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1331 - accuracy: 0.9794 - val_loss: 0.2221 - val_accuracy: 0.9542 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9785 - val_loss: 0.2319 - val_accuracy: 0.9522 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9787 - val_loss: 0.2050 - val_accuracy: 0.9587 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9786 - val_loss: 0.2203 - val_accuracy: 0.9591 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9793 - val_loss: 0.1874 - val_accuracy: 0.9650 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9790 - val_loss: 0.2270 - val_accuracy: 0.9538 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9785 - val_loss: 0.2035 - val_accuracy: 0.9604 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9792 - val_loss: 0.2447 - val_accuracy: 0.9484 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9795 - val_loss: 0.1879 - val_accuracy: 0.9642 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9785 - val_loss: 0.1937 - val_accuracy: 0.9631 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9786 - val_loss: 0.2123 - val_accuracy: 0.9597 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9791 - val_loss: 0.1914 - val_accuracy: 0.9661 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9786 - val_loss: 0.1834 - val_accuracy: 0.9682 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9795 - val_loss: 0.1909 - val_accuracy: 0.9638 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9801 - val_loss: 0.1935 - val_accuracy: 0.9668 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9793 - val_loss: 0.1914 - val_accuracy: 0.9627 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9798 - val_loss: 0.1956 - val_accuracy: 0.9634 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9796 - val_loss: 0.2133 - val_accuracy: 0.9581 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2006 - val_accuracy: 0.9617 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9790 - val_loss: 0.1999 - val_accuracy: 0.9642 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9787 - val_loss: 0.1907 - val_accuracy: 0.9652 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9794 - val_loss: 0.2294 - val_accuracy: 0.9543 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9804 - val_loss: 0.1867 - val_accuracy: 0.9662 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9789 - val_loss: 0.1955 - val_accuracy: 0.9637 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9801 - val_loss: 0.1940 - val_accuracy: 0.9640 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9792 - val_loss: 0.2061 - val_accuracy: 0.9582 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2119 - val_accuracy: 0.9590 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9794 - val_loss: 0.2464 - val_accuracy: 0.9508 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9791 - val_loss: 0.2246 - val_accuracy: 0.9551 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2410 - val_accuracy: 0.9486 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9788 - val_loss: 0.1918 - val_accuracy: 0.9657 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9803 - val_loss: 0.1979 - val_accuracy: 0.9613 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9794 - val_loss: 0.2176 - val_accuracy: 0.9548 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9797 - val_loss: 0.2055 - val_accuracy: 0.9607 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9782 - val_loss: 0.1974 - val_accuracy: 0.9632 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9797 - val_loss: 0.2122 - val_accuracy: 0.9599 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.2145 - val_accuracy: 0.9595 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2237 - val_accuracy: 0.9533 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9790 - val_loss: 0.2160 - val_accuracy: 0.9571 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.1884 - val_accuracy: 0.9647 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9788 - val_loss: 0.2115 - val_accuracy: 0.9576 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1333 - accuracy: 0.9797 - val_loss: 0.1914 - val_accuracy: 0.9650 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1356 - accuracy: 0.9790 - val_loss: 0.1910 - val_accuracy: 0.9655 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9790 - val_loss: 0.1951 - val_accuracy: 0.9625 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9792 - val_loss: 0.2052 - val_accuracy: 0.9583 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9804 - val_loss: 0.1958 - val_accuracy: 0.9650 [-5.4367922e-34 -0.0000000e+00 -4.6383914e-34 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9785 - val_loss: 0.1883 - val_accuracy: 0.9637 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1314 - accuracy: 0.9805 - val_loss: 0.1924 - val_accuracy: 0.9642 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1380 - accuracy: 0.9779 - val_loss: 0.2056 - val_accuracy: 0.9574 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.1927 - val_accuracy: 0.9630 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9781 - val_loss: 0.1766 - val_accuracy: 0.9692 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2212 - val_accuracy: 0.9573 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9795 - val_loss: 0.2238 - val_accuracy: 0.9530 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9787 - val_loss: 0.1859 - val_accuracy: 0.9669 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9787 - val_loss: 0.2065 - val_accuracy: 0.9612 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2061 - val_accuracy: 0.9608 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9799 - val_loss: 0.1973 - val_accuracy: 0.9607 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1360 - accuracy: 0.9782 - val_loss: 0.1880 - val_accuracy: 0.9643 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.2178 - val_accuracy: 0.9572 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9791 - val_loss: 0.2087 - val_accuracy: 0.9588 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1348 - accuracy: 0.9787 - val_loss: 0.1915 - val_accuracy: 0.9646 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9776 - val_loss: 0.2013 - val_accuracy: 0.9617 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9791 - val_loss: 0.2132 - val_accuracy: 0.9586 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9785 - val_loss: 0.1865 - val_accuracy: 0.9669 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.2138 - val_accuracy: 0.9616 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9794 - val_loss: 0.1889 - val_accuracy: 0.9654 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9798 - val_loss: 0.2005 - val_accuracy: 0.9605 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1358 - accuracy: 0.9786 - val_loss: 0.2069 - val_accuracy: 0.9574 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.1948 - val_accuracy: 0.9617 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9796 - val_loss: 0.2269 - val_accuracy: 0.9538 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9784 - val_loss: 0.2106 - val_accuracy: 0.9590 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9798 - val_loss: 0.2000 - val_accuracy: 0.9607 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9785 - val_loss: 0.1890 - val_accuracy: 0.9638 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9784 - val_loss: 0.2133 - val_accuracy: 0.9563 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9793 - val_loss: 0.2026 - val_accuracy: 0.9627 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9801 - val_loss: 0.2089 - val_accuracy: 0.9619 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9785 - val_loss: 0.2047 - val_accuracy: 0.9588 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9797 - val_loss: 0.2117 - val_accuracy: 0.9591 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9797 - val_loss: 0.2260 - val_accuracy: 0.9538 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9779 - val_loss: 0.2070 - val_accuracy: 0.9601 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9803 - val_loss: 0.2279 - val_accuracy: 0.9542 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9780 - val_loss: 0.2226 - val_accuracy: 0.9577 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9782 - val_loss: 0.1922 - val_accuracy: 0.9640 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1326 - accuracy: 0.9797 - val_loss: 0.2036 - val_accuracy: 0.9589 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1364 - accuracy: 0.9792 - val_loss: 0.2017 - val_accuracy: 0.9614 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9796 - val_loss: 0.2047 - val_accuracy: 0.9614 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9788 - val_loss: 0.1866 - val_accuracy: 0.9645 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1340 - accuracy: 0.9793 - val_loss: 0.1964 - val_accuracy: 0.9626 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9796 - val_loss: 0.2211 - val_accuracy: 0.9558 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.2121 - val_accuracy: 0.9584 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9786 - val_loss: 0.2045 - val_accuracy: 0.9600 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9792 - val_loss: 0.2124 - val_accuracy: 0.9551 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9791 - val_loss: 0.2322 - val_accuracy: 0.9515 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9783 - val_loss: 0.1837 - val_accuracy: 0.9663 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1342 - accuracy: 0.9790 - val_loss: 0.2026 - val_accuracy: 0.9628 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9795 - val_loss: 0.2052 - val_accuracy: 0.9593 [-5.4367922e-34 -0.0000000e+00 0.0000000e+00 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9780 - val_loss: 0.1852 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9775 - val_loss: 0.1979 - val_accuracy: 0.9615 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9772 - val_loss: 0.2098 - val_accuracy: 0.9595 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9783 - val_loss: 0.1988 - val_accuracy: 0.9611 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9789 - val_loss: 0.1954 - val_accuracy: 0.9630 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9775 - val_loss: 0.1983 - val_accuracy: 0.9632 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1358 - accuracy: 0.9788 - val_loss: 0.2151 - val_accuracy: 0.9571 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9772 - val_loss: 0.1915 - val_accuracy: 0.9641 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9784 - val_loss: 0.1966 - val_accuracy: 0.9635 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.2091 - val_accuracy: 0.9572 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.2029 - val_accuracy: 0.9594 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1340 - accuracy: 0.9793 - val_loss: 0.2206 - val_accuracy: 0.9579 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1334 - accuracy: 0.9792 - val_loss: 0.1936 - val_accuracy: 0.9627 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9773 - val_loss: 0.2345 - val_accuracy: 0.9516 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9783 - val_loss: 0.2154 - val_accuracy: 0.9572 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1351 - accuracy: 0.9785 - val_loss: 0.2229 - val_accuracy: 0.9569 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1365 - accuracy: 0.9784 - val_loss: 0.2034 - val_accuracy: 0.9591 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1387 - accuracy: 0.9779 - val_loss: 0.2017 - val_accuracy: 0.9633 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9790 - val_loss: 0.2139 - val_accuracy: 0.9569 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9776 - val_loss: 0.2098 - val_accuracy: 0.9606 [ 0. -0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9789 - val_loss: 0.2290 - val_accuracy: 0.9543 [ 0. -0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1358 - accuracy: 0.9789 - val_loss: 0.2032 - val_accuracy: 0.9616 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9778 - val_loss: 0.2077 - val_accuracy: 0.9612 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1360 - accuracy: 0.9782 - val_loss: 0.2095 - val_accuracy: 0.9612 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9786 - val_loss: 0.1906 - val_accuracy: 0.9654 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9790 - val_loss: 0.2072 - val_accuracy: 0.9598 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.2066 - val_accuracy: 0.9618 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9793 - val_loss: 0.1837 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1330 - accuracy: 0.9798 - val_loss: 0.2524 - val_accuracy: 0.9484 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.1873 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1348 - accuracy: 0.9786 - val_loss: 0.2025 - val_accuracy: 0.9642 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9785 - val_loss: 0.2029 - val_accuracy: 0.9613 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9792 - val_loss: 0.2011 - val_accuracy: 0.9597 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1308 - accuracy: 0.9800 - val_loss: 0.1958 - val_accuracy: 0.9635 [ 0. -0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.2313 - val_accuracy: 0.9543 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9779 - val_loss: 0.2116 - val_accuracy: 0.9573 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9782 - val_loss: 0.1876 - val_accuracy: 0.9660 [ 0. -0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9796 - val_loss: 0.2094 - val_accuracy: 0.9603 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9783 - val_loss: 0.1939 - val_accuracy: 0.9662 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1360 - accuracy: 0.9783 - val_loss: 0.1901 - val_accuracy: 0.9657 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1337 - accuracy: 0.9790 - val_loss: 0.1977 - val_accuracy: 0.9645 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1354 - accuracy: 0.9788 - val_loss: 0.1994 - val_accuracy: 0.9623 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1324 - accuracy: 0.9799 - val_loss: 0.2134 - val_accuracy: 0.9590 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9774 - val_loss: 0.2258 - val_accuracy: 0.9548 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.1901 - val_accuracy: 0.9655 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1345 - accuracy: 0.9790 - val_loss: 0.1946 - val_accuracy: 0.9636 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9776 - val_loss: 0.2170 - val_accuracy: 0.9549: [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.1901 - val_accuracy: 0.9640 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9782 - val_loss: 0.2035 - val_accuracy: 0.9613 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9797 - val_loss: 0.2193 - val_accuracy: 0.9563 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9775 - val_loss: 0.1847 - val_accuracy: 0.9651 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1277 - accuracy: 0.9796 - val_loss: 0.1999 - val_accuracy: 0.9577 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1277 - accuracy: 0.9789 - val_loss: 0.2218 - val_accuracy: 0.9557 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1235 - accuracy: 0.9803 - val_loss: 0.1774 - val_accuracy: 0.9669 [ 0. -0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1254 - accuracy: 0.9787 - val_loss: 0.1919 - val_accuracy: 0.9618 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9804 - val_loss: 0.2154 - val_accuracy: 0.9550 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1221 - accuracy: 0.9804 - val_loss: 0.1873 - val_accuracy: 0.9597 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9793 - val_loss: 0.2011 - val_accuracy: 0.9574 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1229 - accuracy: 0.9803 - val_loss: 0.2032 - val_accuracy: 0.9583 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9811 - val_loss: 0.2152 - val_accuracy: 0.9556 [ 0. -0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1186 - accuracy: 0.9811 - val_loss: 0.1906 - val_accuracy: 0.9606 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1187 - accuracy: 0.9807 - val_loss: 0.1894 - val_accuracy: 0.9613 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1229 - accuracy: 0.9797 - val_loss: 0.1851 - val_accuracy: 0.9630 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9800 - val_loss: 0.1913 - val_accuracy: 0.9602 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9806 - val_loss: 0.2278 - val_accuracy: 0.9527 [ 0. -0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1188 - accuracy: 0.9815 - val_loss: 0.1853 - val_accuracy: 0.9636 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1190 - accuracy: 0.9804 - val_loss: 0.1908 - val_accuracy: 0.9628 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1189 - accuracy: 0.9801 - val_loss: 0.1949 - val_accuracy: 0.9619 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9801 - val_loss: 0.1949 - val_accuracy: 0.9614 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1182 - accuracy: 0.9805 - val_loss: 0.1942 - val_accuracy: 0.9634 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1189 - accuracy: 0.9801 - val_loss: 0.2298 - val_accuracy: 0.9501 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.2313 - val_accuracy: 0.9506 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1203 - accuracy: 0.9810 - val_loss: 0.1881 - val_accuracy: 0.9630 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9803 - val_loss: 0.1985 - val_accuracy: 0.9600 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9803 - val_loss: 0.1896 - val_accuracy: 0.9636 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1192 - accuracy: 0.9804 - val_loss: 0.2150 - val_accuracy: 0.9541 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1190 - accuracy: 0.9810 - val_loss: 0.1832 - val_accuracy: 0.9656 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 7s 29ms/step - loss: 0.1183 - accuracy: 0.9810 - val_loss: 0.1807 - val_accuracy: 0.9645 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 5s 21ms/step - loss: 0.1188 - accuracy: 0.9806 - val_loss: 0.2105 - val_accuracy: 0.9558 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 5s 21ms/step - loss: 0.1189 - accuracy: 0.9808 - val_loss: 0.1881 - val_accuracy: 0.9624 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1200 - accuracy: 0.9809 - val_loss: 0.1759 - val_accuracy: 0.9666 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1170 - accuracy: 0.9817 - val_loss: 0.2275 - val_accuracy: 0.9524 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1189 - accuracy: 0.9814 - val_loss: 0.1801 - val_accuracy: 0.9660 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1199 - accuracy: 0.9804 - val_loss: 0.2095 - val_accuracy: 0.9574 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1169 - accuracy: 0.9819 - val_loss: 0.2020 - val_accuracy: 0.9589 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1217 - accuracy: 0.9792 - val_loss: 0.1795 - val_accuracy: 0.9651 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1181 - accuracy: 0.9814 - val_loss: 0.1836 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1186 - accuracy: 0.9810 - val_loss: 0.1986 - val_accuracy: 0.9591 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.1948 - val_accuracy: 0.9604 [ 0. -0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1186 - accuracy: 0.9808 - val_loss: 0.1863 - val_accuracy: 0.9654 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1165 - accuracy: 0.9819 - val_loss: 0.1930 - val_accuracy: 0.9600 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1183 - accuracy: 0.9813 - val_loss: 0.1916 - val_accuracy: 0.9610 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1180 - accuracy: 0.9813 - val_loss: 0.1982 - val_accuracy: 0.9641 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1177 - accuracy: 0.9808 - val_loss: 0.2137 - val_accuracy: 0.9546 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1234 - accuracy: 0.9799 - val_loss: 0.1797 - val_accuracy: 0.9625 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1180 - accuracy: 0.9808 - val_loss: 0.1727 - val_accuracy: 0.9677 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1167 - accuracy: 0.9814 - val_loss: 0.1881 - val_accuracy: 0.9629 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1192 - accuracy: 0.9803 - val_loss: 0.2400 - val_accuracy: 0.9518 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1208 - accuracy: 0.9806 - val_loss: 0.1957 - val_accuracy: 0.9589 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1187 - accuracy: 0.9812 - val_loss: 0.1970 - val_accuracy: 0.9593 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1361 - accuracy: 0.9745 - val_loss: 0.1714 - val_accuracy: 0.9644 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1151 - accuracy: 0.9802 - val_loss: 0.1611 - val_accuracy: 0.9675 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1166 - accuracy: 0.9793 - val_loss: 0.1845 - val_accuracy: 0.9626 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1119 - accuracy: 0.9802 - val_loss: 0.1708 - val_accuracy: 0.9655 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1127 - accuracy: 0.9799 - val_loss: 0.1631 - val_accuracy: 0.9664 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1106 - accuracy: 0.9808 - val_loss: 0.1658 - val_accuracy: 0.9675 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1075 - accuracy: 0.9813 - val_loss: 0.1610 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1091 - accuracy: 0.9807 - val_loss: 0.1802 - val_accuracy: 0.9647 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1094 - accuracy: 0.9804 - val_loss: 0.1655 - val_accuracy: 0.9660 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1076 - accuracy: 0.9812 - val_loss: 0.1659 - val_accuracy: 0.9656 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1076 - accuracy: 0.9814 - val_loss: 0.1774 - val_accuracy: 0.9631 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1077 - accuracy: 0.9812 - val_loss: 0.1719 - val_accuracy: 0.9633 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1070 - accuracy: 0.9814 - val_loss: 0.1658 - val_accuracy: 0.9651 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1078 - accuracy: 0.9808 - val_loss: 0.1700 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1070 - accuracy: 0.9806 - val_loss: 0.1709 - val_accuracy: 0.9653 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1057 - accuracy: 0.9813 - val_loss: 0.1717 - val_accuracy: 0.9649 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1065 - accuracy: 0.9810 - val_loss: 0.1702 - val_accuracy: 0.9647 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1060 - accuracy: 0.9815 - val_loss: 0.1799 - val_accuracy: 0.9646 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1064 - accuracy: 0.9818 - val_loss: 0.1717 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1056 - accuracy: 0.9819 - val_loss: 0.1722 - val_accuracy: 0.9672 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1052 - accuracy: 0.9814 - val_loss: 0.1660 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1062 - accuracy: 0.9820 - val_loss: 0.1790 - val_accuracy: 0.9618 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1060 - accuracy: 0.9814 - val_loss: 0.1800 - val_accuracy: 0.9637 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9821 - val_loss: 0.1622 - val_accuracy: 0.9682 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1057 - accuracy: 0.9814 - val_loss: 0.1556 - val_accuracy: 0.9710 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1031 - accuracy: 0.9827 - val_loss: 0.1603 - val_accuracy: 0.9692 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1050 - accuracy: 0.9816 - val_loss: 0.1757 - val_accuracy: 0.9646 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1054 - accuracy: 0.9824 - val_loss: 0.1732 - val_accuracy: 0.9641 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1044 - accuracy: 0.9819 - val_loss: 0.1662 - val_accuracy: 0.9662 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1037 - accuracy: 0.9820 - val_loss: 0.1859 - val_accuracy: 0.9640 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1045 - accuracy: 0.9815 - val_loss: 0.1636 - val_accuracy: 0.9690 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1050 - accuracy: 0.9813 - val_loss: 0.1719 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1042 - accuracy: 0.9818 - val_loss: 0.1613 - val_accuracy: 0.9681 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1066 - accuracy: 0.9811 - val_loss: 0.1693 - val_accuracy: 0.9642 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1021 - accuracy: 0.9826 - val_loss: 0.1705 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1067 - accuracy: 0.9810 - val_loss: 0.1605 - val_accuracy: 0.9698 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1049 - accuracy: 0.9816 - val_loss: 0.1703 - val_accuracy: 0.9662 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1071 - accuracy: 0.9808 - val_loss: 0.1859 - val_accuracy: 0.9612 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1034 - accuracy: 0.9825 - val_loss: 0.1660 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1051 - accuracy: 0.9812 - val_loss: 0.1553 - val_accuracy: 0.9676 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1049 - accuracy: 0.9815 - val_loss: 0.1585 - val_accuracy: 0.9696 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1042 - accuracy: 0.9819 - val_loss: 0.1756 - val_accuracy: 0.9632 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9811 - val_loss: 0.1685 - val_accuracy: 0.9664 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1040 - accuracy: 0.9815 - val_loss: 0.1616 - val_accuracy: 0.9685 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9813 - val_loss: 0.1663 - val_accuracy: 0.9681 [ 0. -0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1046 - accuracy: 0.9815 - val_loss: 0.1675 - val_accuracy: 0.9666 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1024 - accuracy: 0.9823 - val_loss: 0.1896 - val_accuracy: 0.9602 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1067 - accuracy: 0.9811 - val_loss: 0.1731 - val_accuracy: 0.9615 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1044 - accuracy: 0.9818 - val_loss: 0.1635 - val_accuracy: 0.9671 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9814 - val_loss: 0.1523 - val_accuracy: 0.9710 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1362 - accuracy: 0.9720 - val_loss: 0.1650 - val_accuracy: 0.9638 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1178 - accuracy: 0.9768 - val_loss: 0.1587 - val_accuracy: 0.9653 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1160 - accuracy: 0.9766 - val_loss: 0.1568 - val_accuracy: 0.9657 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1126 - accuracy: 0.9776 - val_loss: 0.1528 - val_accuracy: 0.9671 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1132 - accuracy: 0.9778 - val_loss: 0.1548 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1101 - accuracy: 0.9779 - val_loss: 0.1540 - val_accuracy: 0.9646 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1097 - accuracy: 0.9779 - val_loss: 0.1512 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1093 - accuracy: 0.9779 - val_loss: 0.1669 - val_accuracy: 0.9620 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1087 - accuracy: 0.9785 - val_loss: 0.1608 - val_accuracy: 0.9653 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1099 - accuracy: 0.9785 - val_loss: 0.1605 - val_accuracy: 0.9637 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1081 - accuracy: 0.9779 - val_loss: 0.1570 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1076 - accuracy: 0.9785 - val_loss: 0.1501 - val_accuracy: 0.9666 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1072 - accuracy: 0.9787 - val_loss: 0.1497 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1062 - accuracy: 0.9788 - val_loss: 0.1515 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1072 - accuracy: 0.9784 - val_loss: 0.1656 - val_accuracy: 0.9627 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1071 - accuracy: 0.9785 - val_loss: 0.1524 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1071 - accuracy: 0.9785 - val_loss: 0.1558 - val_accuracy: 0.9672 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1058 - accuracy: 0.9791 - val_loss: 0.1506 - val_accuracy: 0.9670 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1057 - accuracy: 0.9792 - val_loss: 0.1547 - val_accuracy: 0.9652 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1059 - accuracy: 0.9789 - val_loss: 0.1496 - val_accuracy: 0.9677 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1052 - accuracy: 0.9789 - val_loss: 0.1590 - val_accuracy: 0.9658 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1048 - accuracy: 0.9790 - val_loss: 0.1547 - val_accuracy: 0.9675 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1058 - accuracy: 0.9784 - val_loss: 0.1532 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1041 - accuracy: 0.9792 - val_loss: 0.1537 - val_accuracy: 0.9674 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1045 - accuracy: 0.9797 - val_loss: 0.1479 - val_accuracy: 0.9682 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9792 - val_loss: 0.1506 - val_accuracy: 0.9679 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1053 - accuracy: 0.9785 - val_loss: 0.1504 - val_accuracy: 0.9671 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9786 - val_loss: 0.1489 - val_accuracy: 0.9688 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1052 - accuracy: 0.9790 - val_loss: 0.1462 - val_accuracy: 0.9692 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1046 - accuracy: 0.9786 - val_loss: 0.1476 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1048 - accuracy: 0.9786 - val_loss: 0.1531 - val_accuracy: 0.9682 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1050 - accuracy: 0.9786 - val_loss: 0.1535 - val_accuracy: 0.9675 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1059 - accuracy: 0.9785 - val_loss: 0.1501 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1052 - accuracy: 0.9785 - val_loss: 0.1494 - val_accuracy: 0.9681 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1046 - accuracy: 0.9787 - val_loss: 0.1575 - val_accuracy: 0.9643 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1047 - accuracy: 0.9789 - val_loss: 0.1474 - val_accuracy: 0.9689 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1037 - accuracy: 0.9792 - val_loss: 0.1520 - val_accuracy: 0.9682 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1049 - accuracy: 0.9786 - val_loss: 0.1404 - val_accuracy: 0.9689 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9787 - val_loss: 0.1515 - val_accuracy: 0.9671 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1046 - accuracy: 0.9786 - val_loss: 0.1563 - val_accuracy: 0.9672 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1039 - accuracy: 0.9789 - val_loss: 0.1507 - val_accuracy: 0.9672 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1059 - accuracy: 0.9787 - val_loss: 0.1484 - val_accuracy: 0.9685 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1054 - accuracy: 0.9788 - val_loss: 0.1558 - val_accuracy: 0.9648 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1055 - accuracy: 0.9794 - val_loss: 0.1510 - val_accuracy: 0.9680 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9789 - val_loss: 0.1491 - val_accuracy: 0.9674 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9789 - val_loss: 0.1451 - val_accuracy: 0.9698 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9786 - val_loss: 0.1577 - val_accuracy: 0.9672 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1037 - accuracy: 0.9794 - val_loss: 0.1479 - val_accuracy: 0.9676 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1052 - accuracy: 0.9790 - val_loss: 0.1471 - val_accuracy: 0.9692 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1041 - accuracy: 0.9786 - val_loss: 0.1506 - val_accuracy: 0.9679 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1164 - accuracy: 0.9750 - val_loss: 0.1529 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1086 - accuracy: 0.9774 - val_loss: 0.1539 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1053 - accuracy: 0.9792 - val_loss: 0.1537 - val_accuracy: 0.9660 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1043 - accuracy: 0.9784 - val_loss: 0.1516 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1047 - accuracy: 0.9784 - val_loss: 0.1550 - val_accuracy: 0.9648 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1030 - accuracy: 0.9785 - val_loss: 0.1533 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1034 - accuracy: 0.9782 - val_loss: 0.1500 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1023 - accuracy: 0.9793 - val_loss: 0.1613 - val_accuracy: 0.9641 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1021 - accuracy: 0.9789 - val_loss: 0.1517 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1016 - accuracy: 0.9787 - val_loss: 0.1610 - val_accuracy: 0.9639 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1024 - accuracy: 0.9783 - val_loss: 0.1601 - val_accuracy: 0.9643 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1032 - accuracy: 0.9781 - val_loss: 0.1639 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1018 - accuracy: 0.9788 - val_loss: 0.1631 - val_accuracy: 0.9634 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9794 - val_loss: 0.1533 - val_accuracy: 0.9657 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1016 - accuracy: 0.9785 - val_loss: 0.1592 - val_accuracy: 0.9644 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9793 - val_loss: 0.1601 - val_accuracy: 0.9637 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1022 - accuracy: 0.9786 - val_loss: 0.1642 - val_accuracy: 0.9637 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9790 - val_loss: 0.1576 - val_accuracy: 0.9649 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1019 - accuracy: 0.9785 - val_loss: 0.1521 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9789 - val_loss: 0.1509 - val_accuracy: 0.9648 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9793 - val_loss: 0.1502 - val_accuracy: 0.9666 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9793 - val_loss: 0.1512 - val_accuracy: 0.9657 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9788 - val_loss: 0.1579 - val_accuracy: 0.9638 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1008 - accuracy: 0.9790 - val_loss: 0.1547 - val_accuracy: 0.9645 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1021 - accuracy: 0.9784 - val_loss: 0.1507 - val_accuracy: 0.9652 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1020 - accuracy: 0.9787 - val_loss: 0.1558 - val_accuracy: 0.9647 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1016 - accuracy: 0.9786 - val_loss: 0.1525 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9792 - val_loss: 0.1522 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9795 - val_loss: 0.1509 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9787 - val_loss: 0.1623 - val_accuracy: 0.9641 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1015 - accuracy: 0.9781 - val_loss: 0.1563 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1005 - accuracy: 0.9789 - val_loss: 0.1473 - val_accuracy: 0.9674 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1010 - accuracy: 0.9787 - val_loss: 0.1541 - val_accuracy: 0.9654 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9793 - val_loss: 0.1511 - val_accuracy: 0.9676 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9789 - val_loss: 0.1480 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9784 - val_loss: 0.1497 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1006 - accuracy: 0.9789 - val_loss: 0.1484 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9790 - val_loss: 0.1480 - val_accuracy: 0.9670 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9793 - val_loss: 0.1526 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1014 - accuracy: 0.9789 - val_loss: 0.1486 - val_accuracy: 0.9679 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1010 - accuracy: 0.9790 - val_loss: 0.1526 - val_accuracy: 0.9652 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1009 - accuracy: 0.9786 - val_loss: 0.1555 - val_accuracy: 0.9648 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9790 - val_loss: 0.1494 - val_accuracy: 0.9663 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1001 - accuracy: 0.9793 - val_loss: 0.1487 - val_accuracy: 0.9662 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9787 - val_loss: 0.1516 - val_accuracy: 0.9664 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0993 - accuracy: 0.9794 - val_loss: 0.1587 - val_accuracy: 0.9649 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1005 - accuracy: 0.9787 - val_loss: 0.1540 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1000 - accuracy: 0.9789 - val_loss: 0.1491 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1004 - accuracy: 0.9789 - val_loss: 0.1492 - val_accuracy: 0.9657 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9787 - val_loss: 0.1671 - val_accuracy: 0.9620 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0997 - accuracy: 0.9792 - val_loss: 0.1531 - val_accuracy: 0.9656 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9783 - val_loss: 0.1519 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1007 - accuracy: 0.9789 - val_loss: 0.1538 - val_accuracy: 0.9670 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1011 - accuracy: 0.9785 - val_loss: 0.1655 - val_accuracy: 0.9647 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1013 - accuracy: 0.9786 - val_loss: 0.1494 - val_accuracy: 0.9671 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1009 - accuracy: 0.9786 - val_loss: 0.1535 - val_accuracy: 0.9656 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1002 - accuracy: 0.9789 - val_loss: 0.1498 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0998 - accuracy: 0.9785 - val_loss: 0.1426 - val_accuracy: 0.9674 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0998 - accuracy: 0.9792 - val_loss: 0.1551 - val_accuracy: 0.9656 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1001 - accuracy: 0.9782 - val_loss: 0.1569 - val_accuracy: 0.9645 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1007 - accuracy: 0.9788 - val_loss: 0.1500 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1010 - accuracy: 0.9784 - val_loss: 0.1509 - val_accuracy: 0.9655 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0992 - accuracy: 0.9796 - val_loss: 0.1526 - val_accuracy: 0.9656 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0993 - accuracy: 0.9792 - val_loss: 0.1508 - val_accuracy: 0.9658 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0999 - accuracy: 0.9790 - val_loss: 0.1463 - val_accuracy: 0.9672 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1009 - accuracy: 0.9784 - val_loss: 0.1486 - val_accuracy: 0.9678 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1013 - accuracy: 0.9786 - val_loss: 0.1501 - val_accuracy: 0.9667 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0998 - accuracy: 0.9792 - val_loss: 0.1569 - val_accuracy: 0.9648 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1008 - accuracy: 0.9788 - val_loss: 0.1552 - val_accuracy: 0.9644 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9788 - val_loss: 0.1560 - val_accuracy: 0.9654 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0998 - accuracy: 0.9793 - val_loss: 0.1557 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1006 - accuracy: 0.9788 - val_loss: 0.1528 - val_accuracy: 0.9669 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0994 - accuracy: 0.9794 - val_loss: 0.1439 - val_accuracy: 0.9681 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0996 - accuracy: 0.9791 - val_loss: 0.1489 - val_accuracy: 0.9670 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0992 - accuracy: 0.9789 - val_loss: 0.1566 - val_accuracy: 0.9648 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1008 - accuracy: 0.9783 - val_loss: 0.1530 - val_accuracy: 0.9668 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9789 - val_loss: 0.1467 - val_accuracy: 0.9652 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0991 - accuracy: 0.9793 - val_loss: 0.1531 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0987 - accuracy: 0.9797 - val_loss: 0.1595 - val_accuracy: 0.9639 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9794 - val_loss: 0.1506 - val_accuracy: 0.9664 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1003 - accuracy: 0.9792 - val_loss: 0.1562 - val_accuracy: 0.9659 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0986 - accuracy: 0.9796 - val_loss: 0.1509 - val_accuracy: 0.9676 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0985 - accuracy: 0.9801 - val_loss: 0.1523 - val_accuracy: 0.9658 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1003 - accuracy: 0.9787 - val_loss: 0.1433 - val_accuracy: 0.9677 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 4s 17ms/step - loss: 0.0994 - accuracy: 0.9790 - val_loss: 0.1585 - val_accuracy: 0.9650 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0994 - accuracy: 0.9793 - val_loss: 0.1468 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1002 - accuracy: 0.9790 - val_loss: 0.1506 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0992 - accuracy: 0.9793 - val_loss: 0.1556 - val_accuracy: 0.9644 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0995 - accuracy: 0.9790 - val_loss: 0.1580 - val_accuracy: 0.9652 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1003 - accuracy: 0.9780 - val_loss: 0.1479 - val_accuracy: 0.9681 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0991 - accuracy: 0.9786 - val_loss: 0.1511 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1004 - accuracy: 0.9788 - val_loss: 0.1567 - val_accuracy: 0.9652 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0997 - accuracy: 0.9786 - val_loss: 0.1592 - val_accuracy: 0.9657 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0993 - accuracy: 0.9790 - val_loss: 0.1451 - val_accuracy: 0.9675 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0987 - accuracy: 0.9793 - val_loss: 0.1539 - val_accuracy: 0.9669 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0986 - accuracy: 0.9793 - val_loss: 0.1507 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1014 - accuracy: 0.9784 - val_loss: 0.1533 - val_accuracy: 0.9661 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1005 - accuracy: 0.9788 - val_loss: 0.1480 - val_accuracy: 0.9665 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0991 - accuracy: 0.9790 - val_loss: 0.1547 - val_accuracy: 0.9651 [ 0. -0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0997 - accuracy: 0.9791 - val_loss: 0.1500 - val_accuracy: 0.9673 [ 0. -0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 5.1974e-04 - accuracy: 0.9999 - val_loss: 0.0854 - val_accuracy: 0.9837 [-0. 0. 0. ... -0.5530673 -0. 0. ] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3461e-04 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 0.9838 [-0. 0. 0. ... -0.5557222 0. 0. ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7866e-05 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9845 [-0. 0. 0. ... -0.55256295 -0. 0. ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7650e-05 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.5550569 -0. 0. ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1267e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9843 [-0. 0. 0. ... -0.5589374 0. 0. ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 15ms/step - loss: 2.2912e-05 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9843 [-0. 0. 0. ... -0.55955464 0. 0. ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6023e-05 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 0.9846 [-0. 0. 0. ... -0.56160337 -0. -0. ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5238e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.5624639 -0. 0. ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1368e-04 - accuracy: 0.9999 - val_loss: 0.0924 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.566357 -0. 0. ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 4s 15ms/step - loss: 5.9115e-04 - accuracy: 0.9999 - val_loss: 0.0912 - val_accuracy: 0.9831 [-0. 0. 0. ... -0.5548585 0. 0. ] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 15ms/step - loss: 1.8525e-04 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9842 [-0. 0. 0. ... -0.5577729 -0. 0. ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2232e-04 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9844 [-0. 0. 0. ... -0.5702418 -0. -0. ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 4s 15ms/step - loss: 2.1000e-04 - accuracy: 0.9999 - val_loss: 0.0877 - val_accuracy: 0.9850 [-0. 0. 0. ... -0.573148 0. 0. ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 15ms/step - loss: 3.5954e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9848 [-0. 0. 0. ... -0.5758063 0. 0. ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 15ms/step - loss: 1.8773e-05 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9847 [-0. 0. 0. ... -0.576613 0. 0. ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 15ms/step - loss: 2.2774e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9845 [-0. 0. 0. ... -0.57610166 0. 0. ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3874e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9847 [-0. 0. 0. ... -0.5776166 0. 0. ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0020e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9845 [-0. 0. 0. ... -0.57820547 -0. 0. ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 15ms/step - loss: 9.4196e-06 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9849 [-0. 0. 0. ... -0.5780573 0. -0. ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 15ms/step - loss: 8.8427e-06 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9849 [-0. 0. 0. ... -0.57824266 0. 0. ] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 15ms/step - loss: 6.8982e-06 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9851 [-0. 0. 0. ... -0.57917047 0. -0. ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 15ms/step - loss: 6.5843e-06 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9851 [-0. 0. 0. ... -0.5796964 0. -0. ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 15ms/step - loss: 6.1186e-06 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9853 [-0. 0. 0. ... -0.5801737 0. -0. ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6667e-06 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9850 [-0. 0. 0. ... -0.58366317 -0. -0. ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2026e-06 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9848 [-0. 0. 0. ... -0.5863896 0. 0. ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1699e-06 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9851 [-0. 0. 0. ... -0.59061885 0. -0. ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8785e-06 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9846 [-0. 0. 0. ... -0.5899389 0. 0. ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8006e-04 - accuracy: 0.9998 - val_loss: 0.1048 - val_accuracy: 0.9825 [-0. 0. 0. ... -0.596239 0. -0. ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0994 - val_accuracy: 0.9826 [-0. 0. 0. ... -0.6218895 0. -0. ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 15ms/step - loss: 5.5281e-04 - accuracy: 0.9998 - val_loss: 0.1035 - val_accuracy: 0.9829 [-0. 0. 0. ... -0.6150985 0. -0. ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0528e-04 - accuracy: 0.9999 - val_loss: 0.1016 - val_accuracy: 0.9834 [-0. 0. 0. ... -0.61611503 0. -0. ] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7312e-05 - accuracy: 1.0000 - val_loss: 0.0994 - val_accuracy: 0.9848 [-0. 0. 0. ... -0.61966676 0. -0. ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5500e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9845 [-0. 0. 0. ... -0.61863166 0. -0. ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5313e-05 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9844 [-0. 0. 0. ... -0.61932284 0. -0. ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1399e-05 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9838 [-0. 0. 0. ... -0.620924 -0. 0. ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9560e-04 - accuracy: 0.9999 - val_loss: 0.0949 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.59334975 0. 0. ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6057e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9856 [-0. 0. 0. ... -0.594901 0. 0. ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7859e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9857 [-0. 0. 0. ... -0.596771 0. 0. ] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2497e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9856 [-0. 0. 0. ... -0.597793 0. -0. ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0424e-05 - accuracy: 1.0000 - val_loss: 0.0913 - val_accuracy: 0.9855 [-0. 0. 0. ... -0.599311 0. -0. ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0230e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9849 [-0. 0. 0. ... -0.6023537 0. -0. ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0588e-04 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.6026897 0. 0. ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0518e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9853 [-0. 0. 0. ... -0.6037611 0. 0. ] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6980e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9857 [-0. 0. 0. ... -0.6053772 0. -0. ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 15ms/step - loss: 8.5979e-06 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9855 [-0. 0. 0. ... -0.6063888 0. 0. ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4573e-04 - accuracy: 0.9999 - val_loss: 0.1013 - val_accuracy: 0.9832 [-0. 0. 0. ... -0.60614705 -0. -0. ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 15ms/step - loss: 7.2224e-04 - accuracy: 0.9998 - val_loss: 0.1034 - val_accuracy: 0.9844 [-0. 0. 0. ... -0.6232613 0. -0. ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1091e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9843 [-0. 0. 0. ... -0.6366796 -0. -0. ] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 15ms/step - loss: 2.2341e-04 - accuracy: 0.9999 - val_loss: 0.1036 - val_accuracy: 0.9846 [-0. 0. 0. ... -0.6416714 0. -0. ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6020e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9848 [-0. 0. 0. ... -0.64035046 -0. 0. ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0037 - accuracy: 0.9987 - val_loss: 0.0997 - val_accuracy: 0.9821 [-0. 0. 0. ... -0.67721903 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 15ms/step - loss: 6.5882e-04 - accuracy: 0.9998 - val_loss: 0.0936 - val_accuracy: 0.9828 [-0. 0. 0. ... -0.6792987 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1968e-04 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9835 [-0. 0. 0. ... -0.6801244 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3523e-04 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.68185675 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0870e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.6818952 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9041e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9842 [-0. 0. 0. ... -0.67859495 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 15ms/step - loss: 5.1245e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9843 [-0. 0. 0. ... -0.68226415 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9081e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9842 [-0. 0. 0. ... -0.6848408 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4416e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.6854666 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2600e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9844 [-0. 0. 0. ... -0.6891201 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5683e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.6894046 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0525e-05 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 0.9845 [-0. 0. 0. ... -0.68635917 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0031e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9845 [-0. 0. 0. ... -0.6868398 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 15ms/step - loss: 2.8269e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9842 [-0. 0. 0. ... -0.6877827 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4622e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9844 [-0. 0. 0. ... -0.68857604 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2785e-04 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9842 [-0. 0. 0. ... -0.6875967 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3307e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9846 [-0. 0. 0. ... -0.68757784 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 4s 15ms/step - loss: 3.8052e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9846 [-0. 0. 0. ... -0.6912779 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5671e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.6941038 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5500e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9843 [-0. 0. 0. ... -0.69580317 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5026e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.69657964 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1558e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.6998479 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0486e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.7010317 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3231e-05 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.7006972 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3321e-05 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.70243347 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2470e-05 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.7050213 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7761e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.6966571 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 15ms/step - loss: 8.4831e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9838 [-0. 0. 0. ... -0.6981817 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 15ms/step - loss: 6.8140e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9838 [-0. 0. 0. ... -0.70088047 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 15ms/step - loss: 7.0180e-06 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.70243925 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 15ms/step - loss: 5.3528e-06 - accuracy: 1.0000 - val_loss: 0.0966 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.70277315 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 15ms/step - loss: 4.6254e-06 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9843 [-0. 0. 0. ... -0.70333785 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 15ms/step - loss: 4.0172e-06 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.7046567 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8390e-06 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.70542276 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0155e-06 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9842 [-0. 0. 0. ... -0.7067089 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7225e-06 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.7080183 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7384e-06 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.7082352 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3948e-06 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.71089333 -0. -0. ] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 4s 17ms/step - loss: 2.6843e-06 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.7126137 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 4s 16ms/step - loss: 2.2646e-06 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 0.9841 [-0. 0. 0. ... -0.7171528 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 15ms/step - loss: 4.2773e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9838 [-0. 0. 0. ... -0.7182911 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3908e-04 - accuracy: 0.9999 - val_loss: 0.1217 - val_accuracy: 0.9824 [-0. 0. 0. ... -0.69221926 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 0.1233 - val_accuracy: 0.9816 [-0. 0. 0. ... -0.69016886 -0. -0. ] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6124e-04 - accuracy: 0.9999 - val_loss: 0.1117 - val_accuracy: 0.9831 [-0. 0. 0. ... -0.6866151 -0. 0. ] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3847e-05 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9832 [-0. 0. 0. ... -0.6937081 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8299e-04 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9836 [-0. 0. 0. ... -0.6930453 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8104e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9837 [-0. 0. 0. ... -0.69321835 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6209e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9837 [-0. 0. 0. ... -0.6935785 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 4s 15ms/step - loss: 7.9636e-06 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9839 [-0. 0. 0. ... -0.6936766 0. -0. ] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1896e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9840 [-0. 0. 0. ... -0.69357926 0. 0. ] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0112 - accuracy: 0.9967 - val_loss: 0.1107 - val_accuracy: 0.9801 [-0. 0. 0. ... -0.7407761 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1084 - val_accuracy: 0.9820 [-0. 0. 0. ... -0.7365223 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8578e-04 - accuracy: 0.9999 - val_loss: 0.1059 - val_accuracy: 0.9818 [-0. 0. 0. ... -0.735703 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4773e-04 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9817 [-0. 0. 0. ... -0.73411673 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0118e-04 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9819 [-0. 0. 0. ... -0.73429626 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 4s 15ms/step - loss: 1.6714e-04 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9824 [-0. 0. 0. ... -0.73607904 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 12ms/step - loss: 1.6384e-04 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9819 [-0. 0. 0. ... -0.7380855 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2587e-04 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9819 [-0. 0. 0. ... -0.73282015 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2742e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9821 [-0. 0. 0. ... -0.7321297 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 9.9209e-05 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9818 [-0. 0. 0. ... -0.7345622 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1317e-04 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9823 [-0. 0. 0. ... -0.7313788 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 15ms/step - loss: 9.8276e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9822 [-0. 0. 0. ... -0.736128 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 4s 15ms/step - loss: 6.5150e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9823 [-0. 0. 0. ... -0.73811364 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 15ms/step - loss: 6.3891e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9822 [-0. 0. 0. ... -0.7404297 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 4s 15ms/step - loss: 5.6796e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9823 [-0. 0. 0. ... -0.7394241 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 15ms/step - loss: 4.5055e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9824 [-0. 0. 0. ... -0.73982906 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 15ms/step - loss: 4.0964e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9826 [-0. 0. 0. ... -0.7416686 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 15ms/step - loss: 4.2198e-05 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9826 [-0. 0. 0. ... -0.74385947 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 15ms/step - loss: 5.9155e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9826 [-0. 0. 0. ... -0.74229836 0. -0. ] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 15ms/step - loss: 3.7107e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9827 [-0. 0. 0. ... -0.7412203 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 15ms/step - loss: 4.2197e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9832 [-0. 0. 0. ... -0.74157673 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 15ms/step - loss: 3.5201e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.74494153 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9503e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9829 [-0. 0. 0. ... -0.74752736 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0816e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9827 [-0. 0. 0. ... -0.7458942 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7131e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9825 [-0. 0. 0. ... -0.74778545 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9934e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.7491825 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6654e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9831 [-0. 0. 0. ... -0.7542403 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4967e-05 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.7554976 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4305e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9834 [-0. 0. 0. ... -0.7567567 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 15ms/step - loss: 8.9592e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9827 [-0. 0. 0. ... -0.75846106 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2192e-04 - accuracy: 0.9999 - val_loss: 0.1172 - val_accuracy: 0.9822 [-0. 0. 0. ... -0.7628433 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0997e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9828 [-0. 0. 0. ... -0.79670596 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8630e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9828 [-0. 0. 0. ... -0.7897121 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0365e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.7965117 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4366e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.8002411 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2616e-05 - accuracy: 1.0000 - val_loss: 0.1143 - val_accuracy: 0.9826 [-0. 0. 0. ... -0.8029306 0. -0. ] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1815e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9835 [-0. 0. 0. ... -0.80553263 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8557e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9833 [-0. 0. 0. ... -0.8039424 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6699e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9831 [-0. 0. 0. ... -0.8008044 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2167e-05 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9836 [-0. 0. 0. ... -0.8060011 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3413e-05 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9833 [-0. 0. 0. ... -0.8096799 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 15ms/step - loss: 5.2246e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.8090754 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3822e-05 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9833 [-0. 0. 0. ... -0.8169751 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 15ms/step - loss: 8.3490e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9830 [-0. 0. 0. ... -0.8187563 -0. 0. ] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0631e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9833 [-0. 0. 0. ... -0.8198522 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 15ms/step - loss: 5.9744e-06 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9833 [-0. 0. 0. ... -0.8309273 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 4s 15ms/step - loss: 6.7767e-06 - accuracy: 1.0000 - val_loss: 0.1201 - val_accuracy: 0.9835 [-0. 0. 0. ... -0.82163715 0. -0. ] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 15ms/step - loss: 5.7396e-06 - accuracy: 1.0000 - val_loss: 0.1197 - val_accuracy: 0.9838 [-0. 0. 0. ... -0.824797 0. -0. ] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 15ms/step - loss: 5.8046e-06 - accuracy: 1.0000 - val_loss: 0.1192 - val_accuracy: 0.9837 [-0. 0. 0. ... -0.82850313 0. 0. ] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 15ms/step - loss: 3.5680e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9836 [-0. 0. 0. ... -0.82699186 -0. -0. ] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0254 - accuracy: 0.9930 - val_loss: 0.1070 - val_accuracy: 0.9787 [-0. 0. 0. ... -0.8346981 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0037 - accuracy: 0.9991 - val_loss: 0.1085 - val_accuracy: 0.9790 [-0. 0. 0. ... -0.81628245 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0019 - accuracy: 0.9997 - val_loss: 0.1075 - val_accuracy: 0.9795 [-0. 0. 0. ... -0.8310971 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1084 - val_accuracy: 0.9801 [-0. 0. 0. ... -0.82721215 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9799 [-0. 0. 0. ... -0.8300648 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6709e-04 - accuracy: 0.9999 - val_loss: 0.1077 - val_accuracy: 0.9799 [-0. 0. 0. ... -0.8373513 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4875e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9797 [-0. 0. 0. ... -0.8430129 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8255e-04 - accuracy: 0.9999 - val_loss: 0.1072 - val_accuracy: 0.9805 [-0. 0. 0. ... -0.84878075 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 15ms/step - loss: 4.3607e-04 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9802 [-0. 0. 0. ... -0.850467 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7904e-04 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9800 [-0. 0. 0. ... -0.8549605 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3765e-04 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9805 [-0. 0. 0. ... -0.85776556 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7837e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9805 [-0. 0. 0. ... -0.8604504 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5326e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9809 [-0. 0. 0. ... -0.8676103 -0. -0. ] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3964e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9811 [-0. 0. 0. ... -0.87280065 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9696e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9803 [-0. 0. 0. ... -0.88051254 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7303e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9810 [-0. 0. 0. ... -0.88204795 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5516e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9814 [-0. 0. 0. ... -0.88364357 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7535e-04 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9814 [-0. 0. 0. ... -0.8959071 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2331e-04 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9811 [-0. 0. 0. ... -0.9009514 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2230e-04 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9810 [-0. 0. 0. ... -0.90102774 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0146e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9808 [-0. 0. 0. ... -0.9089422 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 4s 16ms/step - loss: 9.0634e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9810 [-0. 0. 0. ... -0.913678 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 15ms/step - loss: 7.7731e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9820 [-0. 0. 0. ... -0.9183778 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 15ms/step - loss: 7.6318e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9816 [-0. 0. 0. ... -0.92362833 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8667e-05 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9814 [-0. 0. 0. ... -0.93130267 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 15ms/step - loss: 5.6826e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9813 [-0. 0. 0. ... -0.9367621 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 15ms/step - loss: 9.4301e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9812 [-0. 0. 0. ... -0.94127256 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2516e-04 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9817 [-0. 0. 0. ... -0.9542083 0. -0. ] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8966e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9817 [-0. 0. 0. ... -0.98382086 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 15ms/step - loss: 9.0313e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9818 [-0. 0. 0. ... -0.97958344 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 15ms/step - loss: 8.7264e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9823 [-0. 0. 0. ... -0.99096423 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3924e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9818 [-0. 0. 0. ... -1.0028114 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 15ms/step - loss: 3.4515e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9818 [-0. 0. 0. ... -1.0061536 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 4s 15ms/step - loss: 2.7056e-05 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9816 [-0. 0. 0. ... -1.0080017 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7346e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9816 [-0. 0. 0. ... -1.0091317 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4549e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9818 [-0. 0. 0. ... -1.0140857 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 15ms/step - loss: 3.6826e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9811 [-0. 0. 0. ... -1.0208602 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5902e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9814 [-0. 0. 0. ... -1.0266346 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8659e-05 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9813 [-0. 0. 0. ... -1.0272093 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8641e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9817 [-0. 0. 0. ... -1.0217968 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8179e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9815 [-0. 0. 0. ... -1.0300295 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2688e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9817 [-0. 0. 0. ... -1.038247 0. -0. ] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5018e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9811 [-0. 0. 0. ... -1.0419943 0. -0. ] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7878e-05 - accuracy: 1.0000 - val_loss: 0.1295 - val_accuracy: 0.9820 [-0. 0. 0. ... -1.033658 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7058e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9817 [-0. 0. 0. ... -1.0435646 0. -0. ] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2595e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9819 [-0. 0. 0. ... -1.0443585 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3152e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9820 [-0. 0. 0. ... -1.0500063 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 9.0549e-06 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9816 [-0. 0. 0. ... -1.0589355 0. -0. ] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4352e-05 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9814 [-0. 0. 0. ... -1.0576293 -0. 0. ] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1861e-05 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9815 [-0. 0. 0. ... -1.0616847 0. 0. ] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0728 - accuracy: 0.9818 - val_loss: 0.1485 - val_accuracy: 0.9743 [-0. 0. 0. ... -1.129212 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0229 - accuracy: 0.9934 - val_loss: 0.1385 - val_accuracy: 0.9755 [-0. 0. 0. ... -1.141127 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0150 - accuracy: 0.9955 - val_loss: 0.1333 - val_accuracy: 0.9764 [-0. 0. 0. ... -1.1500582 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0114 - accuracy: 0.9966 - val_loss: 0.1313 - val_accuracy: 0.9772 [-0. 0. 0. ... -1.1486633 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.1299 - val_accuracy: 0.9771 [-0. 0. 0. ... -1.1617391 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0068 - accuracy: 0.9981 - val_loss: 0.1289 - val_accuracy: 0.9771 [-0. 0. 0. ... -1.162172 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.1293 - val_accuracy: 0.9772 [-0. 0. 0. ... -1.1651667 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 0.1292 - val_accuracy: 0.9779 [-0. 0. 0. ... -1.1716433 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0039 - accuracy: 0.9992 - val_loss: 0.1289 - val_accuracy: 0.9777 [-0. 0. 0. ... -1.1742398 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0035 - accuracy: 0.9995 - val_loss: 0.1304 - val_accuracy: 0.9776 [-0. 0. 0. ... -1.1771548 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1310 - val_accuracy: 0.9779 [-0. 0. 0. ... -1.1768707 -0. 0. ] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 0.1314 - val_accuracy: 0.9782 [-0. 0. 0. ... -1.1873479 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0023 - accuracy: 0.9998 - val_loss: 0.1316 - val_accuracy: 0.9780 [-0. 0. 0. ... -1.1942216 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.1347 - val_accuracy: 0.9780 [-0. 0. 0. ... -1.1818326 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 0.9999 - val_loss: 0.1334 - val_accuracy: 0.9779 [-0. 0. 0. ... -1.1871382 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1354 - val_accuracy: 0.9773 [-0. 0. 0. ... -1.1945542 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1370 - val_accuracy: 0.9780 [-0. 0. 0. ... -1.2026184 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1372 - val_accuracy: 0.9773 [-0. 0. 0. ... -1.2075963 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.1379 - val_accuracy: 0.9777 [-0. 0. 0. ... -1.2079312 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9778 [-0. 0. 0. ... -1.215596 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7103e-04 - accuracy: 0.9999 - val_loss: 0.1395 - val_accuracy: 0.9780 [-0. 0. 0. ... -1.2153323 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5660e-04 - accuracy: 0.9999 - val_loss: 0.1423 - val_accuracy: 0.9778 [-0. 0. 0. ... -1.2166299 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5472e-04 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9781 [-0. 0. 0. ... -1.2306023 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3086e-04 - accuracy: 0.9999 - val_loss: 0.1429 - val_accuracy: 0.9777 [-0. 0. 0. ... -1.2321162 -0. 0. ] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 15ms/step - loss: 5.9676e-04 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9779 [-0. 0. 0. ... -1.2486132 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 15ms/step - loss: 6.1295e-04 - accuracy: 0.9999 - val_loss: 0.1448 - val_accuracy: 0.9782 [-0. 0. 0. ... -1.2411742 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 15ms/step - loss: 5.7922e-04 - accuracy: 0.9999 - val_loss: 0.1462 - val_accuracy: 0.9779 [-0. 0. 0. ... -1.2460974 -0. -0. ] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7296e-04 - accuracy: 1.0000 - val_loss: 0.1477 - val_accuracy: 0.9784 [-0. 0. 0. ... -1.2592012 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 15ms/step - loss: 3.9452e-04 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9784 [-0. 0. 0. ... -1.2589724 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3251e-04 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9786 [-0. 0. 0. ... -1.2649356 -0. 0. ] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5737e-04 - accuracy: 1.0000 - val_loss: 0.1518 - val_accuracy: 0.9778 [-0. 0. 0. ... -1.2751635 -0. 0. ] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9689e-04 - accuracy: 1.0000 - val_loss: 0.1528 - val_accuracy: 0.9782 [-0. 0. 0. ... -1.2827413 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6009e-04 - accuracy: 1.0000 - val_loss: 0.1531 - val_accuracy: 0.9782 [-0. 0. 0. ... -1.2864949 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6719e-04 - accuracy: 1.0000 - val_loss: 0.1536 - val_accuracy: 0.9780 [-0. 0. 0. ... -1.2937901 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6804e-04 - accuracy: 1.0000 - val_loss: 0.1568 - val_accuracy: 0.9786 [-0. 0. 0. ... -1.2982243 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4089e-04 - accuracy: 1.0000 - val_loss: 0.1594 - val_accuracy: 0.9781 [-0. 0. 0. ... -1.306254 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9973e-04 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9786 [-0. 0. 0. ... -1.3152028 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6836e-04 - accuracy: 1.0000 - val_loss: 0.1610 - val_accuracy: 0.9779 [-0. 0. 0. ... -1.3103464 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 15ms/step - loss: 3.3480e-04 - accuracy: 1.0000 - val_loss: 0.1620 - val_accuracy: 0.9782 [-0. 0. 0. ... -1.3056033 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0301e-04 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9783 [-0. 0. 0. ... -1.3112738 -0. -0. ] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8167e-04 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9787 [-0. 0. 0. ... -1.315901 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 15ms/step - loss: 1.9941e-04 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9780 [-0. 0. 0. ... -1.3305752 -0. 0. ] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4542e-04 - accuracy: 0.9999 - val_loss: 0.1661 - val_accuracy: 0.9790 [-0. 0. 0. ... -1.3301604 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5855e-04 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9789 [-0. 0. 0. ... -1.3397722 0. -0. ] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3761e-04 - accuracy: 0.9999 - val_loss: 0.1672 - val_accuracy: 0.9794 [-0. 0. 0. ... -1.3380265 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7658e-04 - accuracy: 1.0000 - val_loss: 0.1695 - val_accuracy: 0.9788 [-0. 0. 0. ... -1.3485799 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6581e-04 - accuracy: 1.0000 - val_loss: 0.1719 - val_accuracy: 0.9781 [-0. 0. 0. ... -1.3614453 -0. -0. ] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3767e-04 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9783 [-0. 0. 0. ... -1.3544908 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0582e-04 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9792 [-0. 0. 0. ... -1.369297 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1617e-04 - accuracy: 1.0000 - val_loss: 0.1714 - val_accuracy: 0.9794 [-0. 0. 0. ... -1.3622464 0. 0. ] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2157 - accuracy: 0.9503 - val_loss: 0.2026 - val_accuracy: 0.9583 [-0. 0. 0. ... -1.1765113 0. -0. ] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0963 - accuracy: 0.9728 - val_loss: 0.1745 - val_accuracy: 0.9629 [-0. 0. 0. ... -1.1249127 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0774 - accuracy: 0.9765 - val_loss: 0.1622 - val_accuracy: 0.9646 [-0. 0. 0. ... -1.0870408 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0657 - accuracy: 0.9798 - val_loss: 0.1542 - val_accuracy: 0.9654 [-0. 0. 0. ... -1.0596551 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0576 - accuracy: 0.9818 - val_loss: 0.1481 - val_accuracy: 0.9661 [-0. 0. 0. ... -1.0405695 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0526 - accuracy: 0.9832 - val_loss: 0.1440 - val_accuracy: 0.9659 [-0. 0. 0. ... -1.0240899 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0477 - accuracy: 0.9850 - val_loss: 0.1404 - val_accuracy: 0.9670 [-0. 0. 0. ... -1.012937 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0444 - accuracy: 0.9857 - val_loss: 0.1375 - val_accuracy: 0.9673 [-0. 0. 0. ... -1.004185 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0413 - accuracy: 0.9867 - val_loss: 0.1361 - val_accuracy: 0.9682 [-0. 0. 0. ... -0.9975208 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0385 - accuracy: 0.9875 - val_loss: 0.1345 - val_accuracy: 0.9686 [-0. 0. 0. ... -0.99195725 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0362 - accuracy: 0.9879 - val_loss: 0.1330 - val_accuracy: 0.9694 [-0. 0. 0. ... -0.98365176 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0340 - accuracy: 0.9887 - val_loss: 0.1320 - val_accuracy: 0.9695 [-0. 0. 0. ... -0.9805692 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0318 - accuracy: 0.9897 - val_loss: 0.1322 - val_accuracy: 0.9696 [-0. 0. 0. ... -0.9754846 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.1315 - val_accuracy: 0.9695 [-0. 0. 0. ... -0.9749589 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0286 - accuracy: 0.9908 - val_loss: 0.1324 - val_accuracy: 0.9700 [-0. 0. 0. ... -0.9709703 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0271 - accuracy: 0.9914 - val_loss: 0.1319 - val_accuracy: 0.9702 [-0. 0. 0. ... -0.96949524 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0257 - accuracy: 0.9919 - val_loss: 0.1320 - val_accuracy: 0.9698 [-0. 0. 0. ... -0.9702149 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0245 - accuracy: 0.9924 - val_loss: 0.1325 - val_accuracy: 0.9699 [-0. 0. 0. ... -0.9675595 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0232 - accuracy: 0.9927 - val_loss: 0.1324 - val_accuracy: 0.9694 [-0. 0. 0. ... -0.9705282 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0226 - accuracy: 0.9933 - val_loss: 0.1337 - val_accuracy: 0.9695 [-0. 0. 0. ... -0.97022223 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0217 - accuracy: 0.9935 - val_loss: 0.1337 - val_accuracy: 0.9705 [-0. 0. 0. ... -0.97583264 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0201 - accuracy: 0.9939 - val_loss: 0.1349 - val_accuracy: 0.9703 [-0. 0. 0. ... -0.97692347 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0190 - accuracy: 0.9946 - val_loss: 0.1358 - val_accuracy: 0.9701 [-0. 0. 0. ... -0.9779637 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0181 - accuracy: 0.9950 - val_loss: 0.1379 - val_accuracy: 0.9707 [-0. 0. 0. ... -0.9833529 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0171 - accuracy: 0.9953 - val_loss: 0.1386 - val_accuracy: 0.9708 [-0. 0. 0. ... -0.9822294 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0169 - accuracy: 0.9952 - val_loss: 0.1389 - val_accuracy: 0.9702 [-0. 0. 0. ... -0.98926944 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0159 - accuracy: 0.9953 - val_loss: 0.1395 - val_accuracy: 0.9709 [-0. 0. 0. ... -0.99106526 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0151 - accuracy: 0.9957 - val_loss: 0.1406 - val_accuracy: 0.9713 [-0. 0. 0. ... -0.99646693 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0145 - accuracy: 0.9960 - val_loss: 0.1443 - val_accuracy: 0.9708 [-0. 0. 0. ... -0.9926664 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0144 - accuracy: 0.9957 - val_loss: 0.1456 - val_accuracy: 0.9701 [-0. 0. 0. ... -0.99850523 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0132 - accuracy: 0.9963 - val_loss: 0.1454 - val_accuracy: 0.9707 [-0. 0. 0. ... -1.000106 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0128 - accuracy: 0.9966 - val_loss: 0.1471 - val_accuracy: 0.9707 [-0. 0. 0. ... -1.0031863 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0122 - accuracy: 0.9967 - val_loss: 0.1478 - val_accuracy: 0.9716 [-0. 0. 0. ... -1.0152969 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0119 - accuracy: 0.9970 - val_loss: 0.1494 - val_accuracy: 0.9710 [-0. 0. 0. ... -1.0244104 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0109 - accuracy: 0.9973 - val_loss: 0.1500 - val_accuracy: 0.9712 [-0. 0. 0. ... -1.0313693 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0105 - accuracy: 0.9974 - val_loss: 0.1533 - val_accuracy: 0.9713 [-0. 0. 0. ... -1.0344816 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0100 - accuracy: 0.9975 - val_loss: 0.1542 - val_accuracy: 0.9707 [-0. 0. 0. ... -1.0482715 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9976 - val_loss: 0.1547 - val_accuracy: 0.9711 [-0. 0. 0. ... -1.0522327 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.1592 - val_accuracy: 0.9706 [-0. 0. 0. ... -1.0525693 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9977 - val_loss: 0.1596 - val_accuracy: 0.9710 [-0. 0. 0. ... -1.0575668 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0085 - accuracy: 0.9981 - val_loss: 0.1610 - val_accuracy: 0.9710 [-0. 0. 0. ... -1.0626106 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.1624 - val_accuracy: 0.9708 [-0. 0. 0. ... -1.0641626 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 0.1634 - val_accuracy: 0.9703 [-0. 0. 0. ... -1.0819227 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0080 - accuracy: 0.9981 - val_loss: 0.1662 - val_accuracy: 0.9713 [-0. 0. 0. ... -1.0779172 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9985 - val_loss: 0.1666 - val_accuracy: 0.9714 [-0. 0. 0. ... -1.0790343 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0070 - accuracy: 0.9985 - val_loss: 0.1684 - val_accuracy: 0.9709 [-0. 0. 0. ... -1.0932534 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9982 - val_loss: 0.1711 - val_accuracy: 0.9712 [-0. 0. 0. ... -1.1132145 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0068 - accuracy: 0.9984 - val_loss: 0.1719 - val_accuracy: 0.9713 [-0. 0. 0. ... -1.1124408 -0. 0. ] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0065 - accuracy: 0.9987 - val_loss: 0.1731 - val_accuracy: 0.9709 [-0. 0. 0. ... -1.1116756 0. -0. ] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0063 - accuracy: 0.9988 - val_loss: 0.1750 - val_accuracy: 0.9708 [-0. 0. 0. ... -1.1213894 0. 0. ] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7795 - accuracy: 0.8075 - val_loss: 0.4690 - val_accuracy: 0.8729 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.3774 - accuracy: 0.8888 - val_loss: 0.3781 - val_accuracy: 0.8978 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 15ms/step - loss: 0.3219 - accuracy: 0.9035 - val_loss: 0.3401 - val_accuracy: 0.9076 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2929 - accuracy: 0.9118 - val_loss: 0.3173 - val_accuracy: 0.9134 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2714 - accuracy: 0.9186 - val_loss: 0.2997 - val_accuracy: 0.9182 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2562 - accuracy: 0.9226 - val_loss: 0.2870 - val_accuracy: 0.9217 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2441 - accuracy: 0.9268 - val_loss: 0.2752 - val_accuracy: 0.9249 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2334 - accuracy: 0.9300 - val_loss: 0.2652 - val_accuracy: 0.9273 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2243 - accuracy: 0.9318 - val_loss: 0.2572 - val_accuracy: 0.9290 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2168 - accuracy: 0.9347 - val_loss: 0.2503 - val_accuracy: 0.9306 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2102 - accuracy: 0.9369 - val_loss: 0.2447 - val_accuracy: 0.9324 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2049 - accuracy: 0.9384 - val_loss: 0.2392 - val_accuracy: 0.9341 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1995 - accuracy: 0.9407 - val_loss: 0.2344 - val_accuracy: 0.9361 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1947 - accuracy: 0.9423 - val_loss: 0.2303 - val_accuracy: 0.9371 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1902 - accuracy: 0.9429 - val_loss: 0.2265 - val_accuracy: 0.9368 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1858 - accuracy: 0.9445 - val_loss: 0.2231 - val_accuracy: 0.9378 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1833 - accuracy: 0.9455 - val_loss: 0.2206 - val_accuracy: 0.9383 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1797 - accuracy: 0.9457 - val_loss: 0.2177 - val_accuracy: 0.9394 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1773 - accuracy: 0.9472 - val_loss: 0.2154 - val_accuracy: 0.9408 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1746 - accuracy: 0.9481 - val_loss: 0.2134 - val_accuracy: 0.9411 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1721 - accuracy: 0.9488 - val_loss: 0.2117 - val_accuracy: 0.9416 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1705 - accuracy: 0.9492 - val_loss: 0.2103 - val_accuracy: 0.9418 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1685 - accuracy: 0.9504 - val_loss: 0.2090 - val_accuracy: 0.9415 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1671 - accuracy: 0.9510 - val_loss: 0.2078 - val_accuracy: 0.9423 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1652 - accuracy: 0.9511 - val_loss: 0.2064 - val_accuracy: 0.9428 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1635 - accuracy: 0.9522 - val_loss: 0.2051 - val_accuracy: 0.9436 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1630 - accuracy: 0.9518 - val_loss: 0.2036 - val_accuracy: 0.9437 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1615 - accuracy: 0.9514 - val_loss: 0.2027 - val_accuracy: 0.9444 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 5s 22ms/step - loss: 0.1594 - accuracy: 0.9528 - val_loss: 0.2020 - val_accuracy: 0.9444 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 5s 20ms/step - loss: 0.1590 - accuracy: 0.9526 - val_loss: 0.2007 - val_accuracy: 0.9451 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 5s 21ms/step - loss: 0.1579 - accuracy: 0.9533 - val_loss: 0.1996 - val_accuracy: 0.9450 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 5s 22ms/step - loss: 0.1563 - accuracy: 0.9534 - val_loss: 0.1990 - val_accuracy: 0.9454 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1548 - accuracy: 0.9536 - val_loss: 0.1980 - val_accuracy: 0.9459 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1533 - accuracy: 0.9550 - val_loss: 0.1977 - val_accuracy: 0.9464 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1527 - accuracy: 0.9543 - val_loss: 0.1970 - val_accuracy: 0.9459 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1516 - accuracy: 0.9552 - val_loss: 0.1968 - val_accuracy: 0.9467 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1511 - accuracy: 0.9550 - val_loss: 0.1956 - val_accuracy: 0.9475 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1497 - accuracy: 0.9555 - val_loss: 0.1956 - val_accuracy: 0.9468 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1485 - accuracy: 0.9563 - val_loss: 0.1953 - val_accuracy: 0.9471 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1482 - accuracy: 0.9561 - val_loss: 0.1946 - val_accuracy: 0.9470 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9566 - val_loss: 0.1949 - val_accuracy: 0.9475 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1471 - accuracy: 0.9568 - val_loss: 0.1942 - val_accuracy: 0.9474 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9572 - val_loss: 0.1941 - val_accuracy: 0.9474 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1457 - accuracy: 0.9565 - val_loss: 0.1938 - val_accuracy: 0.9479 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1453 - accuracy: 0.9568 - val_loss: 0.1944 - val_accuracy: 0.9477 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1437 - accuracy: 0.9572 - val_loss: 0.1938 - val_accuracy: 0.9479 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1439 - accuracy: 0.9573 - val_loss: 0.1940 - val_accuracy: 0.9476 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1432 - accuracy: 0.9574 - val_loss: 0.1938 - val_accuracy: 0.9477 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1419 - accuracy: 0.9577 - val_loss: 0.1941 - val_accuracy: 0.9476 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1420 - accuracy: 0.9582 - val_loss: 0.1936 - val_accuracy: 0.9481 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 4s 15ms/step - loss: 0.6639 - accuracy: 0.7966 - val_loss: 0.5465 - val_accuracy: 0.8385 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 4s 15ms/step - loss: 0.5403 - accuracy: 0.8354 - val_loss: 0.5089 - val_accuracy: 0.8527 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 4s 15ms/step - loss: 0.5132 - accuracy: 0.8451 - val_loss: 0.4909 - val_accuracy: 0.8579 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4977 - accuracy: 0.8506 - val_loss: 0.4808 - val_accuracy: 0.8624 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4876 - accuracy: 0.8529 - val_loss: 0.4735 - val_accuracy: 0.8652 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 4s 17ms/step - loss: 0.4794 - accuracy: 0.8564 - val_loss: 0.4678 - val_accuracy: 0.8672 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4732 - accuracy: 0.8581 - val_loss: 0.4634 - val_accuracy: 0.8687 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4676 - accuracy: 0.8606 - val_loss: 0.4577 - val_accuracy: 0.8714 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4633 - accuracy: 0.8614 - val_loss: 0.4546 - val_accuracy: 0.8726 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4588 - accuracy: 0.8624 - val_loss: 0.4513 - val_accuracy: 0.8740 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4554 - accuracy: 0.8644 - val_loss: 0.4487 - val_accuracy: 0.8752 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4523 - accuracy: 0.8644 - val_loss: 0.4464 - val_accuracy: 0.8753 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4506 - accuracy: 0.8649 - val_loss: 0.4444 - val_accuracy: 0.8753 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4484 - accuracy: 0.8658 - val_loss: 0.4427 - val_accuracy: 0.8759 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4462 - accuracy: 0.8661 - val_loss: 0.4413 - val_accuracy: 0.8757 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4457 - accuracy: 0.8661 - val_loss: 0.4398 - val_accuracy: 0.8759 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4426 - accuracy: 0.8675 - val_loss: 0.4384 - val_accuracy: 0.8769 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4423 - accuracy: 0.8665 - val_loss: 0.4371 - val_accuracy: 0.8771 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4405 - accuracy: 0.8682 - val_loss: 0.4358 - val_accuracy: 0.8769 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4389 - accuracy: 0.8680 - val_loss: 0.4346 - val_accuracy: 0.8775 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4388 - accuracy: 0.8683 - val_loss: 0.4335 - val_accuracy: 0.8785 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4377 - accuracy: 0.8686 - val_loss: 0.4324 - val_accuracy: 0.8790 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4355 - accuracy: 0.8695 - val_loss: 0.4315 - val_accuracy: 0.8795 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4351 - accuracy: 0.8702 - val_loss: 0.4308 - val_accuracy: 0.8788 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4337 - accuracy: 0.8703 - val_loss: 0.4300 - val_accuracy: 0.8789 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4328 - accuracy: 0.8697 - val_loss: 0.4296 - val_accuracy: 0.8785 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4330 - accuracy: 0.8704 - val_loss: 0.4292 - val_accuracy: 0.8784 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4312 - accuracy: 0.8702 - val_loss: 0.4285 - val_accuracy: 0.8786 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4312 - accuracy: 0.8702 - val_loss: 0.4275 - val_accuracy: 0.8784 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4310 - accuracy: 0.8705 - val_loss: 0.4272 - val_accuracy: 0.8782 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4296 - accuracy: 0.8712 - val_loss: 0.4270 - val_accuracy: 0.8783 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4289 - accuracy: 0.8714 - val_loss: 0.4260 - val_accuracy: 0.8792 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4290 - accuracy: 0.8715 - val_loss: 0.4256 - val_accuracy: 0.8791 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4279 - accuracy: 0.8722 - val_loss: 0.4251 - val_accuracy: 0.8791 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4269 - accuracy: 0.8727 - val_loss: 0.4246 - val_accuracy: 0.8797 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4269 - accuracy: 0.8718 - val_loss: 0.4241 - val_accuracy: 0.8798 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4261 - accuracy: 0.8725 - val_loss: 0.4237 - val_accuracy: 0.8803 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4264 - accuracy: 0.8731 - val_loss: 0.4233 - val_accuracy: 0.8803 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4255 - accuracy: 0.8722 - val_loss: 0.4229 - val_accuracy: 0.8806 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4254 - accuracy: 0.8728 - val_loss: 0.4222 - val_accuracy: 0.8809 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4241 - accuracy: 0.8729 - val_loss: 0.4219 - val_accuracy: 0.8807 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4236 - accuracy: 0.8729 - val_loss: 0.4217 - val_accuracy: 0.8814 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4245 - accuracy: 0.8731 - val_loss: 0.4214 - val_accuracy: 0.8802 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4224 - accuracy: 0.8733 - val_loss: 0.4209 - val_accuracy: 0.8807 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4230 - accuracy: 0.8735 - val_loss: 0.4199 - val_accuracy: 0.8815 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4225 - accuracy: 0.8736 - val_loss: 0.4189 - val_accuracy: 0.8818 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4210 - accuracy: 0.8739 - val_loss: 0.4182 - val_accuracy: 0.8821 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4202 - accuracy: 0.8743 - val_loss: 0.4180 - val_accuracy: 0.8823 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4206 - accuracy: 0.8741 - val_loss: 0.4174 - val_accuracy: 0.8822 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4202 - accuracy: 0.8742 - val_loss: 0.4166 - val_accuracy: 0.8825 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0673 - accuracy: 0.6587 - val_loss: 1.0029 - val_accuracy: 0.6754 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9734 - accuracy: 0.6755 - val_loss: 0.9568 - val_accuracy: 0.6862 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9427 - accuracy: 0.6837 - val_loss: 0.9234 - val_accuracy: 0.6967 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9194 - accuracy: 0.6945 - val_loss: 0.9061 - val_accuracy: 0.7071 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9105 - accuracy: 0.7003 - val_loss: 0.9004 - val_accuracy: 0.7099 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9061 - accuracy: 0.7017 - val_loss: 0.8963 - val_accuracy: 0.7114 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9012 - accuracy: 0.7048 - val_loss: 0.8890 - val_accuracy: 0.7153 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8942 - accuracy: 0.7069 - val_loss: 0.8834 - val_accuracy: 0.7172 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8903 - accuracy: 0.7096 - val_loss: 0.8786 - val_accuracy: 0.7203 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8855 - accuracy: 0.7119 - val_loss: 0.8738 - val_accuracy: 0.7218 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8803 - accuracy: 0.7143 - val_loss: 0.8703 - val_accuracy: 0.7235 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8772 - accuracy: 0.7148 - val_loss: 0.8673 - val_accuracy: 0.7254 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8745 - accuracy: 0.7161 - val_loss: 0.8639 - val_accuracy: 0.7259 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8711 - accuracy: 0.7169 - val_loss: 0.8619 - val_accuracy: 0.7268 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8696 - accuracy: 0.7181 - val_loss: 0.8598 - val_accuracy: 0.7282 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8667 - accuracy: 0.7179 - val_loss: 0.8577 - val_accuracy: 0.7287 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8659 - accuracy: 0.7184 - val_loss: 0.8562 - val_accuracy: 0.7304 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8632 - accuracy: 0.7204 - val_loss: 0.8551 - val_accuracy: 0.7299 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8629 - accuracy: 0.7198 - val_loss: 0.8540 - val_accuracy: 0.7306 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8622 - accuracy: 0.7206 - val_loss: 0.8538 - val_accuracy: 0.7309 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8613 - accuracy: 0.7207 - val_loss: 0.8525 - val_accuracy: 0.7322 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8604 - accuracy: 0.7209 - val_loss: 0.8516 - val_accuracy: 0.7320 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8600 - accuracy: 0.7211 - val_loss: 0.8512 - val_accuracy: 0.7322 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8601 - accuracy: 0.7203 - val_loss: 0.8505 - val_accuracy: 0.7321 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8595 - accuracy: 0.7214 - val_loss: 0.8496 - val_accuracy: 0.7328 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 4s 18ms/step - loss: 0.8587 - accuracy: 0.7213 - val_loss: 0.8490 - val_accuracy: 0.7330 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8576 - accuracy: 0.7219 - val_loss: 0.8488 - val_accuracy: 0.7332 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8573 - accuracy: 0.7211 - val_loss: 0.8481 - val_accuracy: 0.7333 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8568 - accuracy: 0.7216 - val_loss: 0.8479 - val_accuracy: 0.7337 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8566 - accuracy: 0.7219 - val_loss: 0.8474 - val_accuracy: 0.7333 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8565 - accuracy: 0.7228 - val_loss: 0.8469 - val_accuracy: 0.7338 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8557 - accuracy: 0.7228 - val_loss: 0.8462 - val_accuracy: 0.7344 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8544 - accuracy: 0.7230 - val_loss: 0.8454 - val_accuracy: 0.7343 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8542 - accuracy: 0.7226 - val_loss: 0.8446 - val_accuracy: 0.7340 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8541 - accuracy: 0.7227 - val_loss: 0.8439 - val_accuracy: 0.7342 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8535 - accuracy: 0.7233 - val_loss: 0.8438 - val_accuracy: 0.7340 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8520 - accuracy: 0.7232 - val_loss: 0.8430 - val_accuracy: 0.7342 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8521 - accuracy: 0.7234 - val_loss: 0.8429 - val_accuracy: 0.7345 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8518 - accuracy: 0.7236 - val_loss: 0.8423 - val_accuracy: 0.7350 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8515 - accuracy: 0.7236 - val_loss: 0.8421 - val_accuracy: 0.7350 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8516 - accuracy: 0.7238 - val_loss: 0.8414 - val_accuracy: 0.7350 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8502 - accuracy: 0.7236 - val_loss: 0.8413 - val_accuracy: 0.7353 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8508 - accuracy: 0.7234 - val_loss: 0.8408 - val_accuracy: 0.7354 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8502 - accuracy: 0.7236 - val_loss: 0.8408 - val_accuracy: 0.7357 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8503 - accuracy: 0.7250 - val_loss: 0.8406 - val_accuracy: 0.7355 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8495 - accuracy: 0.7247 - val_loss: 0.8404 - val_accuracy: 0.7359 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8496 - accuracy: 0.7243 - val_loss: 0.8399 - val_accuracy: 0.7361 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8492 - accuracy: 0.7247 - val_loss: 0.8399 - val_accuracy: 0.7353 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8497 - accuracy: 0.7248 - val_loss: 0.8397 - val_accuracy: 0.7354 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8484 - accuracy: 0.7250 - val_loss: 0.8397 - val_accuracy: 0.7352 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8488 - accuracy: 0.7244 - val_loss: 0.8395 - val_accuracy: 0.7351 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8500 - accuracy: 0.7248 - val_loss: 0.8395 - val_accuracy: 0.7354 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8472 - accuracy: 0.7251 - val_loss: 0.8392 - val_accuracy: 0.7349 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8477 - accuracy: 0.7254 - val_loss: 0.8389 - val_accuracy: 0.7356 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8471 - accuracy: 0.7253 - val_loss: 0.8390 - val_accuracy: 0.7355 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8478 - accuracy: 0.7250 - val_loss: 0.8388 - val_accuracy: 0.7358 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8480 - accuracy: 0.7260 - val_loss: 0.8386 - val_accuracy: 0.7355 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8476 - accuracy: 0.7254 - val_loss: 0.8386 - val_accuracy: 0.7355 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8472 - accuracy: 0.7257 - val_loss: 0.8387 - val_accuracy: 0.7351 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8462 - accuracy: 0.7251 - val_loss: 0.8384 - val_accuracy: 0.7359 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8465 - accuracy: 0.7258 - val_loss: 0.8386 - val_accuracy: 0.7354 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8470 - accuracy: 0.7251 - val_loss: 0.8383 - val_accuracy: 0.7354 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8477 - accuracy: 0.7250 - val_loss: 0.8383 - val_accuracy: 0.7353 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8454 - accuracy: 0.7257 - val_loss: 0.8378 - val_accuracy: 0.7357 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8466 - accuracy: 0.7262 - val_loss: 0.8378 - val_accuracy: 0.7354 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8460 - accuracy: 0.7258 - val_loss: 0.8369 - val_accuracy: 0.7368 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8444 - accuracy: 0.7267 - val_loss: 0.8362 - val_accuracy: 0.7366 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8438 - accuracy: 0.7276 - val_loss: 0.8357 - val_accuracy: 0.7367 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8439 - accuracy: 0.7266 - val_loss: 0.8354 - val_accuracy: 0.7365 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8429 - accuracy: 0.7263 - val_loss: 0.8347 - val_accuracy: 0.7370 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8428 - accuracy: 0.7271 - val_loss: 0.8336 - val_accuracy: 0.7368 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8417 - accuracy: 0.7275 - val_loss: 0.8332 - val_accuracy: 0.7375 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8424 - accuracy: 0.7269 - val_loss: 0.8333 - val_accuracy: 0.7373 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8417 - accuracy: 0.7272 - val_loss: 0.8331 - val_accuracy: 0.7375 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8422 - accuracy: 0.7265 - val_loss: 0.8330 - val_accuracy: 0.7368 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8411 - accuracy: 0.7263 - val_loss: 0.8327 - val_accuracy: 0.7371 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8415 - accuracy: 0.7264 - val_loss: 0.8325 - val_accuracy: 0.7366 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8402 - accuracy: 0.7268 - val_loss: 0.8324 - val_accuracy: 0.7369 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8411 - accuracy: 0.7266 - val_loss: 0.8325 - val_accuracy: 0.7369 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8409 - accuracy: 0.7267 - val_loss: 0.8323 - val_accuracy: 0.7370 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8404 - accuracy: 0.7278 - val_loss: 0.8324 - val_accuracy: 0.7372 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8414 - accuracy: 0.7270 - val_loss: 0.8323 - val_accuracy: 0.7370 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8404 - accuracy: 0.7265 - val_loss: 0.8321 - val_accuracy: 0.7365 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8401 - accuracy: 0.7272 - val_loss: 0.8320 - val_accuracy: 0.7367 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8408 - accuracy: 0.7270 - val_loss: 0.8320 - val_accuracy: 0.7374 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8408 - accuracy: 0.7269 - val_loss: 0.8318 - val_accuracy: 0.7371 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8403 - accuracy: 0.7275 - val_loss: 0.8318 - val_accuracy: 0.7373 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8397 - accuracy: 0.7281 - val_loss: 0.8318 - val_accuracy: 0.7369 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8397 - accuracy: 0.7273 - val_loss: 0.8318 - val_accuracy: 0.7371 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8398 - accuracy: 0.7277 - val_loss: 0.8316 - val_accuracy: 0.7368 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8394 - accuracy: 0.7271 - val_loss: 0.8316 - val_accuracy: 0.7369 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8403 - accuracy: 0.7273 - val_loss: 0.8315 - val_accuracy: 0.7370 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8393 - accuracy: 0.7280 - val_loss: 0.8316 - val_accuracy: 0.7369 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8402 - accuracy: 0.7274 - val_loss: 0.8314 - val_accuracy: 0.7374 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8398 - accuracy: 0.7266 - val_loss: 0.8313 - val_accuracy: 0.7375 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8399 - accuracy: 0.7271 - val_loss: 0.8313 - val_accuracy: 0.7372 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8390 - accuracy: 0.7275 - val_loss: 0.8314 - val_accuracy: 0.7368 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8389 - accuracy: 0.7268 - val_loss: 0.8312 - val_accuracy: 0.7371 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8393 - accuracy: 0.7271 - val_loss: 0.8309 - val_accuracy: 0.7371 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8395 - accuracy: 0.7284 - val_loss: 0.8304 - val_accuracy: 0.7374 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 4s 9ms/step - loss: 0.8536 - accuracy: 0.9010 - val_loss: 0.8275 - val_accuracy: 0.9060 [ 0. 0. 0. ... -0. -0. 0.21805383] Sparsity at: 0.49998323497854075 Epoch 2/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8433 - accuracy: 0.9020 - val_loss: 0.8256 - val_accuracy: 0.9056 [ 0. 0. 0. ... -0. -0. 0.22636366] Sparsity at: 0.49998323497854075 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8420 - accuracy: 0.9018 - val_loss: 0.8254 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.2295623] Sparsity at: 0.49998323497854075 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8417 - accuracy: 0.9018 - val_loss: 0.8245 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. -0. 0.2311757] Sparsity at: 0.49998323497854075 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8412 - accuracy: 0.9019 - val_loss: 0.8244 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. -0. 0.23199476] Sparsity at: 0.49998323497854075 Epoch 6/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8408 - accuracy: 0.9019 - val_loss: 0.8247 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.23231447] Sparsity at: 0.49998323497854075 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.9020 - val_loss: 0.8235 - val_accuracy: 0.9053 [ 0. 0. 0. ... -0. -0. 0.23232621] Sparsity at: 0.49998323497854075 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8403 - accuracy: 0.9015 - val_loss: 0.8233 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. -0. 0.23200971] Sparsity at: 0.49998323497854075 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9017 - val_loss: 0.8237 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.2316019] Sparsity at: 0.49998323497854075 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9015 - val_loss: 0.8234 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.23150554] Sparsity at: 0.49998323497854075 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9017 - val_loss: 0.8231 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. -0. 0.23113468] Sparsity at: 0.49998323497854075 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9015 - val_loss: 0.8232 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.231103] Sparsity at: 0.49998323497854075 Epoch 13/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9018 - val_loss: 0.8233 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.23084843] Sparsity at: 0.49998323497854075 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.23062828] Sparsity at: 0.49998323497854075 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8396 - accuracy: 0.9017 - val_loss: 0.8230 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.23050003] Sparsity at: 0.49998323497854075 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8396 - accuracy: 0.9018 - val_loss: 0.8230 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.23042256] Sparsity at: 0.49998323497854075 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8395 - accuracy: 0.9018 - val_loss: 0.8229 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.23043126] Sparsity at: 0.49998323497854075 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9018 - val_loss: 0.8227 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.23020257] Sparsity at: 0.49998323497854075 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8395 - accuracy: 0.9020 - val_loss: 0.8225 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. -0. 0.22984925] Sparsity at: 0.49998323497854075 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8393 - accuracy: 0.9019 - val_loss: 0.8228 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.22988974] Sparsity at: 0.49998323497854075 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9014 - val_loss: 0.8226 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. -0. 0.22962575] Sparsity at: 0.49998323497854075 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8393 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.2296686] Sparsity at: 0.49998323497854075 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8393 - accuracy: 0.9019 - val_loss: 0.8231 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.22939289] Sparsity at: 0.49998323497854075 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8234 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.22926] Sparsity at: 0.49998323497854075 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. -0. 0.22909662] Sparsity at: 0.49998323497854075 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.22895984] Sparsity at: 0.49998323497854075 Epoch 27/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8392 - accuracy: 0.9020 - val_loss: 0.8224 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.22890972] Sparsity at: 0.49998323497854075 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8225 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.22902352] Sparsity at: 0.49998323497854075 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9020 - val_loss: 0.8227 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.22851856] Sparsity at: 0.49998323497854075 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9017 - val_loss: 0.8226 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.22874747] Sparsity at: 0.49998323497854075 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9019 - val_loss: 0.8224 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. -0. 0.22886471] Sparsity at: 0.49998323497854075 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9018 - val_loss: 0.8227 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.22834724] Sparsity at: 0.49998323497854075 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9019 - val_loss: 0.8222 - val_accuracy: 0.9053 [ 0. 0. 0. ... -0. -0. 0.22836772] Sparsity at: 0.49998323497854075 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9018 - val_loss: 0.8225 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. -0. 0.22839575] Sparsity at: 0.49998323497854075 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9018 - val_loss: 0.8231 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.2281949] Sparsity at: 0.49998323497854075 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9018 - val_loss: 0.8220 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.22857745] Sparsity at: 0.49998323497854075 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9018 - val_loss: 0.8226 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.22819276] Sparsity at: 0.49998323497854075 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9013 - val_loss: 0.8224 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.2283566] Sparsity at: 0.49998323497854075 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8388 - accuracy: 0.9019 - val_loss: 0.8228 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.22793572] Sparsity at: 0.49998323497854075 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8389 - accuracy: 0.9020 - val_loss: 0.8228 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.22830379] Sparsity at: 0.49998323497854075 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9015 - val_loss: 0.8222 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.22834966] Sparsity at: 0.49998323497854075 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8389 - accuracy: 0.9021 - val_loss: 0.8225 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. -0. 0.22823043] Sparsity at: 0.49998323497854075 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8225 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. -0. 0.22847441] Sparsity at: 0.49998323497854075 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9015 - val_loss: 0.8225 - val_accuracy: 0.9051 [ 0. 0. 0. ... -0. -0. 0.22821824] Sparsity at: 0.49998323497854075 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9018 - val_loss: 0.8224 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.22794549] Sparsity at: 0.49998323497854075 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9017 - val_loss: 0.8227 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. -0. 0.22805488] Sparsity at: 0.49998323497854075 Epoch 47/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9016 - val_loss: 0.8221 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.22819583] Sparsity at: 0.49998323497854075 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9017 - val_loss: 0.8226 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.22844236] Sparsity at: 0.49998323497854075 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8389 - accuracy: 0.9017 - val_loss: 0.8227 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. -0. 0.22832353] Sparsity at: 0.49998323497854075 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9019 - val_loss: 0.8227 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.22814119] Sparsity at: 0.49998323497854075 Epoch 51/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8650 - accuracy: 0.9018 - val_loss: 0.8420 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. -0. 0.26181144] Sparsity at: 0.6458724517167382 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8599 - accuracy: 0.9024 - val_loss: 0.8409 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. -0. 0.2705809] Sparsity at: 0.6458724517167382 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.9026 - val_loss: 0.8404 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0. -0. 0.2765177] Sparsity at: 0.6458724517167382 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.9029 - val_loss: 0.8401 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0. -0. 0.280353] Sparsity at: 0.6458724517167382 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8588 - accuracy: 0.9026 - val_loss: 0.8402 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. -0. 0.282932] Sparsity at: 0.6458724517167382 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9027 - val_loss: 0.8402 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0. -0. 0.2848979] Sparsity at: 0.6458724517167382 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8587 - accuracy: 0.9025 - val_loss: 0.8401 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.2859914] Sparsity at: 0.6458724517167382 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9025 - val_loss: 0.8402 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0. -0. 0.2868721] Sparsity at: 0.6458724517167382 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9026 - val_loss: 0.8399 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0. -0. 0.28774148] Sparsity at: 0.6458724517167382 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9027 - val_loss: 0.8400 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.2882026] Sparsity at: 0.6458724517167382 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9027 - val_loss: 0.8399 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.28876236] Sparsity at: 0.6458724517167382 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8587 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.28895956] Sparsity at: 0.6458724517167382 Epoch 63/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0. -0. 0.2891049] Sparsity at: 0.6458724517167382 Epoch 64/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9026 - val_loss: 0.8400 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0. -0. 0.28957662] Sparsity at: 0.6458724517167382 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9027 - val_loss: 0.8401 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0. -0. 0.28979963] Sparsity at: 0.6458724517167382 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9028 - val_loss: 0.8398 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.2899965] Sparsity at: 0.6458724517167382 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. -0. 0.2901228] Sparsity at: 0.6458724517167382 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9027 - val_loss: 0.8398 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. -0. 0.29049462] Sparsity at: 0.6458724517167382 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.29047298] Sparsity at: 0.6458724517167382 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9026 - val_loss: 0.8398 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. -0. 0.29082203] Sparsity at: 0.6458724517167382 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8396 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.2908758] Sparsity at: 0.6458724517167382 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8586 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. -0. 0.29115957] Sparsity at: 0.6458724517167382 Epoch 73/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8396 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.2911333] Sparsity at: 0.6458724517167382 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.29126108] Sparsity at: 0.6458724517167382 Epoch 75/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9029 - val_loss: 0.8399 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.29105765] Sparsity at: 0.6458724517167382 Epoch 76/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8396 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. -0. 0.29139096] Sparsity at: 0.6458724517167382 Epoch 77/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8397 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. -0. 0.29164252] Sparsity at: 0.6458724517167382 Epoch 78/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8396 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. -0. 0.29165566] Sparsity at: 0.6458724517167382 Epoch 79/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8398 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.29187158] Sparsity at: 0.6458724517167382 Epoch 80/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9027 - val_loss: 0.8399 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0. -0. 0.29169327] Sparsity at: 0.6458724517167382 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8397 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. -0. 0.29183212] Sparsity at: 0.6458724517167382 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9029 - val_loss: 0.8397 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. -0. 0.29182228] Sparsity at: 0.6458724517167382 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9026 - val_loss: 0.8399 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. -0. 0.2918862] Sparsity at: 0.6458724517167382 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8397 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.29203737] Sparsity at: 0.6458724517167382 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8395 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.2917743] Sparsity at: 0.6458724517167382 Epoch 86/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8398 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. -0. 0.29214373] Sparsity at: 0.6458724517167382 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.2921344] Sparsity at: 0.6458724517167382 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. -0. 0.2920466] Sparsity at: 0.6458724517167382 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9026 - val_loss: 0.8397 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.29211465] Sparsity at: 0.6458724517167382 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9025 - val_loss: 0.8396 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.2922954] Sparsity at: 0.6458724517167382 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8585 - accuracy: 0.9025 - val_loss: 0.8400 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. -0. 0.29225573] Sparsity at: 0.6458724517167382 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8397 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. -0. 0.29228523] Sparsity at: 0.6458724517167382 Epoch 93/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8586 - accuracy: 0.9025 - val_loss: 0.8402 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.29233763] Sparsity at: 0.6458724517167382 Epoch 94/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8583 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.29241607] Sparsity at: 0.6458724517167382 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9028 - val_loss: 0.8399 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. -0. 0.29256] Sparsity at: 0.6458724517167382 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. -0. 0.29275134] Sparsity at: 0.6458724517167382 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9023 - val_loss: 0.8395 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. -0. 0.292615] Sparsity at: 0.6458724517167382 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8398 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.29260945] Sparsity at: 0.6458724517167382 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9025 - val_loss: 0.8397 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. -0. 0.2926758] Sparsity at: 0.6458724517167382 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8583 - accuracy: 0.9025 - val_loss: 0.8399 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. -0. 0.29269928] Sparsity at: 0.6458724517167382 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8987 - accuracy: 0.9000 - val_loss: 0.8758 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.3242109] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8910 - accuracy: 0.9018 - val_loss: 0.8743 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. -0. 0.32834464] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8901 - accuracy: 0.9020 - val_loss: 0.8741 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.32975054] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9020 - val_loss: 0.8736 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.33069235] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9018 - val_loss: 0.8733 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.33105958] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8895 - accuracy: 0.9015 - val_loss: 0.8734 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.33167672] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8893 - accuracy: 0.9017 - val_loss: 0.8731 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.33176517] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8891 - accuracy: 0.9018 - val_loss: 0.8728 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.3320303] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8891 - accuracy: 0.9018 - val_loss: 0.8729 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.33248746] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8889 - accuracy: 0.9018 - val_loss: 0.8728 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.33279392] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8889 - accuracy: 0.9018 - val_loss: 0.8729 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.33287236] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9016 - val_loss: 0.8728 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.33309814] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9017 - val_loss: 0.8723 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33315548] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9019 - val_loss: 0.8726 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.3335356] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33334437] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.333639] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.3338172] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33377978] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8888 - accuracy: 0.9017 - val_loss: 0.8726 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.33386356] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.3342177] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8887 - accuracy: 0.9015 - val_loss: 0.8725 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.33423054] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.33425477] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9016 - val_loss: 0.8725 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.33455327] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9017 - val_loss: 0.8724 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.33466637] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.3346795] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.3345989] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.33454058] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.33486763] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.33468127] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8726 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.3345939] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8723 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.33467358] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8886 - accuracy: 0.9016 - val_loss: 0.8721 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33478442] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8884 - accuracy: 0.9017 - val_loss: 0.8722 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33460888] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8884 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.33514607] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8723 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.33500412] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9019 - val_loss: 0.8723 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.33499756] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9014 - val_loss: 0.8722 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. -0. 0.33509544] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9015 - val_loss: 0.8720 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.33501717] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8723 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33497882] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8724 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.3349794] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.3350159] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8724 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.3350516] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8720 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.33516055] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8722 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33523577] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8886 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.3353102] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9016 - val_loss: 0.8722 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.3350531] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8883 - accuracy: 0.9016 - val_loss: 0.8722 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.3351312] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8884 - accuracy: 0.9018 - val_loss: 0.8721 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.33521622] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8884 - accuracy: 0.9017 - val_loss: 0.8722 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.33528093] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8885 - accuracy: 0.9017 - val_loss: 0.8722 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.33523038] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9672 - accuracy: 0.8919 - val_loss: 0.9408 - val_accuracy: 0.8990 [ 0. 0. 0. ... -0. -0. 0.3569514] Sparsity at: 0.8448061963519313 Epoch 152/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9546 - accuracy: 0.8961 - val_loss: 0.9387 - val_accuracy: 0.9000 [ 0. 0. 0. ... -0. -0. 0.36170727] Sparsity at: 0.8448061963519313 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9531 - accuracy: 0.8972 - val_loss: 0.9378 - val_accuracy: 0.9006 [ 0. 0. 0. ... -0. -0. 0.36849245] Sparsity at: 0.8448061963519313 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9522 - accuracy: 0.8981 - val_loss: 0.9370 - val_accuracy: 0.9006 [ 0. 0. 0. ... -0. -0. 0.37500384] Sparsity at: 0.8448061963519313 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9516 - accuracy: 0.8984 - val_loss: 0.9364 - val_accuracy: 0.9013 [ 0. 0. 0. ... -0. -0. 0.38067392] Sparsity at: 0.8448061963519313 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9510 - accuracy: 0.8986 - val_loss: 0.9359 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. -0. 0.38540465] Sparsity at: 0.8448061963519313 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9506 - accuracy: 0.8989 - val_loss: 0.9357 - val_accuracy: 0.9016 [ 0. 0. 0. ... -0. -0. 0.38882956] Sparsity at: 0.8448061963519313 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9503 - accuracy: 0.8990 - val_loss: 0.9352 - val_accuracy: 0.9016 [ 0. 0. 0. ... -0. -0. 0.39076683] Sparsity at: 0.8448061963519313 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9501 - accuracy: 0.8994 - val_loss: 0.9352 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. -0. 0.39233917] Sparsity at: 0.8448061963519313 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9498 - accuracy: 0.8996 - val_loss: 0.9351 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. -0. 0.39321017] Sparsity at: 0.8448061963519313 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9497 - accuracy: 0.8996 - val_loss: 0.9348 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. -0. 0.3938331] Sparsity at: 0.8448061963519313 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9495 - accuracy: 0.8999 - val_loss: 0.9346 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39416364] Sparsity at: 0.8448061963519313 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9495 - accuracy: 0.8997 - val_loss: 0.9345 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.3942465] Sparsity at: 0.8448061963519313 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9493 - accuracy: 0.9002 - val_loss: 0.9343 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. -0. 0.39422846] Sparsity at: 0.8448061963519313 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9492 - accuracy: 0.8999 - val_loss: 0.9344 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39412263] Sparsity at: 0.8448061963519313 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9492 - accuracy: 0.9001 - val_loss: 0.9343 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. -0. 0.39411062] Sparsity at: 0.8448061963519313 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9491 - accuracy: 0.9001 - val_loss: 0.9342 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. -0. 0.3938568] Sparsity at: 0.8448061963519313 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9490 - accuracy: 0.9002 - val_loss: 0.9342 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.39366263] Sparsity at: 0.8448061963519313 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9490 - accuracy: 0.9001 - val_loss: 0.9340 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. -0. 0.39358422] Sparsity at: 0.8448061963519313 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9490 - accuracy: 0.9000 - val_loss: 0.9341 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. -0. 0.3933039] Sparsity at: 0.8448061963519313 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9489 - accuracy: 0.9002 - val_loss: 0.9341 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.392975] Sparsity at: 0.8448061963519313 Epoch 172/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9001 - val_loss: 0.9339 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39265066] Sparsity at: 0.8448061963519313 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9001 - val_loss: 0.9339 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.3926857] Sparsity at: 0.8448061963519313 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9001 - val_loss: 0.9338 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.3923594] Sparsity at: 0.8448061963519313 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9003 - val_loss: 0.9340 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39229864] Sparsity at: 0.8448061963519313 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9004 - val_loss: 0.9337 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39191854] Sparsity at: 0.8448061963519313 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9339 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39191952] Sparsity at: 0.8448061963519313 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9000 - val_loss: 0.9338 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39167577] Sparsity at: 0.8448061963519313 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9000 - val_loss: 0.9338 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. -0. 0.3915152] Sparsity at: 0.8448061963519313 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. -0. 0.39140016] Sparsity at: 0.8448061963519313 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9339 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. -0. 0.39123145] Sparsity at: 0.8448061963519313 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9487 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. -0. 0.391031] Sparsity at: 0.8448061963519313 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9488 - accuracy: 0.9002 - val_loss: 0.9338 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. -0. 0.390863] Sparsity at: 0.8448061963519313 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.3906115] Sparsity at: 0.8448061963519313 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. -0. 0.39056227] Sparsity at: 0.8448061963519313 Epoch 186/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9001 - val_loss: 0.9339 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. -0. 0.39052004] Sparsity at: 0.8448061963519313 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9337 - val_accuracy: 0.9025 [ 0. 0. 0. ... -0. -0. 0.39022812] Sparsity at: 0.8448061963519313 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9025 [ 0. 0. 0. ... -0. -0. 0.39030516] Sparsity at: 0.8448061963519313 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9337 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.3902272] Sparsity at: 0.8448061963519313 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9003 - val_loss: 0.9338 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.39020577] Sparsity at: 0.8448061963519313 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9005 - val_loss: 0.9337 - val_accuracy: 0.9025 [ 0. 0. 0. ... -0. -0. 0.3899172] Sparsity at: 0.8448061963519313 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. -0. 0.38981766] Sparsity at: 0.8448061963519313 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9006 - val_loss: 0.9339 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.38972422] Sparsity at: 0.8448061963519313 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9005 - val_loss: 0.9338 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.38974184] Sparsity at: 0.8448061963519313 Epoch 195/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9487 - accuracy: 0.9004 - val_loss: 0.9336 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.3893766] Sparsity at: 0.8448061963519313 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9336 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.38940224] Sparsity at: 0.8448061963519313 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9004 - val_loss: 0.9336 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. -0. 0.3893821] Sparsity at: 0.8448061963519313 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. -0. 0.3894376] Sparsity at: 0.8448061963519313 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9002 - val_loss: 0.9337 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. -0. 0.38932446] Sparsity at: 0.8448061963519313 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9486 - accuracy: 0.9003 - val_loss: 0.9337 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. -0. 0.38929915] Sparsity at: 0.8448061963519313 Epoch 201/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0491 - accuracy: 0.8890 - val_loss: 1.0147 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.44045904] Sparsity at: 0.9059482296137339 Epoch 202/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0282 - accuracy: 0.8952 - val_loss: 1.0124 - val_accuracy: 0.8988 [ 0. 0. 0. ... -0. -0. 0.43820158] Sparsity at: 0.9059482296137339 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0269 - accuracy: 0.8955 - val_loss: 1.0118 - val_accuracy: 0.8993 [ 0. 0. 0. ... -0. -0. 0.43797222] Sparsity at: 0.9059482296137339 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0264 - accuracy: 0.8953 - val_loss: 1.0111 - val_accuracy: 0.8989 [ 0. 0. 0. ... -0. -0. 0.437651] Sparsity at: 0.9059482296137339 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0260 - accuracy: 0.8955 - val_loss: 1.0109 - val_accuracy: 0.8990 [ 0. 0. 0. ... -0. -0. 0.43710318] Sparsity at: 0.9059482296137339 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0258 - accuracy: 0.8952 - val_loss: 1.0106 - val_accuracy: 0.8985 [ 0. 0. 0. ... -0. -0. 0.4365483] Sparsity at: 0.9059482296137339 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0256 - accuracy: 0.8953 - val_loss: 1.0104 - val_accuracy: 0.8987 [ 0. 0. 0. ... -0. -0. 0.43554276] Sparsity at: 0.9059482296137339 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0255 - accuracy: 0.8954 - val_loss: 1.0103 - val_accuracy: 0.8985 [ 0. 0. 0. ... -0. -0. 0.43491262] Sparsity at: 0.9059482296137339 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0254 - accuracy: 0.8954 - val_loss: 1.0103 - val_accuracy: 0.8987 [ 0. 0. 0. ... -0. -0. 0.43428048] Sparsity at: 0.9059482296137339 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0253 - accuracy: 0.8954 - val_loss: 1.0101 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.43354118] Sparsity at: 0.9059482296137339 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0252 - accuracy: 0.8953 - val_loss: 1.0100 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.4328327] Sparsity at: 0.9059482296137339 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0251 - accuracy: 0.8955 - val_loss: 1.0100 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.4324414] Sparsity at: 0.9059482296137339 Epoch 213/500 235/235 [==============================] - 2s 10ms/step - loss: 1.0251 - accuracy: 0.8955 - val_loss: 1.0100 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.43207797] Sparsity at: 0.9059482296137339 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0250 - accuracy: 0.8954 - val_loss: 1.0100 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. -0. 0.43149152] Sparsity at: 0.9059482296137339 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0250 - accuracy: 0.8953 - val_loss: 1.0098 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. -0. 0.4309252] Sparsity at: 0.9059482296137339 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0250 - accuracy: 0.8952 - val_loss: 1.0097 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. -0. 0.43057793] Sparsity at: 0.9059482296137339 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0249 - accuracy: 0.8954 - val_loss: 1.0096 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. -0. 0.4300944] Sparsity at: 0.9059482296137339 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0249 - accuracy: 0.8952 - val_loss: 1.0097 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.43006754] Sparsity at: 0.9059482296137339 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0249 - accuracy: 0.8953 - val_loss: 1.0097 - val_accuracy: 0.8983 [ 0. 0. 0. ... -0. -0. 0.42986378] Sparsity at: 0.9059482296137339 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8953 - val_loss: 1.0096 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.4296145] Sparsity at: 0.9059482296137339 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8954 - val_loss: 1.0096 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.42912394] Sparsity at: 0.9059482296137339 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8955 - val_loss: 1.0095 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.42883182] Sparsity at: 0.9059482296137339 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8952 - val_loss: 1.0096 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. -0. 0.42854697] Sparsity at: 0.9059482296137339 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0096 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.4285308] Sparsity at: 0.9059482296137339 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0248 - accuracy: 0.8954 - val_loss: 1.0095 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.4283238] Sparsity at: 0.9059482296137339 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8954 - val_loss: 1.0096 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.42821] Sparsity at: 0.9059482296137339 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0096 - val_accuracy: 0.8983 [ 0. 0. 0. ... -0. -0. 0.4280149] Sparsity at: 0.9059482296137339 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0095 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. -0. 0.4277269] Sparsity at: 0.9059482296137339 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.42757627] Sparsity at: 0.9059482296137339 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8953 - val_loss: 1.0095 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. -0. 0.42745447] Sparsity at: 0.9059482296137339 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0095 - val_accuracy: 0.8974 [ 0. 0. 0. ... -0. -0. 0.4275424] Sparsity at: 0.9059482296137339 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.4271754] Sparsity at: 0.9059482296137339 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. -0. 0.4270703] Sparsity at: 0.9059482296137339 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8951 - val_loss: 1.0094 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.42695278] Sparsity at: 0.9059482296137339 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.4267202] Sparsity at: 0.9059482296137339 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8953 - val_loss: 1.0094 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. -0. 0.4267205] Sparsity at: 0.9059482296137339 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.42663738] Sparsity at: 0.9059482296137339 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0247 - accuracy: 0.8952 - val_loss: 1.0094 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. -0. 0.42656147] Sparsity at: 0.9059482296137339 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8950 - val_loss: 1.0092 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.4262798] Sparsity at: 0.9059482296137339 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0093 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.42621666] Sparsity at: 0.9059482296137339 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.42633358] Sparsity at: 0.9059482296137339 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0093 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.42596006] Sparsity at: 0.9059482296137339 Epoch 243/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8952 - val_loss: 1.0092 - val_accuracy: 0.8983 [ 0. 0. 0. ... -0. -0. 0.4261564] Sparsity at: 0.9059482296137339 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8950 - val_loss: 1.0091 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. -0. 0.4260047] Sparsity at: 0.9059482296137339 Epoch 245/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.42587775] Sparsity at: 0.9059482296137339 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8948 - val_loss: 1.0092 - val_accuracy: 0.8983 [ 0. 0. 0. ... -0. -0. 0.42569843] Sparsity at: 0.9059482296137339 Epoch 247/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8950 - val_loss: 1.0093 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.42559257] Sparsity at: 0.9059482296137339 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. -0. 0.42568636] Sparsity at: 0.9059482296137339 Epoch 249/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0246 - accuracy: 0.8949 - val_loss: 1.0092 - val_accuracy: 0.8983 [ 0. 0. 0. ... -0. -0. 0.42566693] Sparsity at: 0.9059482296137339 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0245 - accuracy: 0.8949 - val_loss: 1.0091 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.42551887] Sparsity at: 0.9059482296137339 Epoch 251/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2078 - accuracy: 0.8616 - val_loss: 1.1473 - val_accuracy: 0.8788 [ 0. 0. 0. ... 0. -0. 0.68105495] Sparsity at: 0.9468716469957081 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1674 - accuracy: 0.8744 - val_loss: 1.1415 - val_accuracy: 0.8812 [ 0. 0. 0. ... 0. -0. 0.7017333] Sparsity at: 0.9468716469957081 Epoch 253/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1634 - accuracy: 0.8748 - val_loss: 1.1392 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. -0. 0.7052732] Sparsity at: 0.9468716469957081 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1616 - accuracy: 0.8751 - val_loss: 1.1381 - val_accuracy: 0.8818 [ 0. 0. 0. ... 0. -0. 0.7065625] Sparsity at: 0.9468716469957081 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1605 - accuracy: 0.8759 - val_loss: 1.1373 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. -0. 0.7076424] Sparsity at: 0.9468716469957081 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1597 - accuracy: 0.8759 - val_loss: 1.1369 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. -0. 0.7085091] Sparsity at: 0.9468716469957081 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1593 - accuracy: 0.8758 - val_loss: 1.1366 - val_accuracy: 0.8821 [ 0. 0. 0. ... 0. -0. 0.70922065] Sparsity at: 0.9468716469957081 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1590 - accuracy: 0.8756 - val_loss: 1.1364 - val_accuracy: 0.8815 [ 0. 0. 0. ... 0. -0. 0.70972717] Sparsity at: 0.9468716469957081 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1589 - accuracy: 0.8754 - val_loss: 1.1361 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. -0. 0.71010655] Sparsity at: 0.9468716469957081 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1587 - accuracy: 0.8755 - val_loss: 1.1361 - val_accuracy: 0.8813 [ 0. 0. 0. ... 0. -0. 0.71046257] Sparsity at: 0.9468716469957081 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1585 - accuracy: 0.8756 - val_loss: 1.1360 - val_accuracy: 0.8814 [ 0. 0. 0. ... 0. -0. 0.71066684] Sparsity at: 0.9468716469957081 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1584 - accuracy: 0.8755 - val_loss: 1.1359 - val_accuracy: 0.8815 [ 0. 0. 0. ... 0. -0. 0.7108232] Sparsity at: 0.9468716469957081 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1584 - accuracy: 0.8755 - val_loss: 1.1359 - val_accuracy: 0.8813 [ 0. 0. 0. ... 0. -0. 0.71093494] Sparsity at: 0.9468716469957081 Epoch 264/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1583 - accuracy: 0.8756 - val_loss: 1.1358 - val_accuracy: 0.8813 [ 0. 0. 0. ... 0. -0. 0.71103567] Sparsity at: 0.9468716469957081 Epoch 265/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1583 - accuracy: 0.8755 - val_loss: 1.1358 - val_accuracy: 0.8815 [ 0. 0. 0. ... 0. -0. 0.71122766] Sparsity at: 0.9468716469957081 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1582 - accuracy: 0.8755 - val_loss: 1.1358 - val_accuracy: 0.8817 [ 0. 0. 0. ... 0. -0. 0.71111274] Sparsity at: 0.9468716469957081 Epoch 267/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1582 - accuracy: 0.8757 - val_loss: 1.1358 - val_accuracy: 0.8815 [ 0. 0. 0. ... 0. -0. 0.7110791] Sparsity at: 0.9468716469957081 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8755 - val_loss: 1.1357 - val_accuracy: 0.8814 [ 0. 0. 0. ... 0. -0. 0.71092844] Sparsity at: 0.9468716469957081 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8816 [ 0. 0. 0. ... 0. -0. 0.7109319] Sparsity at: 0.9468716469957081 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1358 - val_accuracy: 0.8818 [ 0. 0. 0. ... 0. -0. 0.71097296] Sparsity at: 0.9468716469957081 Epoch 271/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.710939] Sparsity at: 0.9468716469957081 Epoch 272/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8818 [ 0. 0. 0. ... 0. -0. 0.71078634] Sparsity at: 0.9468716469957081 Epoch 273/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8818 [ 0. 0. 0. ... 0. -0. 0.71088874] Sparsity at: 0.9468716469957081 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. -0. 0.7109329] Sparsity at: 0.9468716469957081 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8755 - val_loss: 1.1357 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.71085817] Sparsity at: 0.9468716469957081 Epoch 276/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. -0. 0.7106811] Sparsity at: 0.9468716469957081 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1356 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.71067774] Sparsity at: 0.9468716469957081 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. -0. 0.7104908] Sparsity at: 0.9468716469957081 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8758 - val_loss: 1.1357 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.7106232] Sparsity at: 0.9468716469957081 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8821 [ 0. 0. 0. ... 0. -0. 0.7105501] Sparsity at: 0.9468716469957081 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1356 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. -0. 0.71067584] Sparsity at: 0.9468716469957081 Epoch 282/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8756 - val_loss: 1.1356 - val_accuracy: 0.8821 [ 0. 0. 0. ... 0. -0. 0.7104435] Sparsity at: 0.9468716469957081 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.7104664] Sparsity at: 0.9468716469957081 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8758 - val_loss: 1.1357 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.710571] Sparsity at: 0.9468716469957081 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8821 [ 0. 0. 0. ... 0. -0. 0.71033967] Sparsity at: 0.9468716469957081 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8755 - val_loss: 1.1357 - val_accuracy: 0.8821 [ 0. 0. 0. ... 0. -0. 0.7105198] Sparsity at: 0.9468716469957081 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8757 - val_loss: 1.1356 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. -0. 0.7103677] Sparsity at: 0.9468716469957081 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8757 - val_loss: 1.1356 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. -0. 0.71035534] Sparsity at: 0.9468716469957081 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1580 - accuracy: 0.8756 - val_loss: 1.1356 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. -0. 0.710402] Sparsity at: 0.9468716469957081 Epoch 290/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8757 - val_loss: 1.1357 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. -0. 0.71020424] Sparsity at: 0.9468716469957081 Epoch 291/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1581 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. -0. 0.7104156] Sparsity at: 0.9468716469957081 Epoch 292/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8756 - val_loss: 1.1357 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. -0. 0.7103795] Sparsity at: 0.9468716469957081 Epoch 293/500 235/235 [==============================] - 3s 11ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8821 [ 0. 0. 0. ... 0. -0. 0.71035546] Sparsity at: 0.9468716469957081 Epoch 294/500 235/235 [==============================] - 3s 12ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. -0. 0.7101318] Sparsity at: 0.9468716469957081 Epoch 295/500 235/235 [==============================] - 3s 12ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. -0. 0.7100907] Sparsity at: 0.9468716469957081 Epoch 296/500 235/235 [==============================] - 3s 11ms/step - loss: 1.1580 - accuracy: 0.8759 - val_loss: 1.1356 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. -0. 0.71015835] Sparsity at: 0.9468716469957081 Epoch 297/500 235/235 [==============================] - 3s 11ms/step - loss: 1.1580 - accuracy: 0.8759 - val_loss: 1.1356 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. -0. 0.71022433] Sparsity at: 0.9468716469957081 Epoch 298/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1581 - accuracy: 0.8759 - val_loss: 1.1356 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. -0. 0.71004605] Sparsity at: 0.9468716469957081 Epoch 299/500 235/235 [==============================] - 3s 12ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1355 - val_accuracy: 0.8825 [ 0. 0. 0. ... 0. -0. 0.7101235] Sparsity at: 0.9468716469957081 Epoch 300/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1580 - accuracy: 0.8758 - val_loss: 1.1356 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. -0. 0.71020883] Sparsity at: 0.9468716469957081 Epoch 301/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5396 - accuracy: 0.7085 - val_loss: 1.4520 - val_accuracy: 0.7639 [ 0. 0. 0. ... 0. -0. 0.8391014] Sparsity at: 0.9717844688841202 Epoch 302/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4530 - accuracy: 0.7676 - val_loss: 1.4319 - val_accuracy: 0.7773 [ 0. 0. 0. ... 0. -0. 0.84345615] Sparsity at: 0.9717844688841202 Epoch 303/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4429 - accuracy: 0.7737 - val_loss: 1.4264 - val_accuracy: 0.7798 [ 0. 0. 0. ... 0. -0. 0.8446345] Sparsity at: 0.9717844688841202 Epoch 304/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4394 - accuracy: 0.7755 - val_loss: 1.4241 - val_accuracy: 0.7805 [ 0. 0. 0. ... 0. -0. 0.8456495] Sparsity at: 0.9717844688841202 Epoch 305/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4378 - accuracy: 0.7762 - val_loss: 1.4228 - val_accuracy: 0.7808 [ 0. 0. 0. ... 0. -0. 0.84632176] Sparsity at: 0.9717844688841202 Epoch 306/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4368 - accuracy: 0.7767 - val_loss: 1.4221 - val_accuracy: 0.7810 [ 0. 0. 0. ... 0. -0. 0.84689754] Sparsity at: 0.9717844688841202 Epoch 307/500 235/235 [==============================] - 2s 11ms/step - loss: 1.4362 - accuracy: 0.7770 - val_loss: 1.4217 - val_accuracy: 0.7814 [ 0. 0. 0. ... 0. -0. 0.8471032] Sparsity at: 0.9717844688841202 Epoch 308/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4357 - accuracy: 0.7771 - val_loss: 1.4213 - val_accuracy: 0.7813 [ 0. 0. 0. ... 0. -0. 0.8475388] Sparsity at: 0.9717844688841202 Epoch 309/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4353 - accuracy: 0.7771 - val_loss: 1.4210 - val_accuracy: 0.7816 [ 0. 0. 0. ... 0. -0. 0.8478986] Sparsity at: 0.9717844688841202 Epoch 310/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4351 - accuracy: 0.7774 - val_loss: 1.4208 - val_accuracy: 0.7817 [ 0. 0. 0. ... 0. -0. 0.8479132] Sparsity at: 0.9717844688841202 Epoch 311/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4349 - accuracy: 0.7775 - val_loss: 1.4207 - val_accuracy: 0.7818 [ 0. 0. 0. ... 0. -0. 0.84826994] Sparsity at: 0.9717844688841202 Epoch 312/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4347 - accuracy: 0.7776 - val_loss: 1.4206 - val_accuracy: 0.7819 [ 0. 0. 0. ... 0. -0. 0.84840566] Sparsity at: 0.9717844688841202 Epoch 313/500 235/235 [==============================] - 3s 11ms/step - loss: 1.4346 - accuracy: 0.7774 - val_loss: 1.4204 - val_accuracy: 0.7817 [ 0. 0. 0. ... 0. -0. 0.8484093] Sparsity at: 0.9717844688841202 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4345 - accuracy: 0.7776 - val_loss: 1.4203 - val_accuracy: 0.7819 [ 0. 0. 0. ... 0. -0. 0.84843224] Sparsity at: 0.9717844688841202 Epoch 315/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4344 - accuracy: 0.7775 - val_loss: 1.4202 - val_accuracy: 0.7820 [ 0. 0. 0. ... 0. -0. 0.84858185] Sparsity at: 0.9717844688841202 Epoch 316/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4343 - accuracy: 0.7775 - val_loss: 1.4202 - val_accuracy: 0.7819 [ 0. 0. 0. ... 0. -0. 0.8484623] Sparsity at: 0.9717844688841202 Epoch 317/500 235/235 [==============================] - 2s 11ms/step - loss: 1.4343 - accuracy: 0.7776 - val_loss: 1.4202 - val_accuracy: 0.7818 [ 0. 0. 0. ... 0. -0. 0.84858584] Sparsity at: 0.9717844688841202 Epoch 318/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4342 - accuracy: 0.7777 - val_loss: 1.4200 - val_accuracy: 0.7822 [ 0. 0. 0. ... 0. -0. 0.8484583] Sparsity at: 0.9717844688841202 Epoch 319/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4342 - accuracy: 0.7776 - val_loss: 1.4200 - val_accuracy: 0.7819 [ 0. 0. 0. ... 0. -0. 0.84858406] Sparsity at: 0.9717844688841202 Epoch 320/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7775 - val_loss: 1.4199 - val_accuracy: 0.7820 [ 0. 0. 0. ... 0. -0. 0.84860015] Sparsity at: 0.9717844688841202 Epoch 321/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7775 - val_loss: 1.4200 - val_accuracy: 0.7822 [ 0. 0. 0. ... 0. -0. 0.8487723] Sparsity at: 0.9717844688841202 Epoch 322/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7823 [ 0. 0. 0. ... 0. -0. 0.8485342] Sparsity at: 0.9717844688841202 Epoch 323/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4341 - accuracy: 0.7777 - val_loss: 1.4199 - val_accuracy: 0.7825 [ 0. 0. 0. ... 0. -0. 0.84870464] Sparsity at: 0.9717844688841202 Epoch 324/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7826 [ 0. 0. 0. ... 0. -0. 0.84863997] Sparsity at: 0.9717844688841202 Epoch 325/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8483867] Sparsity at: 0.9717844688841202 Epoch 326/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7826 [ 0. 0. 0. ... 0. -0. 0.84857863] Sparsity at: 0.9717844688841202 Epoch 327/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7827 [ 0. 0. 0. ... 0. -0. 0.84858686] Sparsity at: 0.9717844688841202 Epoch 328/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8484626] Sparsity at: 0.9717844688841202 Epoch 329/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7827 [ 0. 0. 0. ... 0. -0. 0.84849095] Sparsity at: 0.9717844688841202 Epoch 330/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4340 - accuracy: 0.7776 - val_loss: 1.4199 - val_accuracy: 0.7829 [ 0. 0. 0. ... 0. -0. 0.8485646] Sparsity at: 0.9717844688841202 Epoch 331/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7827 [ 0. 0. 0. ... 0. -0. 0.84840494] Sparsity at: 0.9717844688841202 Epoch 332/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7829 [ 0. 0. 0. ... 0. -0. 0.84838897] Sparsity at: 0.9717844688841202 Epoch 333/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7775 - val_loss: 1.4197 - val_accuracy: 0.7827 [ 0. 0. 0. ... 0. -0. 0.8484002] Sparsity at: 0.9717844688841202 Epoch 334/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8484606] Sparsity at: 0.9717844688841202 Epoch 335/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4197 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8483319] Sparsity at: 0.9717844688841202 Epoch 336/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4197 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8483831] Sparsity at: 0.9717844688841202 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8483909] Sparsity at: 0.9717844688841202 Epoch 338/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7830 [ 0. 0. 0. ... 0. -0. 0.848298] Sparsity at: 0.9717844688841202 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4198 - val_accuracy: 0.7830 [ 0. 0. 0. ... 0. -0. 0.84855366] Sparsity at: 0.9717844688841202 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4198 - val_accuracy: 0.7830 [ 0. 0. 0. ... 0. -0. 0.8484151] Sparsity at: 0.9717844688841202 Epoch 341/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7829 [ 0. 0. 0. ... 0. -0. 0.84838897] Sparsity at: 0.9717844688841202 Epoch 342/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7830 [ 0. 0. 0. ... 0. -0. 0.84850764] Sparsity at: 0.9717844688841202 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7831 [ 0. 0. 0. ... 0. -0. 0.84846354] Sparsity at: 0.9717844688841202 Epoch 344/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4339 - accuracy: 0.7779 - val_loss: 1.4198 - val_accuracy: 0.7830 [ 0. 0. 0. ... 0. -0. 0.8486075] Sparsity at: 0.9717844688841202 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7776 - val_loss: 1.4197 - val_accuracy: 0.7831 [ 0. 0. 0. ... 0. -0. 0.8484398] Sparsity at: 0.9717844688841202 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7829 [ 0. 0. 0. ... 0. -0. 0.84838116] Sparsity at: 0.9717844688841202 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7828 [ 0. 0. 0. ... 0. -0. 0.8483] Sparsity at: 0.9717844688841202 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7831 [ 0. 0. 0. ... 0. -0. 0.8484819] Sparsity at: 0.9717844688841202 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7778 - val_loss: 1.4198 - val_accuracy: 0.7829 [ 0. 0. 0. ... 0. -0. 0.8483743] Sparsity at: 0.9717844688841202 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4339 - accuracy: 0.7777 - val_loss: 1.4197 - val_accuracy: 0.7829 [ 0. 0. 0. ... 0. -0. 0.8482588] Sparsity at: 0.9717844688841202 Epoch 351/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7362 - accuracy: 0.5803 - val_loss: 1.6912 - val_accuracy: 0.5809 [ 0. 0. 0. ... 0. -0. 0.8864601] Sparsity at: 0.9845426502145923 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6842 - accuracy: 0.6015 - val_loss: 1.6785 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.88791704] Sparsity at: 0.9845426502145923 Epoch 353/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6782 - accuracy: 0.6061 - val_loss: 1.6759 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.88857573] Sparsity at: 0.9845426502145923 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6767 - accuracy: 0.6061 - val_loss: 1.6750 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.889125] Sparsity at: 0.9845426502145923 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6762 - accuracy: 0.6057 - val_loss: 1.6747 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.8896398] Sparsity at: 0.9845426502145923 Epoch 356/500 235/235 [==============================] - 3s 11ms/step - loss: 1.6759 - accuracy: 0.6057 - val_loss: 1.6744 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8902248] Sparsity at: 0.9845426502145923 Epoch 357/500 235/235 [==============================] - 3s 11ms/step - loss: 1.6758 - accuracy: 0.6057 - val_loss: 1.6744 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.8906863] Sparsity at: 0.9845426502145923 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6757 - accuracy: 0.6055 - val_loss: 1.6743 - val_accuracy: 0.6038 [ 0. 0. 0. ... 0. -0. 0.8908476] Sparsity at: 0.9845426502145923 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6056 - val_loss: 1.6742 - val_accuracy: 0.6039 [ 0. 0. 0. ... 0. -0. 0.89108646] Sparsity at: 0.9845426502145923 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6056 - val_loss: 1.6742 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8913852] Sparsity at: 0.9845426502145923 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6055 - val_loss: 1.6742 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89179623] Sparsity at: 0.9845426502145923 Epoch 362/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6756 - accuracy: 0.6055 - val_loss: 1.6742 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.89191407] Sparsity at: 0.9845426502145923 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89185876] Sparsity at: 0.9845426502145923 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.892028] Sparsity at: 0.9845426502145923 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8921946] Sparsity at: 0.9845426502145923 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6033 [ 0. 0. 0. ... 0. -0. 0.89231884] Sparsity at: 0.9845426502145923 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.8921553] Sparsity at: 0.9845426502145923 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8922771] Sparsity at: 0.9845426502145923 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8924218] Sparsity at: 0.9845426502145923 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8924572] Sparsity at: 0.9845426502145923 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8924386] Sparsity at: 0.9845426502145923 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6054 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89247346] Sparsity at: 0.9845426502145923 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89250517] Sparsity at: 0.9845426502145923 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.8927145] Sparsity at: 0.9845426502145923 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.8925544] Sparsity at: 0.9845426502145923 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.8926245] Sparsity at: 0.9845426502145923 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6033 [ 0. 0. 0. ... 0. -0. 0.892683] Sparsity at: 0.9845426502145923 Epoch 378/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.89268535] Sparsity at: 0.9845426502145923 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.89262277] Sparsity at: 0.9845426502145923 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8925358] Sparsity at: 0.9845426502145923 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89253837] Sparsity at: 0.9845426502145923 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.8925617] Sparsity at: 0.9845426502145923 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6038 [ 0. 0. 0. ... 0. -0. 0.89263034] Sparsity at: 0.9845426502145923 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8926805] Sparsity at: 0.9845426502145923 Epoch 385/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.89263535] Sparsity at: 0.9845426502145923 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6032 [ 0. 0. 0. ... 0. -0. 0.89255166] Sparsity at: 0.9845426502145923 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8927294] Sparsity at: 0.9845426502145923 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6740 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.8926091] Sparsity at: 0.9845426502145923 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.89263994] Sparsity at: 0.9845426502145923 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89255685] Sparsity at: 0.9845426502145923 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8927181] Sparsity at: 0.9845426502145923 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6053 - val_loss: 1.6741 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8926914] Sparsity at: 0.9845426502145923 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6037 [ 0. 0. 0. ... 0. -0. 0.8925936] Sparsity at: 0.9845426502145923 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8926167] Sparsity at: 0.9845426502145923 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6052 - val_loss: 1.6741 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.8928026] Sparsity at: 0.9845426502145923 Epoch 396/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.8926983] Sparsity at: 0.9845426502145923 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89268494] Sparsity at: 0.9845426502145923 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6034 [ 0. 0. 0. ... 0. -0. 0.89276546] Sparsity at: 0.9845426502145923 Epoch 399/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6741 - val_accuracy: 0.6036 [ 0. 0. 0. ... 0. -0. 0.89261985] Sparsity at: 0.9845426502145923 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6755 - accuracy: 0.6051 - val_loss: 1.6740 - val_accuracy: 0.6035 [ 0. 0. 0. ... 0. -0. 0.8926298] Sparsity at: 0.9845426502145923 Epoch 401/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8498 - accuracy: 0.4744 - val_loss: 1.8203 - val_accuracy: 0.4758 [ 0. 0. 0. ... 0. -0. 0.8776634] Sparsity at: 0.9892871512875536 Epoch 402/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8166 - accuracy: 0.4764 - val_loss: 1.8107 - val_accuracy: 0.4737 [ 0. 0. 0. ... 0. -0. 0.8803248] Sparsity at: 0.9892871512875536 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8121 - accuracy: 0.5045 - val_loss: 1.8087 - val_accuracy: 0.5155 [ 0. 0. 0. ... 0. -0. 0.88228405] Sparsity at: 0.9892871512875536 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8110 - accuracy: 0.5185 - val_loss: 1.8081 - val_accuracy: 0.5153 [ 0. 0. 0. ... 0. -0. 0.8834614] Sparsity at: 0.9892871512875536 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8107 - accuracy: 0.5186 - val_loss: 1.8079 - val_accuracy: 0.5152 [ 0. 0. 0. ... 0. -0. 0.8843171] Sparsity at: 0.9892871512875536 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8105 - accuracy: 0.5185 - val_loss: 1.8078 - val_accuracy: 0.5154 [ 0. 0. 0. ... 0. -0. 0.8848582] Sparsity at: 0.9892871512875536 Epoch 407/500 235/235 [==============================] - 3s 11ms/step - loss: 1.8105 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.8855228] Sparsity at: 0.9892871512875536 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8105 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.8858662] Sparsity at: 0.9892871512875536 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5153 [ 0. 0. 0. ... 0. -0. 0.8859212] Sparsity at: 0.9892871512875536 Epoch 410/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8077 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.8862884] Sparsity at: 0.9892871512875536 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5152 [ 0. 0. 0. ... 0. -0. 0.88648665] Sparsity at: 0.9892871512875536 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5154 [ 0. 0. 0. ... 0. -0. 0.8866848] Sparsity at: 0.9892871512875536 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5153 [ 0. 0. 0. ... 0. -0. 0.8867208] Sparsity at: 0.9892871512875536 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5152 [ 0. 0. 0. ... 0. -0. 0.8870142] Sparsity at: 0.9892871512875536 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5152 [ 0. 0. 0. ... 0. -0. 0.8869678] Sparsity at: 0.9892871512875536 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.88720185] Sparsity at: 0.9892871512875536 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5182 - val_loss: 1.8076 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.8872306] Sparsity at: 0.9892871512875536 Epoch 418/500 235/235 [==============================] - 3s 12ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5151 [ 0. 0. 0. ... 0. -0. 0.8872322] Sparsity at: 0.9892871512875536 Epoch 419/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5151 [ 0. 0. 0. ... 0. -0. 0.8873701] Sparsity at: 0.9892871512875536 Epoch 420/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8873265] Sparsity at: 0.9892871512875536 Epoch 421/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149 [ 0. 0. 0. ... 0. -0. 0.88742524] Sparsity at: 0.9892871512875536 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.88729703] Sparsity at: 0.9892871512875536 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5150 [ 0. 0. 0. ... 0. -0. 0.88741076] Sparsity at: 0.9892871512875536 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149 [ 0. 0. 0. ... 0. -0. 0.8873623] Sparsity at: 0.9892871512875536 Epoch 425/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88746285] Sparsity at: 0.9892871512875536 Epoch 426/500 235/235 [==============================] - 2s 11ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88742024] Sparsity at: 0.9892871512875536 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.8875567] Sparsity at: 0.9892871512875536 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88754386] Sparsity at: 0.9892871512875536 Epoch 429/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.8874343] Sparsity at: 0.9892871512875536 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5144 [ 0. 0. 0. ... 0. -0. 0.8874935] Sparsity at: 0.9892871512875536 Epoch 431/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.8874417] Sparsity at: 0.9892871512875536 Epoch 432/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8874639] Sparsity at: 0.9892871512875536 Epoch 433/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8876132] Sparsity at: 0.9892871512875536 Epoch 434/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8875087] Sparsity at: 0.9892871512875536 Epoch 435/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5145 [ 0. 0. 0. ... 0. -0. 0.88745046] Sparsity at: 0.9892871512875536 Epoch 436/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5149 [ 0. 0. 0. ... 0. -0. 0.88748956] Sparsity at: 0.9892871512875536 Epoch 437/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5143 [ 0. 0. 0. ... 0. -0. 0.88747364] Sparsity at: 0.9892871512875536 Epoch 438/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8876321] Sparsity at: 0.9892871512875536 Epoch 439/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88764673] Sparsity at: 0.9892871512875536 Epoch 440/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8877175] Sparsity at: 0.9892871512875536 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875513] Sparsity at: 0.9892871512875536 Epoch 442/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88752633] Sparsity at: 0.9892871512875536 Epoch 443/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149 [ 0. 0. 0. ... 0. -0. 0.88751143] Sparsity at: 0.9892871512875536 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8875532] Sparsity at: 0.9892871512875536 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8875033] Sparsity at: 0.9892871512875536 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88757986] Sparsity at: 0.9892871512875536 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.887458] Sparsity at: 0.9892871512875536 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875297] Sparsity at: 0.9892871512875536 Epoch 449/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88765854] Sparsity at: 0.9892871512875536 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88760966] Sparsity at: 0.9892871512875536 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88772136] Sparsity at: 0.9892871512875536 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5151 [ 0. 0. 0. ... 0. -0. 0.8875618] Sparsity at: 0.9892871512875536 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88762987] Sparsity at: 0.9892871512875536 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.887594] Sparsity at: 0.9892871512875536 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875695] Sparsity at: 0.9892871512875536 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8876889] Sparsity at: 0.9892871512875536 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8873603] Sparsity at: 0.9892871512875536 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8875618] Sparsity at: 0.9892871512875536 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88742465] Sparsity at: 0.9892871512875536 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5151 [ 0. 0. 0. ... 0. -0. 0.88764745] Sparsity at: 0.9892871512875536 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88739806] Sparsity at: 0.9892871512875536 Epoch 462/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88756514] Sparsity at: 0.9892871512875536 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.8874534] Sparsity at: 0.9892871512875536 Epoch 464/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5149 [ 0. 0. 0. ... 0. -0. 0.88758135] Sparsity at: 0.9892871512875536 Epoch 465/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8874519] Sparsity at: 0.9892871512875536 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8876314] Sparsity at: 0.9892871512875536 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88750505] Sparsity at: 0.9892871512875536 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88767457] Sparsity at: 0.9892871512875536 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88755065] Sparsity at: 0.9892871512875536 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88759047] Sparsity at: 0.9892871512875536 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5182 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88760424] Sparsity at: 0.9892871512875536 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875611] Sparsity at: 0.9892871512875536 Epoch 473/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5182 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8876005] Sparsity at: 0.9892871512875536 Epoch 474/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875723] Sparsity at: 0.9892871512875536 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875916] Sparsity at: 0.9892871512875536 Epoch 476/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88760126] Sparsity at: 0.9892871512875536 Epoch 477/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88760597] Sparsity at: 0.9892871512875536 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8876239] Sparsity at: 0.9892871512875536 Epoch 479/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.887567] Sparsity at: 0.9892871512875536 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5145 [ 0. 0. 0. ... 0. -0. 0.8875106] Sparsity at: 0.9892871512875536 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.887612] Sparsity at: 0.9892871512875536 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.8875263] Sparsity at: 0.9892871512875536 Epoch 483/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5144 [ 0. 0. 0. ... 0. -0. 0.8873811] Sparsity at: 0.9892871512875536 Epoch 484/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88744867] Sparsity at: 0.9892871512875536 Epoch 485/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88762844] Sparsity at: 0.9892871512875536 Epoch 486/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88762647] Sparsity at: 0.9892871512875536 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.8875807] Sparsity at: 0.9892871512875536 Epoch 488/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88773155] Sparsity at: 0.9892871512875536 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8075 - val_accuracy: 0.5145 [ 0. 0. 0. ... 0. -0. 0.8874146] Sparsity at: 0.9892871512875536 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88756335] Sparsity at: 0.9892871512875536 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8875651] Sparsity at: 0.9892871512875536 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5149 [ 0. 0. 0. ... 0. -0. 0.8874644] Sparsity at: 0.9892871512875536 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88747734] Sparsity at: 0.9892871512875536 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8075 - val_accuracy: 0.5144 [ 0. 0. 0. ... 0. -0. 0.88746727] Sparsity at: 0.9892871512875536 Epoch 495/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.88756764] Sparsity at: 0.9892871512875536 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88769954] Sparsity at: 0.9892871512875536 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5185 - val_loss: 1.8076 - val_accuracy: 0.5148 [ 0. 0. 0. ... 0. -0. 0.8875874] Sparsity at: 0.9892871512875536 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.88755345] Sparsity at: 0.9892871512875536 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5183 - val_loss: 1.8076 - val_accuracy: 0.5146 [ 0. 0. 0. ... 0. -0. 0.8875806] Sparsity at: 0.9892871512875536 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8104 - accuracy: 0.5184 - val_loss: 1.8076 - val_accuracy: 0.5147 [ 0. 0. 0. ... 0. -0. 0.88751596] Sparsity at: 0.9892871512875536 Epoch 1/500 235/235 [==============================] - 4s 9ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.2421 - val_accuracy: 0.9732 [-0. 0. 0. ... -0.5949296 -0.7967351 0.9044952] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2970e-04 - accuracy: 0.9999 - val_loss: 0.2392 - val_accuracy: 0.9742 [-0. 0. 0. ... -0.59087783 -0.8018444 0.9013458 ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 8.8304e-04 - accuracy: 0.9997 - val_loss: 0.2475 - val_accuracy: 0.9722 [-0. 0. 0. ... -0.5801735 -0.8080663 0.90034556] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9990 - val_loss: 0.2524 - val_accuracy: 0.9729 [-0. 0. 0. ... -0.602204 -0.79381484 0.9078741 ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0023 - accuracy: 0.9991 - val_loss: 0.2354 - val_accuracy: 0.9740 [-0. 0. 0. ... -0.62556905 -0.7988888 0.92145705] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9111e-04 - accuracy: 0.9999 - val_loss: 0.2329 - val_accuracy: 0.9751 [-0. 0. 0. ... -0.62521774 -0.796512 0.9181671 ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 7.6756e-05 - accuracy: 1.0000 - val_loss: 0.2331 - val_accuracy: 0.9748 [-0. 0. 0. ... -0.62813056 -0.7966049 0.919858 ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3206e-05 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9748 [-0. 0. 0. ... -0.62881666 -0.7960898 0.920007 ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4910e-05 - accuracy: 1.0000 - val_loss: 0.2321 - val_accuracy: 0.9748 [-0. 0. 0. ... -0.6293421 -0.79582816 0.92004746] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2867e-05 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9748 [-0. 0. 0. ... -0.6298372 -0.795629 0.92011446] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1484e-05 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9748 [-0. 0. 0. ... -0.63031375 -0.79545784 0.9201984 ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0413e-05 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9748 [-0. 0. 0. ... -0.63079536 -0.795314 0.9202968 ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 9ms/step - loss: 9.5318e-06 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9747 [-0. 0. 0. ... -0.63127935 -0.79518884 0.92041177] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 8.7837e-06 - accuracy: 1.0000 - val_loss: 0.2311 - val_accuracy: 0.9747 [-0. 0. 0. ... -0.6317732 -0.79508007 0.92054445] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 8.1289e-06 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9747 [-0. 0. 0. ... -0.63228625 -0.7949868 0.9206967 ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5469e-06 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6328092 -0.7949071 0.92086804] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0246e-06 - accuracy: 1.0000 - val_loss: 0.2308 - val_accuracy: 0.9750 [-0. 0. 0. ... -0.63334644 -0.7948411 0.9210628 ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5508e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9749 [-0. 0. 0. ... -0.6339009 -0.79478765 0.9212787 ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1162e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9749 [-0. 0. 0. ... -0.63448054 -0.7947449 0.9215186 ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7176e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9749 [-0. 0. 0. ... -0.6350746 -0.7947143 0.9217835] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3487e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9749 [-0. 0. 0. ... -0.63569397 -0.79469824 0.92207736] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0078e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9750 [-0. 0. 0. ... -0.6363327 -0.79469347 0.92239517] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6904e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9750 [-0. 0. 0. ... -0.6369989 -0.79469824 0.9227408 ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3948e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9751 [-0. 0. 0. ... -0.6376965 -0.79471636 0.9231179 ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1186e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6384237 -0.79474336 0.9235218 ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8610e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9753 [-0. 0. 0. ... -0.63917726 -0.7947878 0.9239607 ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6202e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9753 [-0. 0. 0. ... -0.6399645 -0.794844 0.92442757] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3936e-06 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9753 [-0. 0. 0. ... -0.64078605 -0.7949133 0.9249269 ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1808e-06 - accuracy: 1.0000 - val_loss: 0.2308 - val_accuracy: 0.9754 [-0. 0. 0. ... -0.64164996 -0.7949979 0.92546123] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9819e-06 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9753 [-0. 0. 0. ... -0.6425526 -0.79509676 0.9260302 ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7956e-06 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9753 [-0. 0. 0. ... -0.64350206 -0.79521537 0.92663527] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6200e-06 - accuracy: 1.0000 - val_loss: 0.2311 - val_accuracy: 0.9754 [-0. 0. 0. ... -0.6445055 -0.79535216 0.92727697] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4542e-06 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9755 [-0. 0. 0. ... -0.64555746 -0.7955067 0.9279571 ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2978e-06 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9755 [-0. 0. 0. ... -0.646664 -0.795693 0.92867565] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 10ms/step - loss: 2.1521e-06 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9757 [-0. 0. 0. ... -0.6478337 -0.7958971 0.92943966] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0138e-06 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9757 [-0. 0. 0. ... -0.64906716 -0.7961287 0.9302437 ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8834e-06 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9756 [-0. 0. 0. ... -0.65036595 -0.79639417 0.93109155] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7604e-06 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9756 [-0. 0. 0. ... -0.6517346 -0.7966881 0.93198645] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6448e-06 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9756 [-0. 0. 0. ... -0.65318346 -0.7970313 0.9329234 ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5358e-06 - accuracy: 1.0000 - val_loss: 0.2327 - val_accuracy: 0.9755 [-0. 0. 0. ... -0.65468884 -0.7974118 0.9339063 ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4337e-06 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9754 [-0. 0. 0. ... -0.656279 -0.79783255 0.9349413 ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3364e-06 - accuracy: 1.0000 - val_loss: 0.2333 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6579501 -0.7982985 0.9360304] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2455e-06 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6597102 -0.79880923 0.93717694] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1596e-06 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.66152585 -0.79937977 0.9383721 ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0792e-06 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6634188 -0.80001545 0.93963134] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0034e-06 - accuracy: 1.0000 - val_loss: 0.2348 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6653801 -0.80070084 0.9409624 ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 9ms/step - loss: 9.3268e-07 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9752 [-0. 0. 0. ... -0.6674318 -0.80144906 0.9423237 ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 8.6612e-07 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9754 [-0. 0. 0. ... -0.6695709 -0.8022618 0.94375974] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 8.0352e-07 - accuracy: 1.0000 - val_loss: 0.2362 - val_accuracy: 0.9756 [-0. 0. 0. ... -0.6717647 -0.80314386 0.9452477 ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 7.4525e-07 - accuracy: 1.0000 - val_loss: 0.2367 - val_accuracy: 0.9756 [-0. 0. 0. ... -0.6740259 -0.8040751 0.94679165] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0231 - accuracy: 0.9931 - val_loss: 0.1847 - val_accuracy: 0.9731 [-0. 0. 0. ... 0. -0.73509145 0.8957523 ] Sparsity at: 0.6458724517167382 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0029 - accuracy: 0.9991 - val_loss: 0.1744 - val_accuracy: 0.9751 [-0. 0. 0. ... 0. -0.7311096 0.8890226] Sparsity at: 0.6458724517167382 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 6.2802e-04 - accuracy: 0.9999 - val_loss: 0.1748 - val_accuracy: 0.9744 [-0. 0. 0. ... 0. -0.7297645 0.8887018] Sparsity at: 0.6458724517167382 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2520e-04 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9747 [-0. 0. 0. ... 0. -0.73272973 0.88878936] Sparsity at: 0.6458724517167382 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5118e-04 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9746 [-0. 0. 0. ... 0. -0.7334399 0.88943857] Sparsity at: 0.6458724517167382 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 2.1605e-04 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9748 [-0. 0. 0. ... 0. -0.73405415 0.8899287 ] Sparsity at: 0.6458724517167382 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9124e-04 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9748 [-0. 0. 0. ... 0. -0.7346046 0.89040333] Sparsity at: 0.6458724517167382 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7160e-04 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9750 [-0. 0. 0. ... -0. -0.73518705 0.8909222 ] Sparsity at: 0.6458724517167382 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5545e-04 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9747 [-0. 0. 0. ... 0. -0.735778 0.8914976] Sparsity at: 0.6458724517167382 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4143e-04 - accuracy: 1.0000 - val_loss: 0.1792 - val_accuracy: 0.9747 [-0. 0. 0. ... 0. -0.7364397 0.892144 ] Sparsity at: 0.6458724517167382 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2928e-04 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9748 [-0. 0. 0. ... 0. -0.7371226 0.89282554] Sparsity at: 0.6458724517167382 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1838e-04 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9748 [-0. 0. 0. ... 0. -0.73789334 0.8935804 ] Sparsity at: 0.6458724517167382 Epoch 63/500 235/235 [==============================] - 2s 10ms/step - loss: 1.0872e-04 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9748 [-0. 0. 0. ... 0. -0.73870784 0.8944164 ] Sparsity at: 0.6458724517167382 Epoch 64/500 235/235 [==============================] - 2s 10ms/step - loss: 9.9934e-05 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7395601 0.89531624] Sparsity at: 0.6458724517167382 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 9.1982e-05 - accuracy: 1.0000 - val_loss: 0.1820 - val_accuracy: 0.9750 [-0. 0. 0. ... 0. -0.7404641 0.896323 ] Sparsity at: 0.6458724517167382 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 8.4667e-05 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9750 [-0. 0. 0. ... 0. -0.74144167 0.89741373] Sparsity at: 0.6458724517167382 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 7.7973e-05 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.9750 [-0. 0. 0. ... 0. -0.74249196 0.8986089 ] Sparsity at: 0.6458724517167382 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 7.1826e-05 - accuracy: 1.0000 - val_loss: 0.1837 - val_accuracy: 0.9750 [-0. 0. 0. ... -0. -0.7435539 0.899926 ] Sparsity at: 0.6458724517167382 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 6.6187e-05 - accuracy: 1.0000 - val_loss: 0.1844 - val_accuracy: 0.9751 [-0. 0. 0. ... 0. -0.7447125 0.9012957] Sparsity at: 0.6458724517167382 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 6.0907e-05 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9751 [-0. 0. 0. ... 0. -0.7458928 0.9028403] Sparsity at: 0.6458724517167382 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6040e-05 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9751 [-0. 0. 0. ... -0. -0.7471239 0.90447205] Sparsity at: 0.6458724517167382 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1602e-05 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7484403 0.9061902] Sparsity at: 0.6458724517167382 Epoch 73/500 235/235 [==============================] - 2s 9ms/step - loss: 4.7446e-05 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7498223 0.9080335] Sparsity at: 0.6458724517167382 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3599e-05 - accuracy: 1.0000 - val_loss: 0.1877 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7512304 0.9099991] Sparsity at: 0.6458724517167382 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0024e-05 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9752 [-0. 0. 0. ... -0. -0.75272524 0.9121028 ] Sparsity at: 0.6458724517167382 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6722e-05 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9753 [-0. 0. 0. ... 0. -0.7542807 0.9142741] Sparsity at: 0.6458724517167382 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3653e-05 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9753 [-0. 0. 0. ... 0. -0.7558255 0.9166251] Sparsity at: 0.6458724517167382 Epoch 78/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0836e-05 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9751 [-0. 0. 0. ... 0. -0.7575107 0.91898817] Sparsity at: 0.6458724517167382 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8210e-05 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9751 [-0. 0. 0. ... -0. -0.7592358 0.9214902] Sparsity at: 0.6458724517167382 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5803e-05 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.76105595 0.9241198 ] Sparsity at: 0.6458724517167382 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3605e-05 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9749 [-0. 0. 0. ... 0. -0.7629121 0.92692345] Sparsity at: 0.6458724517167382 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 2.1514e-05 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7648923 0.9298033] Sparsity at: 0.6458724517167382 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9612e-05 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7670017 0.9327651] Sparsity at: 0.6458724517167382 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7861e-05 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.76916486 0.93593115] Sparsity at: 0.6458724517167382 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6245e-05 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7714767 0.93912816] Sparsity at: 0.6458724517167382 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4757e-05 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9748 [-0. 0. 0. ... -0. -0.7738235 0.94240904] Sparsity at: 0.6458724517167382 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3421e-05 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9748 [-0. 0. 0. ... -0. -0.77635175 0.94593024] Sparsity at: 0.6458724517167382 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2177e-05 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9748 [-0. 0. 0. ... -0. -0.778937 0.9494618] Sparsity at: 0.6458724517167382 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1035e-05 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.78163123 0.95317775] Sparsity at: 0.6458724517167382 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 9.9931e-06 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7843923 0.9569912] Sparsity at: 0.6458724517167382 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 9.0354e-06 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7872921 0.96092635] Sparsity at: 0.6458724517167382 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 8.1656e-06 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.790321 0.9649001] Sparsity at: 0.6458724517167382 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 7.3798e-06 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9749 [-0. 0. 0. ... -0. -0.7934454 0.9690302] Sparsity at: 0.6458724517167382 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 6.6585e-06 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9750 [-0. 0. 0. ... -0. -0.7966488 0.9732155] Sparsity at: 0.6458724517167382 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 6.0068e-06 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9753 [-0. 0. 0. ... -0. -0.79995257 0.97753745] Sparsity at: 0.6458724517167382 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4153e-06 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9753 [-0. 0. 0. ... -0. -0.803302 0.98189294] Sparsity at: 0.6458724517167382 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 4.8784e-06 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9752 [-0. 0. 0. ... -0. -0.8067683 0.9863515] Sparsity at: 0.6458724517167382 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3888e-06 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9752 [-0. 0. 0. ... -0. -0.8103193 0.9909247] Sparsity at: 0.6458724517167382 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9469e-06 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9752 [-0. 0. 0. ... -0. -0.8139793 0.9955227] Sparsity at: 0.6458724517167382 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5462e-06 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9752 [-0. 0. 0. ... -0. -0.81765753 1.0002588 ] Sparsity at: 0.6458724517167382 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0385 - accuracy: 0.9883 - val_loss: 0.1706 - val_accuracy: 0.9708 [-0. 0. 0. ... -0. -0. 1.0135858] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0088 - accuracy: 0.9971 - val_loss: 0.1634 - val_accuracy: 0.9714 [-0. 0. 0. ... -0. 0. 1.0286574] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9990 - val_loss: 0.1615 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.041981] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0028 - accuracy: 0.9998 - val_loss: 0.1610 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.0521779] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1608 - val_accuracy: 0.9729 [-0. 0. 0. ... -0. 0. 1.062363] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1609 - val_accuracy: 0.9732 [-0. 0. 0. ... -0. -0. 1.0719025] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9736 [-0. 0. 0. ... -0. -0. 1.0807847] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1617 - val_accuracy: 0.9737 [-0. 0. 0. ... -0. -0. 1.0893971] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1623 - val_accuracy: 0.9737 [-0. 0. 0. ... -0. -0. 1.0978664] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 9.6154e-04 - accuracy: 1.0000 - val_loss: 0.1629 - val_accuracy: 0.9737 [-0. 0. 0. ... -0. -0. 1.1060125] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5414e-04 - accuracy: 1.0000 - val_loss: 0.1635 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. -0. 1.1139268] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 7.6361e-04 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. -0. 1.1221764] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 6.8692e-04 - accuracy: 1.0000 - val_loss: 0.1648 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. -0. 1.1304115] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1952e-04 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. -0. 1.1386477] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6040e-04 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. -0. 1.1467956] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0676e-04 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. -0. 1.1547996] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6018e-04 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. -0. 1.1632582] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1757e-04 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. 0. 1.1718184] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7923e-04 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. 0. 1.1802437] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4450e-04 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. 0. 1.1888474] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1284e-04 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9742 [-0. 0. 0. ... -0. 0. 1.1975056] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8426e-04 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9743 [-0. 0. 0. ... -0. 0. 1.2064425] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5859e-04 - accuracy: 1.0000 - val_loss: 0.1745 - val_accuracy: 0.9743 [-0. 0. 0. ... -0. -0. 1.2154092] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3445e-04 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9742 [-0. 0. 0. ... -0. 0. 1.2244335] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 2.1282e-04 - accuracy: 1.0000 - val_loss: 0.1771 - val_accuracy: 0.9744 [-0. 0. 0. ... -0. 0. 1.2337043] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9352e-04 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9742 [-0. 0. 0. ... -0. -0. 1.2427634] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7537e-04 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9743 [-0. 0. 0. ... -0. 0. 1.252267] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5901e-04 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9742 [-0. 0. 0. ... -0. 0. 1.2615753] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4396e-04 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9742 [-0. 0. 0. ... -0. 0. 1.271116] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3044e-04 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. 0. 1.2802848] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1803e-04 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. 0. 1.290245] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0670e-04 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. 0. 1.2998358] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 9.6437e-05 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9740 [-0. 0. 0. ... -0. 0. 1.309438] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 8.7060e-05 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. 0. 1.318891] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 7.8697e-05 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. 0. 1.3285699] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0943e-05 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9741 [-0. 0. 0. ... -0. 0. 1.3384598] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3961e-05 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9739 [-0. 0. 0. ... -0. 0. 1.3484141] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7812e-05 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9738 [-0. 0. 0. ... -0. 0. 1.3582155] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 5.2114e-05 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9738 [-0. 0. 0. ... -0. 0. 1.368312] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6834e-05 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9736 [-0. 0. 0. ... -0. 0. 1.3782719] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2158e-05 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9736 [-0. 0. 0. ... -0. 0. 1.3880044] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8001e-05 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9733 [-0. 0. 0. ... -0. 0. 1.3980275] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4147e-05 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9730 [-0. 0. 0. ... -0. 0. 1.4080955] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0713e-05 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9730 [-0. 0. 0. ... -0. -0. 1.4181806] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7617e-05 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9733 [-0. 0. 0. ... -0. -0. 1.4283594] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4846e-05 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9731 [-0. 0. 0. ... -0. 0. 1.4382554] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2242e-05 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9733 [-0. 0. 0. ... -0. 0. 1.448506] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9989e-05 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9732 [-0. 0. 0. ... -0. -0. 1.4585552] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8007e-05 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9733 [-0. 0. 0. ... -0. 0. 1.4686309] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6131e-05 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9731 [-0. 0. 0. ... -0. -0. 1.4790257] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0777 - accuracy: 0.9781 - val_loss: 0.1735 - val_accuracy: 0.9691 [-0. 0. 0. ... -0. 0. 1.3690395] Sparsity at: 0.8448229613733905 Epoch 152/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0300 - accuracy: 0.9900 - val_loss: 0.1653 - val_accuracy: 0.9694 [-0. 0. 0. ... -0. 0. 1.3398262] Sparsity at: 0.8448229613733905 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0219 - accuracy: 0.9927 - val_loss: 0.1619 - val_accuracy: 0.9705 [-0. 0. 0. ... -0. 0. 1.3242564] Sparsity at: 0.8448229613733905 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0176 - accuracy: 0.9944 - val_loss: 0.1602 - val_accuracy: 0.9710 [-0. 0. 0. ... -0. 0. 1.3124429] Sparsity at: 0.8448229613733905 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.1592 - val_accuracy: 0.9710 [-0. 0. 0. ... -0. 0. 1.3022195] Sparsity at: 0.8448229613733905 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0127 - accuracy: 0.9964 - val_loss: 0.1588 - val_accuracy: 0.9714 [-0. 0. 0. ... -0. 0. 1.2935442] Sparsity at: 0.8448229613733905 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0111 - accuracy: 0.9972 - val_loss: 0.1586 - val_accuracy: 0.9716 [-0. 0. 0. ... -0. 0. 1.2854189] Sparsity at: 0.8448229613733905 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0099 - accuracy: 0.9979 - val_loss: 0.1590 - val_accuracy: 0.9716 [-0. 0. 0. ... -0. 0. 1.2781818] Sparsity at: 0.8448229613733905 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0088 - accuracy: 0.9982 - val_loss: 0.1592 - val_accuracy: 0.9718 [-0. 0. 0. ... -0. 0. 1.2717553] Sparsity at: 0.8448229613733905 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0080 - accuracy: 0.9986 - val_loss: 0.1597 - val_accuracy: 0.9719 [-0. 0. 0. ... -0. 0. 1.2666832] Sparsity at: 0.8448229613733905 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0073 - accuracy: 0.9989 - val_loss: 0.1601 - val_accuracy: 0.9717 [-0. 0. 0. ... -0. 0. 1.2621882] Sparsity at: 0.8448229613733905 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0066 - accuracy: 0.9990 - val_loss: 0.1607 - val_accuracy: 0.9716 [-0. 0. 0. ... -0. 0. 1.2579225] Sparsity at: 0.8448229613733905 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0061 - accuracy: 0.9992 - val_loss: 0.1613 - val_accuracy: 0.9717 [-0. 0. 0. ... -0. 0. 1.2542441] Sparsity at: 0.8448229613733905 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0056 - accuracy: 0.9994 - val_loss: 0.1619 - val_accuracy: 0.9715 [-0. 0. 0. ... -0. 0. 1.2514261] Sparsity at: 0.8448229613733905 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0052 - accuracy: 0.9995 - val_loss: 0.1625 - val_accuracy: 0.9714 [-0. 0. 0. ... -0. 0. 1.2488104] Sparsity at: 0.8448229613733905 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0048 - accuracy: 0.9996 - val_loss: 0.1630 - val_accuracy: 0.9717 [-0. 0. 0. ... -0. 0. 1.246735] Sparsity at: 0.8448229613733905 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9997 - val_loss: 0.1636 - val_accuracy: 0.9720 [-0. 0. 0. ... 0. 0. 1.2449026] Sparsity at: 0.8448229613733905 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0041 - accuracy: 0.9998 - val_loss: 0.1642 - val_accuracy: 0.9720 [-0. 0. 0. ... -0. 0. 1.2436571] Sparsity at: 0.8448229613733905 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9998 - val_loss: 0.1650 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.2431991] Sparsity at: 0.8448229613733905 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0035 - accuracy: 0.9999 - val_loss: 0.1657 - val_accuracy: 0.9721 [-0. 0. 0. ... -0. 0. 1.2426915] Sparsity at: 0.8448229613733905 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0033 - accuracy: 0.9999 - val_loss: 0.1664 - val_accuracy: 0.9723 [-0. 0. 0. ... -0. 0. 1.2427006] Sparsity at: 0.8448229613733905 Epoch 172/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0031 - accuracy: 0.9999 - val_loss: 0.1671 - val_accuracy: 0.9724 [-0. 0. 0. ... -0. 0. 1.243142] Sparsity at: 0.8448229613733905 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9726 [-0. 0. 0. ... -0. 0. 1.2440412] Sparsity at: 0.8448229613733905 Epoch 174/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1690 - val_accuracy: 0.9725 [-0. 0. 0. ... -0. 0. 1.2454926] Sparsity at: 0.8448229613733905 Epoch 175/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9725 [-0. 0. 0. ... -0. 0. 1.2470108] Sparsity at: 0.8448229613733905 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1709 - val_accuracy: 0.9726 [-0. 0. 0. ... -0. 0. 1.248603] Sparsity at: 0.8448229613733905 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9727 [-0. 0. 0. ... -0. 0. 1.2511225] Sparsity at: 0.8448229613733905 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9727 [-0. 0. 0. ... -0. 0. 1.2533319] Sparsity at: 0.8448229613733905 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9726 [-0. 0. 0. ... -0. 0. 1.2558413] Sparsity at: 0.8448229613733905 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1749 - val_accuracy: 0.9726 [-0. 0. 0. ... -0. 0. 1.258688] Sparsity at: 0.8448229613733905 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9723 [-0. 0. 0. ... 0. 0. 1.2622253] Sparsity at: 0.8448229613733905 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1772 - val_accuracy: 0.9719 [-0. 0. 0. ... -0. 0. 1.2659397] Sparsity at: 0.8448229613733905 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9719 [-0. 0. 0. ... -0. 0. 1.2693189] Sparsity at: 0.8448229613733905 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1799 - val_accuracy: 0.9720 [-0. 0. 0. ... -0. 0. 1.2732551] Sparsity at: 0.8448229613733905 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9723 [-0. 0. 0. ... -0. 0. 1.2776635] Sparsity at: 0.8448229613733905 Epoch 186/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9723 [-0. 0. 0. ... -0. 0. 1.2825797] Sparsity at: 0.8448229613733905 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9723 [-0. 0. 0. ... -0. 0. 1.2869611] Sparsity at: 0.8448229613733905 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9724 [-0. 0. 0. ... -0. 0. 1.2919506] Sparsity at: 0.8448229613733905 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 9.8149e-04 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.2971457] Sparsity at: 0.8448229613733905 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 9.1682e-04 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9723 [-0. 0. 0. ... -0. 0. 1.3026649] Sparsity at: 0.8448229613733905 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5529e-04 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.308464] Sparsity at: 0.8448229613733905 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 8.0176e-04 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.3141136] Sparsity at: 0.8448229613733905 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 7.4792e-04 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9721 [-0. 0. 0. ... -0. 0. 1.319991] Sparsity at: 0.8448229613733905 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9921e-04 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9721 [-0. 0. 0. ... -0. 0. 1.3260802] Sparsity at: 0.8448229613733905 Epoch 195/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5282e-04 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9722 [-0. 0. 0. ... -0. 0. 1.332319] Sparsity at: 0.8448229613733905 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1015e-04 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9725 [-0. 0. 0. ... -0. 0. 1.3383763] Sparsity at: 0.8448229613733905 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7188e-04 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9725 [-0. 0. 0. ... -0. 0. 1.3453194] Sparsity at: 0.8448229613733905 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3581e-04 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9725 [-0. 0. 0. ... -0. 0. 1.3522477] Sparsity at: 0.8448229613733905 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9731e-04 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9725 [-0. 0. 0. ... -0. 0. 1.3595047] Sparsity at: 0.8448229613733905 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6412e-04 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9724 [-0. 0. 0. ... -0. 0. 1.3669329] Sparsity at: 0.8448229613733905 Epoch 201/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1938 - accuracy: 0.9481 - val_loss: 0.2282 - val_accuracy: 0.9499 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 202/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1021 - accuracy: 0.9687 - val_loss: 0.2040 - val_accuracy: 0.9540 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0852 - accuracy: 0.9732 - val_loss: 0.1914 - val_accuracy: 0.9561 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0760 - accuracy: 0.9757 - val_loss: 0.1829 - val_accuracy: 0.9575 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0695 - accuracy: 0.9775 - val_loss: 0.1767 - val_accuracy: 0.9591 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0649 - accuracy: 0.9787 - val_loss: 0.1718 - val_accuracy: 0.9596 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0612 - accuracy: 0.9796 - val_loss: 0.1680 - val_accuracy: 0.9608 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0581 - accuracy: 0.9804 - val_loss: 0.1648 - val_accuracy: 0.9612 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0556 - accuracy: 0.9812 - val_loss: 0.1623 - val_accuracy: 0.9616 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0535 - accuracy: 0.9821 - val_loss: 0.1599 - val_accuracy: 0.9615 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0516 - accuracy: 0.9827 - val_loss: 0.1580 - val_accuracy: 0.9629 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0500 - accuracy: 0.9831 - val_loss: 0.1563 - val_accuracy: 0.9628 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 213/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0485 - accuracy: 0.9833 - val_loss: 0.1549 - val_accuracy: 0.9634 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0471 - accuracy: 0.9838 - val_loss: 0.1536 - val_accuracy: 0.9642 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0460 - accuracy: 0.9843 - val_loss: 0.1524 - val_accuracy: 0.9640 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0448 - accuracy: 0.9849 - val_loss: 0.1515 - val_accuracy: 0.9641 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0438 - accuracy: 0.9851 - val_loss: 0.1506 - val_accuracy: 0.9645 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0429 - accuracy: 0.9855 - val_loss: 0.1499 - val_accuracy: 0.9648 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0421 - accuracy: 0.9858 - val_loss: 0.1492 - val_accuracy: 0.9646 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0412 - accuracy: 0.9862 - val_loss: 0.1487 - val_accuracy: 0.9647 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0405 - accuracy: 0.9865 - val_loss: 0.1482 - val_accuracy: 0.9647 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0398 - accuracy: 0.9868 - val_loss: 0.1478 - val_accuracy: 0.9646 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0391 - accuracy: 0.9870 - val_loss: 0.1475 - val_accuracy: 0.9648 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0384 - accuracy: 0.9873 - val_loss: 0.1472 - val_accuracy: 0.9648 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0378 - accuracy: 0.9874 - val_loss: 0.1469 - val_accuracy: 0.9650 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0372 - accuracy: 0.9877 - val_loss: 0.1467 - val_accuracy: 0.9655 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0367 - accuracy: 0.9877 - val_loss: 0.1465 - val_accuracy: 0.9653 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0362 - accuracy: 0.9880 - val_loss: 0.1464 - val_accuracy: 0.9653 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0356 - accuracy: 0.9882 - val_loss: 0.1463 - val_accuracy: 0.9654 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0352 - accuracy: 0.9884 - val_loss: 0.1463 - val_accuracy: 0.9654 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0347 - accuracy: 0.9888 - val_loss: 0.1463 - val_accuracy: 0.9655 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0343 - accuracy: 0.9890 - val_loss: 0.1463 - val_accuracy: 0.9660 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0338 - accuracy: 0.9891 - val_loss: 0.1463 - val_accuracy: 0.9661 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0334 - accuracy: 0.9892 - val_loss: 0.1464 - val_accuracy: 0.9662 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0330 - accuracy: 0.9894 - val_loss: 0.1465 - val_accuracy: 0.9663 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0326 - accuracy: 0.9894 - val_loss: 0.1467 - val_accuracy: 0.9663 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0322 - accuracy: 0.9895 - val_loss: 0.1468 - val_accuracy: 0.9663 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0318 - accuracy: 0.9897 - val_loss: 0.1471 - val_accuracy: 0.9664 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0315 - accuracy: 0.9898 - val_loss: 0.1473 - val_accuracy: 0.9664 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0311 - accuracy: 0.9900 - val_loss: 0.1475 - val_accuracy: 0.9662 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0308 - accuracy: 0.9901 - val_loss: 0.1477 - val_accuracy: 0.9664 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.1480 - val_accuracy: 0.9666 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 243/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0301 - accuracy: 0.9905 - val_loss: 0.1482 - val_accuracy: 0.9666 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0298 - accuracy: 0.9906 - val_loss: 0.1485 - val_accuracy: 0.9668 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 245/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0295 - accuracy: 0.9908 - val_loss: 0.1488 - val_accuracy: 0.9667 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0292 - accuracy: 0.9909 - val_loss: 0.1491 - val_accuracy: 0.9666 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 247/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0289 - accuracy: 0.9911 - val_loss: 0.1494 - val_accuracy: 0.9666 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0286 - accuracy: 0.9912 - val_loss: 0.1497 - val_accuracy: 0.9666 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 249/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0283 - accuracy: 0.9913 - val_loss: 0.1501 - val_accuracy: 0.9665 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0281 - accuracy: 0.9915 - val_loss: 0.1504 - val_accuracy: 0.9665 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 251/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4551 - accuracy: 0.8622 - val_loss: 0.3081 - val_accuracy: 0.9078 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2601 - accuracy: 0.9160 - val_loss: 0.2643 - val_accuracy: 0.9209 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 253/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2282 - accuracy: 0.9267 - val_loss: 0.2439 - val_accuracy: 0.9266 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2111 - accuracy: 0.9327 - val_loss: 0.2313 - val_accuracy: 0.9314 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1999 - accuracy: 0.9361 - val_loss: 0.2226 - val_accuracy: 0.9340 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1917 - accuracy: 0.9386 - val_loss: 0.2160 - val_accuracy: 0.9355 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1853 - accuracy: 0.9403 - val_loss: 0.2108 - val_accuracy: 0.9367 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1802 - accuracy: 0.9423 - val_loss: 0.2065 - val_accuracy: 0.9377 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1759 - accuracy: 0.9444 - val_loss: 0.2029 - val_accuracy: 0.9388 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1722 - accuracy: 0.9456 - val_loss: 0.1999 - val_accuracy: 0.9398 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1690 - accuracy: 0.9468 - val_loss: 0.1972 - val_accuracy: 0.9400 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1661 - accuracy: 0.9478 - val_loss: 0.1949 - val_accuracy: 0.9414 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1636 - accuracy: 0.9488 - val_loss: 0.1928 - val_accuracy: 0.9423 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 264/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1613 - accuracy: 0.9495 - val_loss: 0.1910 - val_accuracy: 0.9436 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 265/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1592 - accuracy: 0.9501 - val_loss: 0.1894 - val_accuracy: 0.9445 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1573 - accuracy: 0.9505 - val_loss: 0.1879 - val_accuracy: 0.9446 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 267/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1555 - accuracy: 0.9511 - val_loss: 0.1865 - val_accuracy: 0.9453 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1539 - accuracy: 0.9517 - val_loss: 0.1853 - val_accuracy: 0.9459 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1524 - accuracy: 0.9524 - val_loss: 0.1842 - val_accuracy: 0.9464 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1510 - accuracy: 0.9527 - val_loss: 0.1831 - val_accuracy: 0.9468 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1497 - accuracy: 0.9531 - val_loss: 0.1821 - val_accuracy: 0.9471 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1484 - accuracy: 0.9535 - val_loss: 0.1812 - val_accuracy: 0.9477 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 273/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1473 - accuracy: 0.9537 - val_loss: 0.1804 - val_accuracy: 0.9482 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1462 - accuracy: 0.9539 - val_loss: 0.1796 - val_accuracy: 0.9479 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1451 - accuracy: 0.9542 - val_loss: 0.1788 - val_accuracy: 0.9479 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 276/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1442 - accuracy: 0.9547 - val_loss: 0.1781 - val_accuracy: 0.9483 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1432 - accuracy: 0.9547 - val_loss: 0.1775 - val_accuracy: 0.9485 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1423 - accuracy: 0.9550 - val_loss: 0.1768 - val_accuracy: 0.9487 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1415 - accuracy: 0.9552 - val_loss: 0.1762 - val_accuracy: 0.9487 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 280/500 235/235 [==============================] - 2s 10ms/step - loss: 0.1407 - accuracy: 0.9554 - val_loss: 0.1757 - val_accuracy: 0.9489 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1399 - accuracy: 0.9556 - val_loss: 0.1752 - val_accuracy: 0.9487 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 282/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1392 - accuracy: 0.9556 - val_loss: 0.1747 - val_accuracy: 0.9487 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1385 - accuracy: 0.9559 - val_loss: 0.1742 - val_accuracy: 0.9487 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1378 - accuracy: 0.9561 - val_loss: 0.1737 - val_accuracy: 0.9486 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1371 - accuracy: 0.9564 - val_loss: 0.1733 - val_accuracy: 0.9484 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1365 - accuracy: 0.9566 - val_loss: 0.1729 - val_accuracy: 0.9483 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 287/500 235/235 [==============================] - 2s 10ms/step - loss: 0.1359 - accuracy: 0.9568 - val_loss: 0.1725 - val_accuracy: 0.9483 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1353 - accuracy: 0.9571 - val_loss: 0.1721 - val_accuracy: 0.9482 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1348 - accuracy: 0.9573 - val_loss: 0.1718 - val_accuracy: 0.9488 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 290/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1342 - accuracy: 0.9574 - val_loss: 0.1715 - val_accuracy: 0.9489 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1337 - accuracy: 0.9577 - val_loss: 0.1711 - val_accuracy: 0.9488 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1332 - accuracy: 0.9579 - val_loss: 0.1708 - val_accuracy: 0.9488 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1327 - accuracy: 0.9580 - val_loss: 0.1706 - val_accuracy: 0.9492 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1322 - accuracy: 0.9581 - val_loss: 0.1703 - val_accuracy: 0.9492 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 295/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1318 - accuracy: 0.9583 - val_loss: 0.1701 - val_accuracy: 0.9492 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1313 - accuracy: 0.9584 - val_loss: 0.1698 - val_accuracy: 0.9491 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1309 - accuracy: 0.9584 - val_loss: 0.1696 - val_accuracy: 0.9494 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1305 - accuracy: 0.9584 - val_loss: 0.1694 - val_accuracy: 0.9494 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1301 - accuracy: 0.9585 - val_loss: 0.1692 - val_accuracy: 0.9500 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1297 - accuracy: 0.9585 - val_loss: 0.1691 - val_accuracy: 0.9502 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 301/500 235/235 [==============================] - 2s 9ms/step - loss: 0.7589 - accuracy: 0.7692 - val_loss: 0.5609 - val_accuracy: 0.8279 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 302/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5612 - accuracy: 0.8243 - val_loss: 0.5208 - val_accuracy: 0.8420 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5315 - accuracy: 0.8343 - val_loss: 0.5017 - val_accuracy: 0.8481 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5139 - accuracy: 0.8406 - val_loss: 0.4888 - val_accuracy: 0.8536 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 305/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5014 - accuracy: 0.8444 - val_loss: 0.4793 - val_accuracy: 0.8570 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4924 - accuracy: 0.8477 - val_loss: 0.4726 - val_accuracy: 0.8586 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 307/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4858 - accuracy: 0.8496 - val_loss: 0.4675 - val_accuracy: 0.8606 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4807 - accuracy: 0.8510 - val_loss: 0.4635 - val_accuracy: 0.8619 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4763 - accuracy: 0.8522 - val_loss: 0.4600 - val_accuracy: 0.8636 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4726 - accuracy: 0.8534 - val_loss: 0.4569 - val_accuracy: 0.8650 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 311/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4692 - accuracy: 0.8546 - val_loss: 0.4542 - val_accuracy: 0.8655 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 312/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4660 - accuracy: 0.8553 - val_loss: 0.4516 - val_accuracy: 0.8660 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 313/500 235/235 [==============================] - 3s 11ms/step - loss: 0.4630 - accuracy: 0.8560 - val_loss: 0.4491 - val_accuracy: 0.8671 [-0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 314/500 235/235 [==============================] - 2s 10ms/step - loss: 0.4599 - accuracy: 0.8574 - val_loss: 0.4467 - val_accuracy: 0.8676 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 315/500 235/235 [==============================] - 2s 10ms/step - loss: 0.4570 - accuracy: 0.8583 - val_loss: 0.4444 - val_accuracy: 0.8671 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4542 - accuracy: 0.8592 - val_loss: 0.4423 - val_accuracy: 0.8683 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 317/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4515 - accuracy: 0.8602 - val_loss: 0.4403 - val_accuracy: 0.8690 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 318/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4490 - accuracy: 0.8609 - val_loss: 0.4384 - val_accuracy: 0.8698 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4465 - accuracy: 0.8619 - val_loss: 0.4366 - val_accuracy: 0.8703 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 320/500 235/235 [==============================] - 2s 10ms/step - loss: 0.4442 - accuracy: 0.8628 - val_loss: 0.4350 - val_accuracy: 0.8704 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 321/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4418 - accuracy: 0.8637 - val_loss: 0.4335 - val_accuracy: 0.8709 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 322/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4396 - accuracy: 0.8648 - val_loss: 0.4322 - val_accuracy: 0.8713 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 323/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4377 - accuracy: 0.8652 - val_loss: 0.4311 - val_accuracy: 0.8713 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4361 - accuracy: 0.8656 - val_loss: 0.4302 - val_accuracy: 0.8719 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 325/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4346 - accuracy: 0.8660 - val_loss: 0.4294 - val_accuracy: 0.8719 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 326/500 235/235 [==============================] - 2s 10ms/step - loss: 0.4333 - accuracy: 0.8665 - val_loss: 0.4286 - val_accuracy: 0.8720 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4321 - accuracy: 0.8668 - val_loss: 0.4278 - val_accuracy: 0.8723 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4308 - accuracy: 0.8673 - val_loss: 0.4271 - val_accuracy: 0.8727 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4296 - accuracy: 0.8677 - val_loss: 0.4264 - val_accuracy: 0.8734 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 330/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4285 - accuracy: 0.8684 - val_loss: 0.4257 - val_accuracy: 0.8739 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4274 - accuracy: 0.8688 - val_loss: 0.4250 - val_accuracy: 0.8743 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4263 - accuracy: 0.8690 - val_loss: 0.4243 - val_accuracy: 0.8742 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4253 - accuracy: 0.8691 - val_loss: 0.4236 - val_accuracy: 0.8748 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4242 - accuracy: 0.8694 - val_loss: 0.4230 - val_accuracy: 0.8753 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 335/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4232 - accuracy: 0.8698 - val_loss: 0.4224 - val_accuracy: 0.8759 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 336/500 235/235 [==============================] - 2s 10ms/step - loss: 0.4223 - accuracy: 0.8703 - val_loss: 0.4218 - val_accuracy: 0.8759 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4214 - accuracy: 0.8706 - val_loss: 0.4213 - val_accuracy: 0.8755 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4206 - accuracy: 0.8707 - val_loss: 0.4208 - val_accuracy: 0.8758 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4199 - accuracy: 0.8711 - val_loss: 0.4204 - val_accuracy: 0.8766 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4191 - accuracy: 0.8715 - val_loss: 0.4199 - val_accuracy: 0.8769 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 341/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4185 - accuracy: 0.8715 - val_loss: 0.4196 - val_accuracy: 0.8769 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4178 - accuracy: 0.8717 - val_loss: 0.4191 - val_accuracy: 0.8767 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4172 - accuracy: 0.8720 - val_loss: 0.4188 - val_accuracy: 0.8768 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4166 - accuracy: 0.8721 - val_loss: 0.4184 - val_accuracy: 0.8767 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4160 - accuracy: 0.8721 - val_loss: 0.4181 - val_accuracy: 0.8771 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4155 - accuracy: 0.8724 - val_loss: 0.4178 - val_accuracy: 0.8773 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4149 - accuracy: 0.8725 - val_loss: 0.4174 - val_accuracy: 0.8776 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4144 - accuracy: 0.8726 - val_loss: 0.4171 - val_accuracy: 0.8778 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4139 - accuracy: 0.8729 - val_loss: 0.4169 - val_accuracy: 0.8778 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4135 - accuracy: 0.8731 - val_loss: 0.4166 - val_accuracy: 0.8777 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 351/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4278 - accuracy: 0.5314 - val_loss: 1.2303 - val_accuracy: 0.5995 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2782 - accuracy: 0.5742 - val_loss: 1.2069 - val_accuracy: 0.5901 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 353/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2582 - accuracy: 0.5782 - val_loss: 1.1920 - val_accuracy: 0.5952 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2414 - accuracy: 0.5774 - val_loss: 1.1757 - val_accuracy: 0.6140 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2246 - accuracy: 0.5794 - val_loss: 1.1603 - val_accuracy: 0.6154 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 356/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2125 - accuracy: 0.5820 - val_loss: 1.1502 - val_accuracy: 0.6159 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2053 - accuracy: 0.5844 - val_loss: 1.1443 - val_accuracy: 0.6160 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 358/500 235/235 [==============================] - 2s 10ms/step - loss: 1.2009 - accuracy: 0.5855 - val_loss: 1.1407 - val_accuracy: 0.6175 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1980 - accuracy: 0.5858 - val_loss: 1.1383 - val_accuracy: 0.6175 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1958 - accuracy: 0.5857 - val_loss: 1.1365 - val_accuracy: 0.6176 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1941 - accuracy: 0.5864 - val_loss: 1.1351 - val_accuracy: 0.6179 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 362/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1926 - accuracy: 0.5867 - val_loss: 1.1338 - val_accuracy: 0.6185 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1914 - accuracy: 0.5868 - val_loss: 1.1327 - val_accuracy: 0.6188 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1901 - accuracy: 0.5870 - val_loss: 1.1317 - val_accuracy: 0.6189 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1890 - accuracy: 0.5878 - val_loss: 1.1308 - val_accuracy: 0.6192 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1880 - accuracy: 0.5886 - val_loss: 1.1299 - val_accuracy: 0.6197 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1870 - accuracy: 0.5892 - val_loss: 1.1290 - val_accuracy: 0.6200 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1860 - accuracy: 0.5901 - val_loss: 1.1282 - val_accuracy: 0.6206 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1851 - accuracy: 0.5906 - val_loss: 1.1274 - val_accuracy: 0.6211 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1841 - accuracy: 0.5912 - val_loss: 1.1265 - val_accuracy: 0.6213 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1830 - accuracy: 0.5907 - val_loss: 1.1254 - val_accuracy: 0.6215 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1813 - accuracy: 0.5906 - val_loss: 1.1236 - val_accuracy: 0.6222 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1779 - accuracy: 0.5918 - val_loss: 1.1194 - val_accuracy: 0.6231 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1720 - accuracy: 0.5929 - val_loss: 1.1128 - val_accuracy: 0.6250 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1666 - accuracy: 0.5934 - val_loss: 1.1087 - val_accuracy: 0.6267 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1637 - accuracy: 0.5940 - val_loss: 1.1058 - val_accuracy: 0.6288 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1610 - accuracy: 0.5943 - val_loss: 1.1032 - val_accuracy: 0.6300 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 378/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1588 - accuracy: 0.5950 - val_loss: 1.1013 - val_accuracy: 0.6305 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1573 - accuracy: 0.5950 - val_loss: 1.0999 - val_accuracy: 0.6315 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1560 - accuracy: 0.5958 - val_loss: 1.0989 - val_accuracy: 0.6313 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1550 - accuracy: 0.5963 - val_loss: 1.0981 - val_accuracy: 0.6322 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1540 - accuracy: 0.5969 - val_loss: 1.0973 - val_accuracy: 0.6333 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1531 - accuracy: 0.5969 - val_loss: 1.0967 - val_accuracy: 0.6331 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1523 - accuracy: 0.5970 - val_loss: 1.0960 - val_accuracy: 0.6341 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 385/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1516 - accuracy: 0.5974 - val_loss: 1.0955 - val_accuracy: 0.6342 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1509 - accuracy: 0.5977 - val_loss: 1.0948 - val_accuracy: 0.6344 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1502 - accuracy: 0.5977 - val_loss: 1.0944 - val_accuracy: 0.6345 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1496 - accuracy: 0.5980 - val_loss: 1.0939 - val_accuracy: 0.6347 [-0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1490 - accuracy: 0.5984 - val_loss: 1.0934 - val_accuracy: 0.6351 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1484 - accuracy: 0.5984 - val_loss: 1.0931 - val_accuracy: 0.6356 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1480 - accuracy: 0.5989 - val_loss: 1.0927 - val_accuracy: 0.6361 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1475 - accuracy: 0.5990 - val_loss: 1.0923 - val_accuracy: 0.6363 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1471 - accuracy: 0.5985 - val_loss: 1.0920 - val_accuracy: 0.6357 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1467 - accuracy: 0.5987 - val_loss: 1.0917 - val_accuracy: 0.6362 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1463 - accuracy: 0.5989 - val_loss: 1.0914 - val_accuracy: 0.6362 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 396/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1459 - accuracy: 0.5994 - val_loss: 1.0912 - val_accuracy: 0.6359 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1456 - accuracy: 0.5995 - val_loss: 1.0909 - val_accuracy: 0.6361 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1453 - accuracy: 0.5997 - val_loss: 1.0906 - val_accuracy: 0.6363 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1450 - accuracy: 0.5998 - val_loss: 1.0904 - val_accuracy: 0.6370 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1447 - accuracy: 0.5995 - val_loss: 1.0901 - val_accuracy: 0.6372 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 401/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7532 - accuracy: 0.3609 - val_loss: 1.7031 - val_accuracy: 0.3777 [-0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7150 - accuracy: 0.3575 - val_loss: 1.6967 - val_accuracy: 0.3784 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7108 - accuracy: 0.3538 - val_loss: 1.6943 - val_accuracy: 0.3788 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7089 - accuracy: 0.3538 - val_loss: 1.6929 - val_accuracy: 0.3789 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7075 - accuracy: 0.3546 - val_loss: 1.6916 - val_accuracy: 0.3791 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7065 - accuracy: 0.3551 - val_loss: 1.6907 - val_accuracy: 0.3793 [-0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7056 - accuracy: 0.3557 - val_loss: 1.6899 - val_accuracy: 0.3797 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7048 - accuracy: 0.3563 - val_loss: 1.6892 - val_accuracy: 0.3797 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7041 - accuracy: 0.3574 - val_loss: 1.6886 - val_accuracy: 0.3798 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7035 - accuracy: 0.3589 - val_loss: 1.6881 - val_accuracy: 0.3798 [-0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7030 - accuracy: 0.3596 - val_loss: 1.6876 - val_accuracy: 0.3801 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7025 - accuracy: 0.3618 - val_loss: 1.6872 - val_accuracy: 0.3801 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7021 - accuracy: 0.3636 - val_loss: 1.6868 - val_accuracy: 0.3801 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7017 - accuracy: 0.3659 - val_loss: 1.6865 - val_accuracy: 0.3805 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7014 - accuracy: 0.3664 - val_loss: 1.6862 - val_accuracy: 0.3808 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7011 - accuracy: 0.3679 - val_loss: 1.6860 - val_accuracy: 0.3808 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7008 - accuracy: 0.3681 - val_loss: 1.6857 - val_accuracy: 0.3808 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7006 - accuracy: 0.3692 - val_loss: 1.6855 - val_accuracy: 0.3809 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7003 - accuracy: 0.3697 - val_loss: 1.6853 - val_accuracy: 0.3810 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7001 - accuracy: 0.3700 - val_loss: 1.6852 - val_accuracy: 0.3814 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6999 - accuracy: 0.3711 - val_loss: 1.6850 - val_accuracy: 0.3813 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6997 - accuracy: 0.3717 - val_loss: 1.6848 - val_accuracy: 0.3814 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6995 - accuracy: 0.3709 - val_loss: 1.6847 - val_accuracy: 0.3817 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6993 - accuracy: 0.3719 - val_loss: 1.6845 - val_accuracy: 0.3818 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6991 - accuracy: 0.3723 - val_loss: 1.6844 - val_accuracy: 0.3818 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6989 - accuracy: 0.3721 - val_loss: 1.6842 - val_accuracy: 0.3818 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6988 - accuracy: 0.3726 - val_loss: 1.6842 - val_accuracy: 0.3818 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6986 - accuracy: 0.3731 - val_loss: 1.6840 - val_accuracy: 0.3817 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6985 - accuracy: 0.3731 - val_loss: 1.6838 - val_accuracy: 0.3816 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6983 - accuracy: 0.3731 - val_loss: 1.6838 - val_accuracy: 0.3816 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6982 - accuracy: 0.3732 - val_loss: 1.6835 - val_accuracy: 0.3817 [-0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6981 - accuracy: 0.3729 - val_loss: 1.6835 - val_accuracy: 0.3817 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6979 - accuracy: 0.3735 - val_loss: 1.6835 - val_accuracy: 0.3817 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6978 - accuracy: 0.3742 - val_loss: 1.6833 - val_accuracy: 0.3816 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6977 - accuracy: 0.3746 - val_loss: 1.6832 - val_accuracy: 0.3818 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6976 - accuracy: 0.3752 - val_loss: 1.6831 - val_accuracy: 0.3818 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6975 - accuracy: 0.3752 - val_loss: 1.6831 - val_accuracy: 0.3819 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6974 - accuracy: 0.3758 - val_loss: 1.6830 - val_accuracy: 0.3819 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6973 - accuracy: 0.3761 - val_loss: 1.6828 - val_accuracy: 0.3820 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6972 - accuracy: 0.3764 - val_loss: 1.6827 - val_accuracy: 0.3819 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6971 - accuracy: 0.3771 - val_loss: 1.6827 - val_accuracy: 0.3820 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6970 - accuracy: 0.3764 - val_loss: 1.6826 - val_accuracy: 0.3820 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6969 - accuracy: 0.3760 - val_loss: 1.6825 - val_accuracy: 0.3820 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6969 - accuracy: 0.3760 - val_loss: 1.6825 - val_accuracy: 0.3819 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6968 - accuracy: 0.3764 - val_loss: 1.6824 - val_accuracy: 0.3819 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6967 - accuracy: 0.3772 - val_loss: 1.6824 - val_accuracy: 0.3820 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6966 - accuracy: 0.3764 - val_loss: 1.6822 - val_accuracy: 0.3821 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6965 - accuracy: 0.3776 - val_loss: 1.6822 - val_accuracy: 0.3822 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6965 - accuracy: 0.3771 - val_loss: 1.6822 - val_accuracy: 0.3822 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6964 - accuracy: 0.3769 - val_loss: 1.6821 - val_accuracy: 0.3823 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6963 - accuracy: 0.3770 - val_loss: 1.6819 - val_accuracy: 0.3823 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6963 - accuracy: 0.3772 - val_loss: 1.6819 - val_accuracy: 0.3823 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6962 - accuracy: 0.3774 - val_loss: 1.6818 - val_accuracy: 0.3825 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6961 - accuracy: 0.3778 - val_loss: 1.6819 - val_accuracy: 0.3825 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6961 - accuracy: 0.3779 - val_loss: 1.6818 - val_accuracy: 0.3825 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6960 - accuracy: 0.3775 - val_loss: 1.6817 - val_accuracy: 0.3825 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6960 - accuracy: 0.3774 - val_loss: 1.6817 - val_accuracy: 0.3827 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6959 - accuracy: 0.3783 - val_loss: 1.6817 - val_accuracy: 0.3827 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6959 - accuracy: 0.3776 - val_loss: 1.6815 - val_accuracy: 0.3827 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6958 - accuracy: 0.3776 - val_loss: 1.6815 - val_accuracy: 0.3827 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6957 - accuracy: 0.3779 - val_loss: 1.6814 - val_accuracy: 0.3826 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6957 - accuracy: 0.3783 - val_loss: 1.6814 - val_accuracy: 0.3825 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6956 - accuracy: 0.3774 - val_loss: 1.6813 - val_accuracy: 0.3828 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6956 - accuracy: 0.3779 - val_loss: 1.6813 - val_accuracy: 0.3827 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6955 - accuracy: 0.3781 - val_loss: 1.6813 - val_accuracy: 0.3829 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6955 - accuracy: 0.3777 - val_loss: 1.6812 - val_accuracy: 0.3830 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6954 - accuracy: 0.3783 - val_loss: 1.6812 - val_accuracy: 0.3832 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6954 - accuracy: 0.3780 - val_loss: 1.6810 - val_accuracy: 0.3833 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6954 - accuracy: 0.3783 - val_loss: 1.6811 - val_accuracy: 0.3832 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6953 - accuracy: 0.3781 - val_loss: 1.6810 - val_accuracy: 0.3832 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6953 - accuracy: 0.3785 - val_loss: 1.6810 - val_accuracy: 0.3832 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6953 - accuracy: 0.3783 - val_loss: 1.6811 - val_accuracy: 0.3833 [-0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6952 - accuracy: 0.3783 - val_loss: 1.6810 - val_accuracy: 0.3834 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6952 - accuracy: 0.3789 - val_loss: 1.6809 - val_accuracy: 0.3833 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6951 - accuracy: 0.3795 - val_loss: 1.6809 - val_accuracy: 0.3833 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6951 - accuracy: 0.3786 - val_loss: 1.6809 - val_accuracy: 0.3835 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6950 - accuracy: 0.3786 - val_loss: 1.6809 - val_accuracy: 0.3833 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6950 - accuracy: 0.3790 - val_loss: 1.6809 - val_accuracy: 0.3834 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6950 - accuracy: 0.3785 - val_loss: 1.6808 - val_accuracy: 0.3835 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6949 - accuracy: 0.3793 - val_loss: 1.6807 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6949 - accuracy: 0.3788 - val_loss: 1.6806 - val_accuracy: 0.3837 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6949 - accuracy: 0.3787 - val_loss: 1.6806 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6948 - accuracy: 0.3786 - val_loss: 1.6806 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6948 - accuracy: 0.3790 - val_loss: 1.6807 - val_accuracy: 0.3837 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6948 - accuracy: 0.3791 - val_loss: 1.6806 - val_accuracy: 0.3837 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3790 - val_loss: 1.6805 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3785 - val_loss: 1.6805 - val_accuracy: 0.3837 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3800 - val_loss: 1.6805 - val_accuracy: 0.3837 [-0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6947 - accuracy: 0.3800 - val_loss: 1.6804 - val_accuracy: 0.3835 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3795 - val_loss: 1.6804 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3794 - val_loss: 1.6804 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3786 - val_loss: 1.6803 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6946 - accuracy: 0.3793 - val_loss: 1.6803 - val_accuracy: 0.3839 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6945 - accuracy: 0.3787 - val_loss: 1.6803 - val_accuracy: 0.3839 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6945 - accuracy: 0.3788 - val_loss: 1.6803 - val_accuracy: 0.3839 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3790 - val_loss: 1.6802 - val_accuracy: 0.3839 [-0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3787 - val_loss: 1.6803 - val_accuracy: 0.3839 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3788 - val_loss: 1.6802 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3794 - val_loss: 1.6802 - val_accuracy: 0.3839 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6944 - accuracy: 0.3794 - val_loss: 1.6800 - val_accuracy: 0.3838 [-0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 1/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1454 - accuracy: 0.9292 - val_loss: 1.5733 - val_accuracy: 0.8402 Epoch 2/200 235/235 [==============================] - 3s 14ms/step - loss: 0.4344 - accuracy: 0.9587 - val_loss: 0.4834 - val_accuracy: 0.9416 Epoch 3/200 235/235 [==============================] - 3s 14ms/step - loss: 0.3107 - accuracy: 0.9630 - val_loss: 0.3306 - val_accuracy: 0.9510 Epoch 4/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2776 - accuracy: 0.9658 - val_loss: 0.2861 - val_accuracy: 0.9585 Epoch 5/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2595 - accuracy: 0.9677 - val_loss: 0.3229 - val_accuracy: 0.9442 Epoch 6/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2490 - accuracy: 0.9685 - val_loss: 0.3016 - val_accuracy: 0.9507 Epoch 7/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2356 - accuracy: 0.9698 - val_loss: 0.2860 - val_accuracy: 0.9521 Epoch 8/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2342 - accuracy: 0.9692 - val_loss: 0.2749 - val_accuracy: 0.9546 Epoch 9/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2272 - accuracy: 0.9701 - val_loss: 0.3155 - val_accuracy: 0.9394 Epoch 10/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2191 - accuracy: 0.9715 - val_loss: 0.2557 - val_accuracy: 0.9580 Epoch 11/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2132 - accuracy: 0.9708 - val_loss: 0.2929 - val_accuracy: 0.9461 Epoch 12/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2107 - accuracy: 0.9707 - val_loss: 0.2492 - val_accuracy: 0.9615 Epoch 13/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2056 - accuracy: 0.9714 - val_loss: 0.2650 - val_accuracy: 0.9516 Epoch 14/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2018 - accuracy: 0.9722 - val_loss: 0.2707 - val_accuracy: 0.9499 Epoch 15/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1976 - accuracy: 0.9724 - val_loss: 0.2322 - val_accuracy: 0.9613 Epoch 16/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1952 - accuracy: 0.9740 - val_loss: 0.2493 - val_accuracy: 0.9553 Epoch 17/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1937 - accuracy: 0.9730 - val_loss: 0.2286 - val_accuracy: 0.9620 Epoch 18/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1929 - accuracy: 0.9732 - val_loss: 0.2577 - val_accuracy: 0.9524 Epoch 19/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1893 - accuracy: 0.9729 - val_loss: 0.2270 - val_accuracy: 0.9597 Epoch 20/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1850 - accuracy: 0.9732 - val_loss: 0.2624 - val_accuracy: 0.9532 Epoch 21/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1820 - accuracy: 0.9741 - val_loss: 0.2538 - val_accuracy: 0.9544 Epoch 22/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1824 - accuracy: 0.9741 - val_loss: 0.2694 - val_accuracy: 0.9493 Epoch 23/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1848 - accuracy: 0.9738 - val_loss: 0.2395 - val_accuracy: 0.9566 Epoch 24/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1793 - accuracy: 0.9747 - val_loss: 0.2493 - val_accuracy: 0.9511 Epoch 25/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1785 - accuracy: 0.9743 - val_loss: 0.2484 - val_accuracy: 0.9512 Epoch 26/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1769 - accuracy: 0.9737 - val_loss: 0.3107 - val_accuracy: 0.9352 Epoch 27/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1776 - accuracy: 0.9739 - val_loss: 0.2413 - val_accuracy: 0.9544 Epoch 28/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1743 - accuracy: 0.9739 - val_loss: 0.3283 - val_accuracy: 0.9286 Epoch 29/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1760 - accuracy: 0.9736 - val_loss: 0.2230 - val_accuracy: 0.9601 Epoch 30/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1758 - accuracy: 0.9734 - val_loss: 0.2404 - val_accuracy: 0.9568 Epoch 31/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1710 - accuracy: 0.9750 - val_loss: 0.2401 - val_accuracy: 0.9529 Epoch 32/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1698 - accuracy: 0.9749 - val_loss: 0.2294 - val_accuracy: 0.9559 Epoch 33/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1674 - accuracy: 0.9751 - val_loss: 0.2717 - val_accuracy: 0.9467 Epoch 34/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1661 - accuracy: 0.9754 - val_loss: 0.2432 - val_accuracy: 0.9539 Epoch 35/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1704 - accuracy: 0.9742 - val_loss: 0.2476 - val_accuracy: 0.9520 Epoch 36/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1688 - accuracy: 0.9742 - val_loss: 0.2607 - val_accuracy: 0.9479 Epoch 37/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1701 - accuracy: 0.9742 - val_loss: 0.2286 - val_accuracy: 0.9570 Epoch 38/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1649 - accuracy: 0.9759 - val_loss: 0.2328 - val_accuracy: 0.9570 Epoch 39/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1640 - accuracy: 0.9758 - val_loss: 0.2212 - val_accuracy: 0.9599 Epoch 40/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1650 - accuracy: 0.9755 - val_loss: 0.2457 - val_accuracy: 0.9516 Epoch 41/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1644 - accuracy: 0.9754 - val_loss: 0.2491 - val_accuracy: 0.9514 Epoch 42/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1654 - accuracy: 0.9756 - val_loss: 0.2391 - val_accuracy: 0.9514 Epoch 43/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1626 - accuracy: 0.9764 - val_loss: 0.2387 - val_accuracy: 0.9552 Epoch 44/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1641 - accuracy: 0.9755 - val_loss: 0.2481 - val_accuracy: 0.9510 Epoch 45/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1594 - accuracy: 0.9768 - val_loss: 0.2200 - val_accuracy: 0.9595 Epoch 46/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1634 - accuracy: 0.9754 - val_loss: 0.2393 - val_accuracy: 0.9543 Epoch 47/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1613 - accuracy: 0.9761 - val_loss: 0.2159 - val_accuracy: 0.9595 Epoch 48/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1647 - accuracy: 0.9747 - val_loss: 0.2254 - val_accuracy: 0.9568 Epoch 49/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1635 - accuracy: 0.9751 - val_loss: 0.2694 - val_accuracy: 0.9426 Epoch 50/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1612 - accuracy: 0.9759 - val_loss: 0.2214 - val_accuracy: 0.9583 Epoch 51/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1601 - accuracy: 0.9759 - val_loss: 0.2061 - val_accuracy: 0.9644 Epoch 52/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1610 - accuracy: 0.9752 - val_loss: 0.2485 - val_accuracy: 0.9499 Epoch 53/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1631 - accuracy: 0.9753 - val_loss: 0.2383 - val_accuracy: 0.9524 Epoch 54/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1612 - accuracy: 0.9754 - val_loss: 0.2578 - val_accuracy: 0.9454 Epoch 55/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1566 - accuracy: 0.9762 - val_loss: 0.2446 - val_accuracy: 0.9484 Epoch 56/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1603 - accuracy: 0.9762 - val_loss: 0.2704 - val_accuracy: 0.9429 Epoch 57/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1574 - accuracy: 0.9762 - val_loss: 0.2118 - val_accuracy: 0.9625 Epoch 58/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1547 - accuracy: 0.9775 - val_loss: 0.1978 - val_accuracy: 0.9657 Epoch 59/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1558 - accuracy: 0.9765 - val_loss: 0.2498 - val_accuracy: 0.9508 Epoch 60/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1561 - accuracy: 0.9757 - val_loss: 0.2385 - val_accuracy: 0.9529 Epoch 61/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1567 - accuracy: 0.9759 - val_loss: 0.2399 - val_accuracy: 0.9491 Epoch 62/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1576 - accuracy: 0.9756 - val_loss: 0.2146 - val_accuracy: 0.9606 Epoch 63/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1542 - accuracy: 0.9772 - val_loss: 0.2141 - val_accuracy: 0.9583 Epoch 64/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1541 - accuracy: 0.9766 - val_loss: 0.2473 - val_accuracy: 0.9496 Epoch 65/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9763 - val_loss: 0.2284 - val_accuracy: 0.9561 Epoch 66/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1557 - accuracy: 0.9765 - val_loss: 0.2047 - val_accuracy: 0.9614 Epoch 67/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1556 - accuracy: 0.9766 - val_loss: 0.2492 - val_accuracy: 0.9483 Epoch 68/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9772 - val_loss: 0.2081 - val_accuracy: 0.9612 Epoch 69/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1523 - accuracy: 0.9771 - val_loss: 0.2365 - val_accuracy: 0.9500 Epoch 70/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1542 - accuracy: 0.9767 - val_loss: 0.2797 - val_accuracy: 0.9389 Epoch 71/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1527 - accuracy: 0.9765 - val_loss: 0.2338 - val_accuracy: 0.9516 Epoch 72/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1556 - accuracy: 0.9764 - val_loss: 0.2479 - val_accuracy: 0.9493 Epoch 73/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1551 - accuracy: 0.9761 - val_loss: 0.2443 - val_accuracy: 0.9508 Epoch 74/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1552 - accuracy: 0.9768 - val_loss: 0.2480 - val_accuracy: 0.9498 Epoch 75/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1494 - accuracy: 0.9775 - val_loss: 0.2251 - val_accuracy: 0.9538 Epoch 76/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1486 - accuracy: 0.9777 - val_loss: 0.2226 - val_accuracy: 0.9582 Epoch 77/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1546 - accuracy: 0.9762 - val_loss: 0.2502 - val_accuracy: 0.9468 Epoch 78/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1532 - accuracy: 0.9773 - val_loss: 0.2542 - val_accuracy: 0.9496 Epoch 79/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1511 - accuracy: 0.9776 - val_loss: 0.2119 - val_accuracy: 0.9593 Epoch 80/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9788 - val_loss: 0.2458 - val_accuracy: 0.9505 Epoch 81/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1529 - accuracy: 0.9768 - val_loss: 0.2041 - val_accuracy: 0.9616 Epoch 82/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1510 - accuracy: 0.9771 - val_loss: 0.2390 - val_accuracy: 0.9509 Epoch 83/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1547 - accuracy: 0.9764 - val_loss: 0.2209 - val_accuracy: 0.9595 Epoch 84/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1482 - accuracy: 0.9776 - val_loss: 0.2052 - val_accuracy: 0.9619 Epoch 85/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1504 - accuracy: 0.9779 - val_loss: 0.2250 - val_accuracy: 0.9558 Epoch 86/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1514 - accuracy: 0.9770 - val_loss: 0.2591 - val_accuracy: 0.9439 Epoch 87/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1472 - accuracy: 0.9786 - val_loss: 0.2446 - val_accuracy: 0.9498 Epoch 88/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1489 - accuracy: 0.9776 - val_loss: 0.2769 - val_accuracy: 0.9383 Epoch 89/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1526 - accuracy: 0.9767 - val_loss: 0.2197 - val_accuracy: 0.9561 Epoch 90/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1492 - accuracy: 0.9780 - val_loss: 0.2546 - val_accuracy: 0.9445 Epoch 91/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1479 - accuracy: 0.9780 - val_loss: 0.2169 - val_accuracy: 0.9590 Epoch 92/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1492 - accuracy: 0.9779 - val_loss: 0.2458 - val_accuracy: 0.9514 Epoch 93/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9775 - val_loss: 0.2426 - val_accuracy: 0.9517 Epoch 94/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1532 - accuracy: 0.9767 - val_loss: 0.2327 - val_accuracy: 0.9566 Epoch 95/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9768 - val_loss: 0.2457 - val_accuracy: 0.9495 Epoch 96/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1509 - accuracy: 0.9768 - val_loss: 0.2344 - val_accuracy: 0.9545 Epoch 97/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1492 - accuracy: 0.9780 - val_loss: 0.2101 - val_accuracy: 0.9599 Epoch 98/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9775 - val_loss: 0.2216 - val_accuracy: 0.9580 Epoch 99/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1538 - accuracy: 0.9758 - val_loss: 0.2166 - val_accuracy: 0.9592 Epoch 100/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9762 - val_loss: 0.2394 - val_accuracy: 0.9541 Epoch 101/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1484 - accuracy: 0.9775 - val_loss: 0.2232 - val_accuracy: 0.9561 Epoch 102/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1500 - accuracy: 0.9770 - val_loss: 0.2295 - val_accuracy: 0.9521 Epoch 103/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9769 - val_loss: 0.2177 - val_accuracy: 0.9548 Epoch 104/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1463 - accuracy: 0.9775 - val_loss: 0.2079 - val_accuracy: 0.9599 Epoch 105/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1490 - accuracy: 0.9770 - val_loss: 0.2246 - val_accuracy: 0.9572 Epoch 106/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1463 - accuracy: 0.9780 - val_loss: 0.2344 - val_accuracy: 0.9522 Epoch 107/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1489 - accuracy: 0.9777 - val_loss: 0.2412 - val_accuracy: 0.9512 Epoch 108/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9770 - val_loss: 0.2176 - val_accuracy: 0.9569 Epoch 109/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9772 - val_loss: 0.2274 - val_accuracy: 0.9543 Epoch 110/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1505 - accuracy: 0.9762 - val_loss: 0.1942 - val_accuracy: 0.9644 Epoch 111/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1473 - accuracy: 0.9782 - val_loss: 0.2001 - val_accuracy: 0.9625 Epoch 112/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9790 - val_loss: 0.2319 - val_accuracy: 0.9524 Epoch 113/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1452 - accuracy: 0.9780 - val_loss: 0.2360 - val_accuracy: 0.9524 Epoch 114/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9779 - val_loss: 0.2237 - val_accuracy: 0.9567 Epoch 115/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9771 - val_loss: 0.1986 - val_accuracy: 0.9617 Epoch 116/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1438 - accuracy: 0.9783 - val_loss: 0.2101 - val_accuracy: 0.9596 Epoch 117/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9778 - val_loss: 0.2186 - val_accuracy: 0.9567 Epoch 118/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9781 - val_loss: 0.2434 - val_accuracy: 0.9476 Epoch 119/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9778 - val_loss: 0.2260 - val_accuracy: 0.9543 Epoch 120/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1473 - accuracy: 0.9773 - val_loss: 0.2305 - val_accuracy: 0.9521 Epoch 121/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9776 - val_loss: 0.2227 - val_accuracy: 0.9558 Epoch 122/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9776 - val_loss: 0.2129 - val_accuracy: 0.9596 Epoch 123/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9774 - val_loss: 0.2051 - val_accuracy: 0.9610 Epoch 124/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9780 - val_loss: 0.2657 - val_accuracy: 0.9438 Epoch 125/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1448 - accuracy: 0.9785 - val_loss: 0.2179 - val_accuracy: 0.9563 Epoch 126/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1447 - accuracy: 0.9782 - val_loss: 0.2275 - val_accuracy: 0.9540 Epoch 127/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9784 - val_loss: 0.2255 - val_accuracy: 0.9562 Epoch 128/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1442 - accuracy: 0.9782 - val_loss: 0.2351 - val_accuracy: 0.9556 Epoch 129/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1445 - accuracy: 0.9787 - val_loss: 0.1881 - val_accuracy: 0.9660 Epoch 130/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9775 - val_loss: 0.2121 - val_accuracy: 0.9586 Epoch 131/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1447 - accuracy: 0.9778 - val_loss: 0.2044 - val_accuracy: 0.9612 Epoch 132/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1449 - accuracy: 0.9783 - val_loss: 0.2028 - val_accuracy: 0.9613 Epoch 133/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1419 - accuracy: 0.9785 - val_loss: 0.2153 - val_accuracy: 0.9574 Epoch 134/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1471 - accuracy: 0.9774 - val_loss: 0.2053 - val_accuracy: 0.9593 Epoch 135/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1478 - accuracy: 0.9769 - val_loss: 0.2054 - val_accuracy: 0.9625 Epoch 136/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1392 - accuracy: 0.9793 - val_loss: 0.2158 - val_accuracy: 0.9561 Epoch 137/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1431 - accuracy: 0.9777 - val_loss: 0.2155 - val_accuracy: 0.9577 Epoch 138/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1444 - accuracy: 0.9783 - val_loss: 0.2105 - val_accuracy: 0.9586 Epoch 139/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1437 - accuracy: 0.9782 - val_loss: 0.2069 - val_accuracy: 0.9613 Epoch 140/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9781 - val_loss: 0.2048 - val_accuracy: 0.9597 Epoch 141/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1443 - accuracy: 0.9779 - val_loss: 0.2253 - val_accuracy: 0.9547 Epoch 142/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9793 - val_loss: 0.1924 - val_accuracy: 0.9650 Epoch 143/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1415 - accuracy: 0.9786 - val_loss: 0.1856 - val_accuracy: 0.9668 Epoch 144/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1454 - accuracy: 0.9780 - val_loss: 0.2002 - val_accuracy: 0.9626 Epoch 145/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1435 - accuracy: 0.9778 - val_loss: 0.2282 - val_accuracy: 0.9558 Epoch 146/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1420 - accuracy: 0.9789 - val_loss: 0.2057 - val_accuracy: 0.9629 Epoch 147/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1403 - accuracy: 0.9786 - val_loss: 0.2261 - val_accuracy: 0.9534 Epoch 148/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9792 - val_loss: 0.2279 - val_accuracy: 0.9549 Epoch 149/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9782 - val_loss: 0.1964 - val_accuracy: 0.9647 Epoch 150/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1449 - accuracy: 0.9779 - val_loss: 0.2233 - val_accuracy: 0.9583 Epoch 151/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9792 - val_loss: 0.1941 - val_accuracy: 0.9630 Epoch 152/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1427 - accuracy: 0.9781 - val_loss: 0.2011 - val_accuracy: 0.9619 Epoch 153/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9786 - val_loss: 0.2251 - val_accuracy: 0.9518 Epoch 154/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9784 - val_loss: 0.2162 - val_accuracy: 0.9594 Epoch 155/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9790 - val_loss: 0.1850 - val_accuracy: 0.9654 Epoch 156/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9788 - val_loss: 0.1949 - val_accuracy: 0.9633 Epoch 157/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9771 - val_loss: 0.1856 - val_accuracy: 0.9668 Epoch 158/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9789 - val_loss: 0.1855 - val_accuracy: 0.9664 Epoch 159/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2397 - val_accuracy: 0.9488 Epoch 160/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9785 - val_loss: 0.1933 - val_accuracy: 0.9657 Epoch 161/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2132 - val_accuracy: 0.9591 Epoch 162/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9790 - val_loss: 0.2009 - val_accuracy: 0.9607 Epoch 163/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9784 - val_loss: 0.2454 - val_accuracy: 0.9478 Epoch 164/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9786 - val_loss: 0.1971 - val_accuracy: 0.9617 Epoch 165/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2046 - val_accuracy: 0.9618 Epoch 166/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9788 - val_loss: 0.1899 - val_accuracy: 0.9648 Epoch 167/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9790 - val_loss: 0.1959 - val_accuracy: 0.9634 Epoch 168/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9781 - val_loss: 0.2380 - val_accuracy: 0.9520 Epoch 169/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9791 - val_loss: 0.2065 - val_accuracy: 0.9599 Epoch 170/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1390 - accuracy: 0.9794 - val_loss: 0.2059 - val_accuracy: 0.9628 Epoch 171/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9788 - val_loss: 0.2194 - val_accuracy: 0.9562 Epoch 172/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9785 - val_loss: 0.2068 - val_accuracy: 0.9629 Epoch 173/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1400 - accuracy: 0.9787 - val_loss: 0.2196 - val_accuracy: 0.9562 Epoch 174/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1407 - accuracy: 0.9782 - val_loss: 0.2099 - val_accuracy: 0.9608 Epoch 175/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1439 - accuracy: 0.9776 - val_loss: 0.2251 - val_accuracy: 0.9536 Epoch 176/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9792 - val_loss: 0.2119 - val_accuracy: 0.9573 Epoch 177/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1434 - accuracy: 0.9780 - val_loss: 0.1996 - val_accuracy: 0.9627 Epoch 178/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1402 - accuracy: 0.9792 - val_loss: 0.1966 - val_accuracy: 0.9630 Epoch 179/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1451 - accuracy: 0.9774 - val_loss: 0.2435 - val_accuracy: 0.9473 Epoch 180/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1368 - accuracy: 0.9797 - val_loss: 0.2024 - val_accuracy: 0.9589 Epoch 181/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9784 - val_loss: 0.1977 - val_accuracy: 0.9635 Epoch 182/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1409 - accuracy: 0.9782 - val_loss: 0.2096 - val_accuracy: 0.9580 Epoch 183/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1418 - accuracy: 0.9786 - val_loss: 0.1945 - val_accuracy: 0.9645 Epoch 184/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1403 - accuracy: 0.9787 - val_loss: 0.1930 - val_accuracy: 0.9656 Epoch 185/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1409 - accuracy: 0.9786 - val_loss: 0.2056 - val_accuracy: 0.9606 Epoch 186/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9786 - val_loss: 0.2137 - val_accuracy: 0.9607 Epoch 187/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1407 - accuracy: 0.9782 - val_loss: 0.2217 - val_accuracy: 0.9553 Epoch 188/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1417 - accuracy: 0.9785 - val_loss: 0.1866 - val_accuracy: 0.9662 Epoch 189/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1376 - accuracy: 0.9794 - val_loss: 0.1943 - val_accuracy: 0.9643 Epoch 190/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1408 - accuracy: 0.9786 - val_loss: 0.2086 - val_accuracy: 0.9593 Epoch 191/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9795 - val_loss: 0.2375 - val_accuracy: 0.9539 Epoch 192/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9789 - val_loss: 0.2054 - val_accuracy: 0.9635 Epoch 193/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9775 - val_loss: 0.1843 - val_accuracy: 0.9652 Epoch 194/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9790 - val_loss: 0.1963 - val_accuracy: 0.9619 Epoch 195/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9797 - val_loss: 0.2048 - val_accuracy: 0.9639 Epoch 196/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1429 - accuracy: 0.9785 - val_loss: 0.1878 - val_accuracy: 0.9661 Epoch 197/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1387 - accuracy: 0.9794 - val_loss: 0.1953 - val_accuracy: 0.9618 Epoch 198/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9800 - val_loss: 0.2030 - val_accuracy: 0.9630 Epoch 199/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1399 - accuracy: 0.9787 - val_loss: 0.1882 - val_accuracy: 0.9658 Epoch 200/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1406 - accuracy: 0.9788 - val_loss: 0.2303 - val_accuracy: 0.9534 Epoch 1/200 235/235 [==============================] - 4s 14ms/step - loss: 0.2384 - accuracy: 0.9303 - val_loss: 0.2010 - val_accuracy: 0.9554 Epoch 2/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0871 - accuracy: 0.9750 - val_loss: 0.1023 - val_accuracy: 0.9665 Epoch 3/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0501 - accuracy: 0.9861 - val_loss: 0.0894 - val_accuracy: 0.9705 Epoch 4/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0298 - accuracy: 0.9924 - val_loss: 0.0839 - val_accuracy: 0.9724 Epoch 5/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9951 - val_loss: 0.0908 - val_accuracy: 0.9726 Epoch 6/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0130 - accuracy: 0.9971 - val_loss: 0.0931 - val_accuracy: 0.9723 Epoch 7/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.0942 - val_accuracy: 0.9725 Epoch 8/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0115 - accuracy: 0.9969 - val_loss: 0.0914 - val_accuracy: 0.9741 Epoch 9/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0103 - accuracy: 0.9972 - val_loss: 0.1034 - val_accuracy: 0.9734 Epoch 10/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0116 - accuracy: 0.9966 - val_loss: 0.1107 - val_accuracy: 0.9722 Epoch 11/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0103 - accuracy: 0.9968 - val_loss: 0.1069 - val_accuracy: 0.9740 Epoch 12/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 0.0904 - val_accuracy: 0.9765 Epoch 13/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.0980 - val_accuracy: 0.9767 Epoch 14/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.0875 - val_accuracy: 0.9795 Epoch 15/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.0960 - val_accuracy: 0.9771 Epoch 16/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.1077 - val_accuracy: 0.9734 Epoch 17/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9959 - val_loss: 0.1104 - val_accuracy: 0.9750 Epoch 18/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0943 - val_accuracy: 0.9773 Epoch 19/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0049 - accuracy: 0.9987 - val_loss: 0.0864 - val_accuracy: 0.9796 Epoch 20/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0743 - val_accuracy: 0.9816 Epoch 21/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0851 - val_accuracy: 0.9806 Epoch 22/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0884 - val_accuracy: 0.9794 Epoch 23/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0971 - val_accuracy: 0.9786 Epoch 24/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9965 - val_loss: 0.1217 - val_accuracy: 0.9747 Epoch 25/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 0.1144 - val_accuracy: 0.9746 Epoch 26/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0062 - accuracy: 0.9980 - val_loss: 0.0859 - val_accuracy: 0.9788 Epoch 27/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0804 - val_accuracy: 0.9826 Epoch 28/200 235/235 [==============================] - 3s 14ms/step - loss: 9.8967e-04 - accuracy: 0.9998 - val_loss: 0.0820 - val_accuracy: 0.9831 Epoch 29/200 235/235 [==============================] - 3s 14ms/step - loss: 6.4159e-04 - accuracy: 0.9999 - val_loss: 0.0772 - val_accuracy: 0.9846 Epoch 30/200 235/235 [==============================] - 3s 14ms/step - loss: 2.2164e-04 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9845 Epoch 31/200 235/235 [==============================] - 4s 16ms/step - loss: 3.0036e-04 - accuracy: 1.0000 - val_loss: 0.0815 - val_accuracy: 0.9835 Epoch 32/200 235/235 [==============================] - 4s 16ms/step - loss: 1.4568e-04 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9846 Epoch 33/200 235/235 [==============================] - 3s 14ms/step - loss: 8.9280e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9848 Epoch 34/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0255e-04 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9850 Epoch 35/200 235/235 [==============================] - 3s 14ms/step - loss: 9.4754e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9843 Epoch 36/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1177 - val_accuracy: 0.9768 Epoch 37/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0331 - accuracy: 0.9898 - val_loss: 0.1184 - val_accuracy: 0.9743 Epoch 38/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.0839 - val_accuracy: 0.9811 Epoch 39/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.0739 - val_accuracy: 0.9848 Epoch 40/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0768 - val_accuracy: 0.9846 Epoch 41/200 235/235 [==============================] - 3s 14ms/step - loss: 3.7544e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9844 Epoch 42/200 235/235 [==============================] - 3s 14ms/step - loss: 2.0662e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9849 Epoch 43/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5944e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9852 Epoch 44/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5341e-04 - accuracy: 1.0000 - val_loss: 0.0766 - val_accuracy: 0.9847 Epoch 45/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0698e-04 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9846 Epoch 46/200 235/235 [==============================] - 3s 14ms/step - loss: 9.2168e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9848 Epoch 47/200 235/235 [==============================] - 3s 14ms/step - loss: 9.0834e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9856 Epoch 48/200 235/235 [==============================] - 3s 14ms/step - loss: 6.9801e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9853 Epoch 49/200 235/235 [==============================] - 3s 14ms/step - loss: 6.3549e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9849 Epoch 50/200 235/235 [==============================] - 3s 14ms/step - loss: 5.3713e-05 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9851 Epoch 51/200 235/235 [==============================] - 3s 14ms/step - loss: 5.3301e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9850 Epoch 52/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0171 - accuracy: 0.9942 - val_loss: 0.2425 - val_accuracy: 0.9558 Epoch 53/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0211 - accuracy: 0.9932 - val_loss: 0.0956 - val_accuracy: 0.9784 Epoch 54/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0769 - val_accuracy: 0.9837 Epoch 55/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0721 - val_accuracy: 0.9840 Epoch 56/200 235/235 [==============================] - 3s 14ms/step - loss: 4.5208e-04 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 0.9845 Epoch 57/200 235/235 [==============================] - 3s 14ms/step - loss: 3.0806e-04 - accuracy: 1.0000 - val_loss: 0.0732 - val_accuracy: 0.9851 Epoch 58/200 235/235 [==============================] - 3s 14ms/step - loss: 1.9521e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9848 Epoch 59/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3770e-04 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9853 Epoch 60/200 235/235 [==============================] - 3s 14ms/step - loss: 1.1612e-04 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9851 Epoch 61/200 235/235 [==============================] - 3s 14ms/step - loss: 1.1397e-04 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9848 Epoch 62/200 235/235 [==============================] - 4s 15ms/step - loss: 9.9060e-05 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9852 Epoch 63/200 235/235 [==============================] - 3s 15ms/step - loss: 7.3421e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9851 Epoch 64/200 235/235 [==============================] - 3s 15ms/step - loss: 6.7283e-05 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9848 Epoch 65/200 235/235 [==============================] - 3s 14ms/step - loss: 6.2857e-05 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9853 Epoch 66/200 235/235 [==============================] - 3s 15ms/step - loss: 8.5222e-05 - accuracy: 1.0000 - val_loss: 0.0800 - val_accuracy: 0.9845 Epoch 67/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0126 - accuracy: 0.9958 - val_loss: 0.1294 - val_accuracy: 0.9720 Epoch 68/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0129 - accuracy: 0.9957 - val_loss: 0.1015 - val_accuracy: 0.9787 Epoch 69/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.0826 - val_accuracy: 0.9823 Epoch 70/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0810 - val_accuracy: 0.9840 Epoch 71/200 235/235 [==============================] - 3s 15ms/step - loss: 4.6130e-04 - accuracy: 0.9999 - val_loss: 0.0782 - val_accuracy: 0.9852 Epoch 72/200 235/235 [==============================] - 3s 15ms/step - loss: 3.7710e-04 - accuracy: 0.9999 - val_loss: 0.0778 - val_accuracy: 0.9852 Epoch 73/200 235/235 [==============================] - 3s 15ms/step - loss: 7.1448e-04 - accuracy: 0.9998 - val_loss: 0.0780 - val_accuracy: 0.9842 Epoch 74/200 235/235 [==============================] - 3s 15ms/step - loss: 3.8315e-04 - accuracy: 0.9999 - val_loss: 0.0807 - val_accuracy: 0.9844 Epoch 75/200 235/235 [==============================] - 3s 15ms/step - loss: 1.6320e-04 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9853 Epoch 76/200 235/235 [==============================] - 3s 15ms/step - loss: 9.9596e-05 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9854 Epoch 77/200 235/235 [==============================] - 3s 15ms/step - loss: 6.8125e-05 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9853 Epoch 78/200 235/235 [==============================] - 3s 15ms/step - loss: 6.1544e-05 - accuracy: 1.0000 - val_loss: 0.0768 - val_accuracy: 0.9856 Epoch 79/200 235/235 [==============================] - 3s 14ms/step - loss: 5.1015e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9859 Epoch 80/200 235/235 [==============================] - 3s 14ms/step - loss: 4.9467e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9853 Epoch 81/200 235/235 [==============================] - 3s 14ms/step - loss: 3.9085e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9854 Epoch 82/200 235/235 [==============================] - 3s 14ms/step - loss: 4.3078e-05 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 0.9863 Epoch 83/200 235/235 [==============================] - 3s 14ms/step - loss: 3.9389e-05 - accuracy: 1.0000 - val_loss: 0.0783 - val_accuracy: 0.9854 Epoch 84/200 235/235 [==============================] - 3s 14ms/step - loss: 4.2268e-05 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9854 Epoch 85/200 235/235 [==============================] - 3s 14ms/step - loss: 2.9745e-05 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9857 Epoch 86/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3704e-05 - accuracy: 1.0000 - val_loss: 0.0797 - val_accuracy: 0.9855 Epoch 87/200 235/235 [==============================] - 3s 14ms/step - loss: 2.4212e-05 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9855 Epoch 88/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3469e-05 - accuracy: 1.0000 - val_loss: 0.0813 - val_accuracy: 0.9857 Epoch 89/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0085 - accuracy: 0.9972 - val_loss: 0.2356 - val_accuracy: 0.9604 Epoch 90/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0206 - accuracy: 0.9933 - val_loss: 0.0964 - val_accuracy: 0.9806 Epoch 91/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.0872 - val_accuracy: 0.9829 Epoch 92/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0866 - val_accuracy: 0.9849 Epoch 93/200 235/235 [==============================] - 3s 14ms/step - loss: 4.5601e-04 - accuracy: 0.9999 - val_loss: 0.0859 - val_accuracy: 0.9854 Epoch 94/200 235/235 [==============================] - 4s 15ms/step - loss: 3.6784e-04 - accuracy: 0.9999 - val_loss: 0.0862 - val_accuracy: 0.9851 Epoch 95/200 235/235 [==============================] - 3s 14ms/step - loss: 5.1643e-04 - accuracy: 0.9999 - val_loss: 0.0896 - val_accuracy: 0.9841 Epoch 96/200 235/235 [==============================] - 3s 14ms/step - loss: 4.9737e-04 - accuracy: 0.9998 - val_loss: 0.0857 - val_accuracy: 0.9840 Epoch 97/200 235/235 [==============================] - 3s 14ms/step - loss: 2.2418e-04 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9845 Epoch 98/200 235/235 [==============================] - 3s 14ms/step - loss: 1.1549e-04 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9849 Epoch 99/200 235/235 [==============================] - 3s 14ms/step - loss: 1.8224e-04 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9840 Epoch 100/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0491e-04 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 0.9848 Epoch 101/200 235/235 [==============================] - 3s 14ms/step - loss: 7.2000e-05 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9847 Epoch 102/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3033e-04 - accuracy: 0.9999 - val_loss: 0.1052 - val_accuracy: 0.9824 Epoch 103/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9982 - val_loss: 0.1388 - val_accuracy: 0.9756 Epoch 104/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0081 - accuracy: 0.9971 - val_loss: 0.1142 - val_accuracy: 0.9796 Epoch 105/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 0.0971 - val_accuracy: 0.9821 Epoch 106/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0942 - val_accuracy: 0.9838 Epoch 107/200 235/235 [==============================] - 3s 14ms/step - loss: 3.4603e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9839 Epoch 108/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3253e-04 - accuracy: 1.0000 - val_loss: 0.0913 - val_accuracy: 0.9845 Epoch 109/200 235/235 [==============================] - 3s 14ms/step - loss: 8.0258e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9844 Epoch 110/200 235/235 [==============================] - 3s 14ms/step - loss: 6.0365e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9848 Epoch 111/200 235/235 [==============================] - 3s 14ms/step - loss: 5.5904e-05 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9847 Epoch 112/200 235/235 [==============================] - 3s 14ms/step - loss: 4.7209e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9846 Epoch 113/200 235/235 [==============================] - 3s 14ms/step - loss: 3.8688e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9846 Epoch 114/200 235/235 [==============================] - 3s 14ms/step - loss: 3.4847e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9847 Epoch 115/200 235/235 [==============================] - 3s 14ms/step - loss: 3.1833e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9842 Epoch 116/200 235/235 [==============================] - 3s 14ms/step - loss: 3.2284e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9847 Epoch 117/200 235/235 [==============================] - 3s 14ms/step - loss: 8.8095e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9849 Epoch 118/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.1481 - val_accuracy: 0.9771 Epoch 119/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9967 - val_loss: 0.1260 - val_accuracy: 0.9777 Epoch 120/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 0.0953 - val_accuracy: 0.9842 Epoch 121/200 235/235 [==============================] - 3s 14ms/step - loss: 7.4479e-04 - accuracy: 0.9999 - val_loss: 0.0909 - val_accuracy: 0.9843 Epoch 122/200 235/235 [==============================] - 3s 14ms/step - loss: 5.4389e-04 - accuracy: 0.9999 - val_loss: 0.0924 - val_accuracy: 0.9848 Epoch 123/200 235/235 [==============================] - 3s 14ms/step - loss: 1.9079e-04 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9840 Epoch 124/200 235/235 [==============================] - 3s 14ms/step - loss: 3.1363e-04 - accuracy: 0.9999 - val_loss: 0.0920 - val_accuracy: 0.9846 Epoch 125/200 235/235 [==============================] - 3s 14ms/step - loss: 9.8832e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9848 Epoch 126/200 235/235 [==============================] - 3s 14ms/step - loss: 7.4385e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9850 Epoch 127/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0119e-04 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9848 Epoch 128/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1018 - val_accuracy: 0.9830 Epoch 129/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1237 - val_accuracy: 0.9785 Epoch 130/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0969 - val_accuracy: 0.9831 Epoch 131/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0954 - val_accuracy: 0.9829 Epoch 132/200 235/235 [==============================] - 3s 14ms/step - loss: 6.9090e-04 - accuracy: 0.9998 - val_loss: 0.0944 - val_accuracy: 0.9841 Epoch 133/200 235/235 [==============================] - 3s 14ms/step - loss: 3.8857e-04 - accuracy: 0.9999 - val_loss: 0.0875 - val_accuracy: 0.9851 Epoch 134/200 235/235 [==============================] - 3s 14ms/step - loss: 1.9984e-04 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9857 Epoch 135/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3966e-04 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9849 Epoch 136/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5426e-04 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9852 Epoch 137/200 235/235 [==============================] - 3s 14ms/step - loss: 3.9932e-05 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9851 Epoch 138/200 235/235 [==============================] - 3s 14ms/step - loss: 7.6793e-05 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9848 Epoch 139/200 235/235 [==============================] - 3s 14ms/step - loss: 4.4028e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9852 Epoch 140/200 235/235 [==============================] - 3s 14ms/step - loss: 2.4411e-05 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9856 Epoch 141/200 235/235 [==============================] - 3s 14ms/step - loss: 2.0525e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9857 Epoch 142/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5722e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9857 Epoch 143/200 235/235 [==============================] - 3s 14ms/step - loss: 4.3466e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9858 Epoch 144/200 235/235 [==============================] - 3s 14ms/step - loss: 6.1547e-04 - accuracy: 0.9999 - val_loss: 0.0944 - val_accuracy: 0.9838 Epoch 145/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9965 - val_loss: 0.1296 - val_accuracy: 0.9772 Epoch 146/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9983 - val_loss: 0.0920 - val_accuracy: 0.9833 Epoch 147/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0891 - val_accuracy: 0.9847 Epoch 148/200 235/235 [==============================] - 3s 14ms/step - loss: 3.8984e-04 - accuracy: 0.9999 - val_loss: 0.0888 - val_accuracy: 0.9850 Epoch 149/200 235/235 [==============================] - 3s 14ms/step - loss: 3.1552e-04 - accuracy: 0.9999 - val_loss: 0.0900 - val_accuracy: 0.9838 Epoch 150/200 235/235 [==============================] - 3s 14ms/step - loss: 3.1804e-04 - accuracy: 0.9999 - val_loss: 0.0899 - val_accuracy: 0.9845 Epoch 151/200 235/235 [==============================] - 3s 14ms/step - loss: 8.4239e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9843 Epoch 152/200 235/235 [==============================] - 4s 15ms/step - loss: 5.2126e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9847 Epoch 153/200 235/235 [==============================] - 3s 15ms/step - loss: 3.9892e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9848 Epoch 154/200 235/235 [==============================] - 3s 15ms/step - loss: 4.0656e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9853 Epoch 155/200 235/235 [==============================] - 3s 15ms/step - loss: 2.8978e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9851 Epoch 156/200 235/235 [==============================] - 3s 15ms/step - loss: 6.8577e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9850 Epoch 157/200 235/235 [==============================] - 3s 15ms/step - loss: 4.0521e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9849 Epoch 158/200 235/235 [==============================] - 3s 15ms/step - loss: 5.2547e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9852 Epoch 159/200 235/235 [==============================] - 3s 15ms/step - loss: 3.3802e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9848 Epoch 160/200 235/235 [==============================] - 4s 15ms/step - loss: 2.2985e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9845 Epoch 161/200 235/235 [==============================] - 3s 15ms/step - loss: 1.6751e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9846 Epoch 162/200 235/235 [==============================] - 3s 15ms/step - loss: 1.3570e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9850 Epoch 163/200 235/235 [==============================] - 3s 15ms/step - loss: 1.2749e-05 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 0.9848 Epoch 164/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0201e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9850 Epoch 165/200 235/235 [==============================] - 3s 15ms/step - loss: 9.3159e-06 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9850 Epoch 166/200 235/235 [==============================] - 4s 15ms/step - loss: 1.1591e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9853 Epoch 167/200 235/235 [==============================] - 4s 15ms/step - loss: 9.3892e-06 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9854 Epoch 168/200 235/235 [==============================] - 3s 15ms/step - loss: 7.1178e-06 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9854 Epoch 169/200 235/235 [==============================] - 3s 14ms/step - loss: 5.9696e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9856 Epoch 170/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1682 - val_accuracy: 0.9740 Epoch 171/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0106 - accuracy: 0.9965 - val_loss: 0.1030 - val_accuracy: 0.9819 Epoch 172/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0997 - val_accuracy: 0.9827 Epoch 173/200 235/235 [==============================] - 3s 14ms/step - loss: 8.0874e-04 - accuracy: 0.9998 - val_loss: 0.0946 - val_accuracy: 0.9841 Epoch 174/200 235/235 [==============================] - 3s 14ms/step - loss: 1.9233e-04 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9844 Epoch 175/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3721e-04 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9852 Epoch 176/200 235/235 [==============================] - 3s 14ms/step - loss: 6.8103e-05 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9849 Epoch 177/200 235/235 [==============================] - 3s 14ms/step - loss: 6.0059e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9851 Epoch 178/200 235/235 [==============================] - 3s 14ms/step - loss: 7.3392e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9856 Epoch 179/200 235/235 [==============================] - 3s 14ms/step - loss: 2.5223e-04 - accuracy: 0.9999 - val_loss: 0.0937 - val_accuracy: 0.9851 Epoch 180/200 235/235 [==============================] - 3s 14ms/step - loss: 9.0932e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9825 Epoch 181/200 235/235 [==============================] - 3s 14ms/step - loss: 8.9146e-04 - accuracy: 0.9998 - val_loss: 0.1189 - val_accuracy: 0.9815 Epoch 182/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1149 - val_accuracy: 0.9814 Epoch 183/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1123 - val_accuracy: 0.9827 Epoch 184/200 235/235 [==============================] - 3s 14ms/step - loss: 8.5703e-04 - accuracy: 0.9997 - val_loss: 0.1159 - val_accuracy: 0.9817 Epoch 185/200 235/235 [==============================] - 3s 14ms/step - loss: 7.5192e-04 - accuracy: 0.9998 - val_loss: 0.1070 - val_accuracy: 0.9843 Epoch 186/200 235/235 [==============================] - 3s 14ms/step - loss: 6.4805e-04 - accuracy: 0.9998 - val_loss: 0.1091 - val_accuracy: 0.9829 Epoch 187/200 235/235 [==============================] - 3s 14ms/step - loss: 9.2249e-04 - accuracy: 0.9997 - val_loss: 0.1023 - val_accuracy: 0.9833 Epoch 188/200 235/235 [==============================] - 3s 14ms/step - loss: 9.0278e-04 - accuracy: 0.9997 - val_loss: 0.1110 - val_accuracy: 0.9810 Epoch 189/200 235/235 [==============================] - 3s 14ms/step - loss: 7.1587e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9827 Epoch 190/200 235/235 [==============================] - 3s 14ms/step - loss: 8.3858e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9840 Epoch 191/200 235/235 [==============================] - 3s 14ms/step - loss: 8.0972e-05 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9845 Epoch 192/200 235/235 [==============================] - 3s 14ms/step - loss: 1.4262e-04 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9848 Epoch 193/200 235/235 [==============================] - 3s 14ms/step - loss: 4.3506e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9849 Epoch 194/200 235/235 [==============================] - 3s 14ms/step - loss: 4.1487e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9844 Epoch 195/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3147e-05 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9848 Epoch 196/200 235/235 [==============================] - 3s 14ms/step - loss: 2.1125e-05 - accuracy: 1.0000 - val_loss: 0.0978 - val_accuracy: 0.9847 Epoch 197/200 235/235 [==============================] - 3s 14ms/step - loss: 1.2570e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9849 Epoch 198/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0638e-05 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9848 Epoch 199/200 235/235 [==============================] - 3s 14ms/step - loss: 9.1066e-06 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9851 Epoch 200/200 235/235 [==============================] - 3s 12ms/step - loss: 9.9772e-06 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9851 Epoch 1/200 235/235 [==============================] - 3s 9ms/step - loss: 1.5773 - accuracy: 0.8533 - val_loss: 0.9265 - val_accuracy: 0.9021 Epoch 2/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8749 - accuracy: 0.8969 - val_loss: 0.8292 - val_accuracy: 0.9008 Epoch 3/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8351 - accuracy: 0.8977 - val_loss: 0.8140 - val_accuracy: 0.9004 Epoch 4/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8243 - accuracy: 0.8978 - val_loss: 0.8081 - val_accuracy: 0.8990 Epoch 5/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8195 - accuracy: 0.8974 - val_loss: 0.8038 - val_accuracy: 0.8982 Epoch 6/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.8977 - val_loss: 0.8009 - val_accuracy: 0.8980 Epoch 7/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8979 - val_loss: 0.7990 - val_accuracy: 0.8985 Epoch 8/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8121 - accuracy: 0.8978 - val_loss: 0.7972 - val_accuracy: 0.8983 Epoch 9/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8110 - accuracy: 0.8977 - val_loss: 0.7958 - val_accuracy: 0.8984 Epoch 10/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8103 - accuracy: 0.8978 - val_loss: 0.7943 - val_accuracy: 0.8988 Epoch 11/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8094 - accuracy: 0.8976 - val_loss: 0.7942 - val_accuracy: 0.8993 Epoch 12/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8090 - accuracy: 0.8977 - val_loss: 0.7938 - val_accuracy: 0.8988 Epoch 13/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8082 - accuracy: 0.8977 - val_loss: 0.7922 - val_accuracy: 0.8999 Epoch 14/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8077 - accuracy: 0.8981 - val_loss: 0.7921 - val_accuracy: 0.8998 Epoch 15/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8075 - accuracy: 0.8981 - val_loss: 0.7922 - val_accuracy: 0.8991 Epoch 16/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8073 - accuracy: 0.8981 - val_loss: 0.7910 - val_accuracy: 0.8999 Epoch 17/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8070 - accuracy: 0.8977 - val_loss: 0.7903 - val_accuracy: 0.9003 Epoch 18/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8068 - accuracy: 0.8982 - val_loss: 0.7908 - val_accuracy: 0.9001 Epoch 19/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8064 - accuracy: 0.8982 - val_loss: 0.7896 - val_accuracy: 0.9008 Epoch 20/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8062 - accuracy: 0.8986 - val_loss: 0.7898 - val_accuracy: 0.9012 Epoch 21/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8061 - accuracy: 0.8985 - val_loss: 0.7890 - val_accuracy: 0.9018 Epoch 22/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.8985 - val_loss: 0.7898 - val_accuracy: 0.9011 Epoch 23/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8059 - accuracy: 0.8983 - val_loss: 0.7896 - val_accuracy: 0.9009 Epoch 24/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.8983 - val_loss: 0.7897 - val_accuracy: 0.9005 Epoch 25/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8986 - val_loss: 0.7895 - val_accuracy: 0.9013 Epoch 26/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8991 - val_loss: 0.7890 - val_accuracy: 0.9011 Epoch 27/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8983 - val_loss: 0.7897 - val_accuracy: 0.9007 Epoch 28/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8986 - val_loss: 0.7886 - val_accuracy: 0.9014 Epoch 29/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8990 - val_loss: 0.7895 - val_accuracy: 0.9015 Epoch 30/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8987 - val_loss: 0.7902 - val_accuracy: 0.9003 Epoch 31/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8988 - val_loss: 0.7887 - val_accuracy: 0.9010 Epoch 32/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8050 - accuracy: 0.8986 - val_loss: 0.7886 - val_accuracy: 0.9013 Epoch 33/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8990 - val_loss: 0.7891 - val_accuracy: 0.9012 Epoch 34/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8988 - val_loss: 0.7896 - val_accuracy: 0.9007 Epoch 35/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8049 - accuracy: 0.8990 - val_loss: 0.7885 - val_accuracy: 0.9009 Epoch 36/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8990 - val_loss: 0.7882 - val_accuracy: 0.9014 Epoch 37/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.8990 - val_loss: 0.7890 - val_accuracy: 0.9008 Epoch 38/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8995 - val_loss: 0.7893 - val_accuracy: 0.9008 Epoch 39/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8049 - accuracy: 0.8990 - val_loss: 0.7883 - val_accuracy: 0.9017 Epoch 40/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8991 - val_loss: 0.7890 - val_accuracy: 0.9007 Epoch 41/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8993 - val_loss: 0.7884 - val_accuracy: 0.9014 Epoch 42/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.8991 - val_loss: 0.7890 - val_accuracy: 0.9009 Epoch 43/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8994 - val_loss: 0.7890 - val_accuracy: 0.9013 Epoch 44/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8993 - val_loss: 0.7884 - val_accuracy: 0.9016 Epoch 45/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.8990 - val_loss: 0.7891 - val_accuracy: 0.9016 Epoch 46/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8995 - val_loss: 0.7886 - val_accuracy: 0.9007 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.8994 - val_loss: 0.7885 - val_accuracy: 0.9015 Epoch 48/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8044 - accuracy: 0.8994 - val_loss: 0.7876 - val_accuracy: 0.9016 Epoch 49/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8997 - val_loss: 0.7873 - val_accuracy: 0.9022 Epoch 50/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8996 - val_loss: 0.7883 - val_accuracy: 0.9014 Epoch 51/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.8997 - val_loss: 0.7880 - val_accuracy: 0.9024 Epoch 52/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.8992 - val_loss: 0.7883 - val_accuracy: 0.9020 Epoch 53/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.8999 - val_loss: 0.7892 - val_accuracy: 0.9016 Epoch 54/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.8996 - val_loss: 0.7880 - val_accuracy: 0.9020 Epoch 55/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.8996 - val_loss: 0.7880 - val_accuracy: 0.9024 Epoch 56/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9000 - val_loss: 0.7882 - val_accuracy: 0.9021 Epoch 57/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9000 - val_loss: 0.7883 - val_accuracy: 0.9015 Epoch 58/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9000 - val_loss: 0.7882 - val_accuracy: 0.9017 Epoch 59/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.8998 - val_loss: 0.7886 - val_accuracy: 0.9016 Epoch 60/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9000 - val_loss: 0.7883 - val_accuracy: 0.9017 Epoch 61/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9001 - val_loss: 0.7871 - val_accuracy: 0.9029 Epoch 62/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9002 - val_loss: 0.7880 - val_accuracy: 0.9027 Epoch 63/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9000 - val_loss: 0.7879 - val_accuracy: 0.9020 Epoch 64/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9003 - val_loss: 0.7873 - val_accuracy: 0.9027 Epoch 65/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.8999 - val_loss: 0.7877 - val_accuracy: 0.9026 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9033 Epoch 67/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7887 - val_accuracy: 0.9024 Epoch 68/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9002 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 69/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9021 Epoch 70/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9004 - val_loss: 0.7887 - val_accuracy: 0.9019 Epoch 71/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7880 - val_accuracy: 0.9025 Epoch 72/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7880 - val_accuracy: 0.9030 Epoch 73/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7876 - val_accuracy: 0.9027 Epoch 74/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9003 - val_loss: 0.7877 - val_accuracy: 0.9029 Epoch 75/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9004 - val_loss: 0.7880 - val_accuracy: 0.9035 Epoch 76/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7885 - val_accuracy: 0.9029 Epoch 77/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7880 - val_accuracy: 0.9023 Epoch 78/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9004 - val_loss: 0.7877 - val_accuracy: 0.9028 Epoch 79/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9023 Epoch 80/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9004 - val_loss: 0.7883 - val_accuracy: 0.9021 Epoch 81/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9028 Epoch 82/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7862 - val_accuracy: 0.9034 Epoch 83/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9004 - val_loss: 0.7872 - val_accuracy: 0.9038 Epoch 84/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7878 - val_accuracy: 0.9031 Epoch 85/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7872 - val_accuracy: 0.9036 Epoch 86/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9006 - val_loss: 0.7878 - val_accuracy: 0.9038 Epoch 87/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7870 - val_accuracy: 0.9033 Epoch 88/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7865 - val_accuracy: 0.9038 Epoch 89/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7871 - val_accuracy: 0.9031 Epoch 90/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9039 Epoch 91/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9003 - val_loss: 0.7871 - val_accuracy: 0.9026 Epoch 92/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9005 - val_loss: 0.7882 - val_accuracy: 0.9033 Epoch 93/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9003 - val_loss: 0.7872 - val_accuracy: 0.9027 Epoch 94/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7878 - val_accuracy: 0.9036 Epoch 95/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9029 Epoch 96/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7873 - val_accuracy: 0.9030 Epoch 97/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7881 - val_accuracy: 0.9033 Epoch 98/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7863 - val_accuracy: 0.9035 Epoch 99/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9036 Epoch 100/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7872 - val_accuracy: 0.9036 Epoch 101/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9034 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9037 Epoch 103/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9036 Epoch 104/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9030 Epoch 105/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9035 Epoch 106/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9031 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7874 - val_accuracy: 0.9035 Epoch 108/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7880 - val_accuracy: 0.9029 Epoch 109/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7869 - val_accuracy: 0.9040 Epoch 110/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9028 Epoch 111/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7866 - val_accuracy: 0.9034 Epoch 112/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9032 Epoch 113/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9037 Epoch 114/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9031 Epoch 115/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7877 - val_accuracy: 0.9031 Epoch 116/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9008 - val_loss: 0.7865 - val_accuracy: 0.9038 Epoch 117/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7869 - val_accuracy: 0.9038 Epoch 118/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9022 Epoch 119/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9030 Epoch 120/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7874 - val_accuracy: 0.9034 Epoch 121/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7877 - val_accuracy: 0.9033 Epoch 122/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7868 - val_accuracy: 0.9036 Epoch 123/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7877 - val_accuracy: 0.9034 Epoch 124/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7879 - val_accuracy: 0.9027 Epoch 125/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9004 - val_loss: 0.7874 - val_accuracy: 0.9032 Epoch 126/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7875 - val_accuracy: 0.9033 Epoch 127/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7865 - val_accuracy: 0.9037 Epoch 128/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9032 Epoch 129/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9036 Epoch 130/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7868 - val_accuracy: 0.9034 Epoch 131/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9034 Epoch 132/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7880 - val_accuracy: 0.9030 Epoch 133/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7877 - val_accuracy: 0.9036 Epoch 134/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9037 Epoch 135/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9036 Epoch 136/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9031 Epoch 137/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7869 - val_accuracy: 0.9039 Epoch 138/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7867 - val_accuracy: 0.9029 Epoch 139/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9038 Epoch 140/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7876 - val_accuracy: 0.9034 Epoch 141/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9037 Epoch 142/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9005 - val_loss: 0.7869 - val_accuracy: 0.9033 Epoch 143/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7867 - val_accuracy: 0.9038 Epoch 144/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9031 Epoch 145/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9039 Epoch 146/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9005 - val_loss: 0.7880 - val_accuracy: 0.9032 Epoch 147/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7865 - val_accuracy: 0.9041 Epoch 148/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9005 - val_loss: 0.7876 - val_accuracy: 0.9033 Epoch 149/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9032 Epoch 150/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7871 - val_accuracy: 0.9041 Epoch 151/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9036 Epoch 152/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9039 Epoch 153/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7877 - val_accuracy: 0.9039 Epoch 154/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9002 - val_loss: 0.7874 - val_accuracy: 0.9038 Epoch 155/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9028 Epoch 156/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9035 Epoch 157/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7878 - val_accuracy: 0.9033 Epoch 158/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9032 Epoch 159/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7874 - val_accuracy: 0.9038 Epoch 160/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7867 - val_accuracy: 0.9041 Epoch 161/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7873 - val_accuracy: 0.9038 Epoch 162/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9032 Epoch 163/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7876 - val_accuracy: 0.9034 Epoch 164/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9027 Epoch 165/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7877 - val_accuracy: 0.9034 Epoch 166/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9038 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9033 Epoch 168/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9030 Epoch 169/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9004 - val_loss: 0.7860 - val_accuracy: 0.9031 Epoch 170/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9040 Epoch 171/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7870 - val_accuracy: 0.9032 Epoch 172/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9005 - val_loss: 0.7870 - val_accuracy: 0.9034 Epoch 173/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9036 Epoch 174/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7861 - val_accuracy: 0.9041 Epoch 175/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9005 - val_loss: 0.7866 - val_accuracy: 0.9039 Epoch 176/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9029 Epoch 177/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9035 Epoch 178/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7872 - val_accuracy: 0.9036 Epoch 179/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7870 - val_accuracy: 0.9029 Epoch 180/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9031 Epoch 181/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9009 - val_loss: 0.7870 - val_accuracy: 0.9041 Epoch 182/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9036 Epoch 183/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 184/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9009 - val_loss: 0.7874 - val_accuracy: 0.9043 Epoch 185/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7871 - val_accuracy: 0.9032 Epoch 186/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7877 - val_accuracy: 0.9035 Epoch 187/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9039 Epoch 188/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9040 Epoch 189/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9033 Epoch 190/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7876 - val_accuracy: 0.9032 Epoch 191/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7881 - val_accuracy: 0.9026 Epoch 192/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7881 - val_accuracy: 0.9031 Epoch 193/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9004 - val_loss: 0.7867 - val_accuracy: 0.9043 Epoch 194/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9005 - val_loss: 0.7870 - val_accuracy: 0.9037 Epoch 195/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9036 Epoch 196/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9040 Epoch 197/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7871 - val_accuracy: 0.9036 Epoch 198/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9033 Epoch 199/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9036 Epoch 200/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7867 - val_accuracy: 0.9037 Epoch 1/200 235/235 [==============================] - 2s 9ms/step - loss: 0.4777 - accuracy: 0.8683 - val_loss: 0.2514 - val_accuracy: 0.9274 Epoch 2/200 235/235 [==============================] - 2s 8ms/step - loss: 0.2277 - accuracy: 0.9330 - val_loss: 0.1910 - val_accuracy: 0.9436 Epoch 3/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1733 - accuracy: 0.9496 - val_loss: 0.1576 - val_accuracy: 0.9535 Epoch 4/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1389 - accuracy: 0.9594 - val_loss: 0.1368 - val_accuracy: 0.9580 Epoch 5/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1145 - accuracy: 0.9663 - val_loss: 0.1230 - val_accuracy: 0.9624 Epoch 6/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0958 - accuracy: 0.9714 - val_loss: 0.1150 - val_accuracy: 0.9638 Epoch 7/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0813 - accuracy: 0.9762 - val_loss: 0.1092 - val_accuracy: 0.9647 Epoch 8/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0692 - accuracy: 0.9801 - val_loss: 0.1065 - val_accuracy: 0.9657 Epoch 9/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0595 - accuracy: 0.9829 - val_loss: 0.1047 - val_accuracy: 0.9665 Epoch 10/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0511 - accuracy: 0.9857 - val_loss: 0.1029 - val_accuracy: 0.9678 Epoch 11/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0443 - accuracy: 0.9880 - val_loss: 0.1032 - val_accuracy: 0.9675 Epoch 12/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0383 - accuracy: 0.9900 - val_loss: 0.1015 - val_accuracy: 0.9684 Epoch 13/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0331 - accuracy: 0.9918 - val_loss: 0.1025 - val_accuracy: 0.9690 Epoch 14/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0286 - accuracy: 0.9933 - val_loss: 0.1036 - val_accuracy: 0.9692 Epoch 15/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0248 - accuracy: 0.9945 - val_loss: 0.1045 - val_accuracy: 0.9703 Epoch 16/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0214 - accuracy: 0.9954 - val_loss: 0.1045 - val_accuracy: 0.9715 Epoch 17/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0183 - accuracy: 0.9963 - val_loss: 0.1058 - val_accuracy: 0.9713 Epoch 18/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.1051 - val_accuracy: 0.9724 Epoch 19/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0141 - accuracy: 0.9974 - val_loss: 0.1099 - val_accuracy: 0.9721 Epoch 20/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0123 - accuracy: 0.9979 - val_loss: 0.1116 - val_accuracy: 0.9716 Epoch 21/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0111 - accuracy: 0.9982 - val_loss: 0.1156 - val_accuracy: 0.9718 Epoch 22/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0103 - accuracy: 0.9980 - val_loss: 0.1214 - val_accuracy: 0.9709 Epoch 23/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9979 - val_loss: 0.1223 - val_accuracy: 0.9701 Epoch 24/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9977 - val_loss: 0.1239 - val_accuracy: 0.9715 Epoch 25/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0099 - accuracy: 0.9972 - val_loss: 0.1301 - val_accuracy: 0.9700 Epoch 26/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0079 - accuracy: 0.9980 - val_loss: 0.1345 - val_accuracy: 0.9701 Epoch 27/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.1351 - val_accuracy: 0.9710 Epoch 28/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0053 - accuracy: 0.9991 - val_loss: 0.1498 - val_accuracy: 0.9690 Epoch 29/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0051 - accuracy: 0.9991 - val_loss: 0.1463 - val_accuracy: 0.9704 Epoch 30/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0044 - accuracy: 0.9991 - val_loss: 0.1408 - val_accuracy: 0.9695 Epoch 31/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.1352 - val_accuracy: 0.9713 Epoch 32/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 0.1360 - val_accuracy: 0.9730 Epoch 33/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1378 - val_accuracy: 0.9717 Epoch 34/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 0.9999 - val_loss: 0.1401 - val_accuracy: 0.9716 Epoch 35/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1450 - val_accuracy: 0.9717 Epoch 36/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9727 Epoch 37/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9708 Epoch 38/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1573 - val_accuracy: 0.9712 Epoch 39/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0209 - accuracy: 0.9934 - val_loss: 0.1639 - val_accuracy: 0.9692 Epoch 40/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9972 - val_loss: 0.1570 - val_accuracy: 0.9713 Epoch 41/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.1566 - val_accuracy: 0.9733 Epoch 42/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1427 - val_accuracy: 0.9754 Epoch 43/200 235/235 [==============================] - 2s 9ms/step - loss: 6.9572e-04 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9750 Epoch 44/200 235/235 [==============================] - 2s 9ms/step - loss: 5.0109e-04 - accuracy: 1.0000 - val_loss: 0.1439 - val_accuracy: 0.9746 Epoch 45/200 235/235 [==============================] - 2s 8ms/step - loss: 4.0017e-04 - accuracy: 1.0000 - val_loss: 0.1444 - val_accuracy: 0.9747 Epoch 46/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4035e-04 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 0.9746 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 3.0142e-04 - accuracy: 1.0000 - val_loss: 0.1458 - val_accuracy: 0.9746 Epoch 48/200 235/235 [==============================] - 2s 8ms/step - loss: 2.7058e-04 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9749 Epoch 49/200 235/235 [==============================] - 2s 9ms/step - loss: 2.4405e-04 - accuracy: 1.0000 - val_loss: 0.1477 - val_accuracy: 0.9748 Epoch 50/200 235/235 [==============================] - 2s 9ms/step - loss: 2.2169e-04 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9748 Epoch 51/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0148e-04 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9747 Epoch 52/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8346e-04 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9748 Epoch 53/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6685e-04 - accuracy: 1.0000 - val_loss: 0.1521 - val_accuracy: 0.9748 Epoch 54/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5204e-04 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9748 Epoch 55/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3855e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9749 Epoch 56/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2598e-04 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9748 Epoch 57/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1443e-04 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9748 Epoch 58/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0383e-04 - accuracy: 1.0000 - val_loss: 0.1591 - val_accuracy: 0.9748 Epoch 59/200 235/235 [==============================] - 2s 8ms/step - loss: 9.4306e-05 - accuracy: 1.0000 - val_loss: 0.1606 - val_accuracy: 0.9746 Epoch 60/200 235/235 [==============================] - 2s 8ms/step - loss: 8.5305e-05 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9746 Epoch 61/200 235/235 [==============================] - 2s 8ms/step - loss: 7.7055e-05 - accuracy: 1.0000 - val_loss: 0.1636 - val_accuracy: 0.9746 Epoch 62/200 235/235 [==============================] - 2s 8ms/step - loss: 6.9650e-05 - accuracy: 1.0000 - val_loss: 0.1653 - val_accuracy: 0.9745 Epoch 63/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2657e-05 - accuracy: 1.0000 - val_loss: 0.1669 - val_accuracy: 0.9744 Epoch 64/200 235/235 [==============================] - 2s 8ms/step - loss: 5.6425e-05 - accuracy: 1.0000 - val_loss: 0.1687 - val_accuracy: 0.9743 Epoch 65/200 235/235 [==============================] - 2s 8ms/step - loss: 5.0673e-05 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9745 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 4.5437e-05 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9743 Epoch 67/200 235/235 [==============================] - 2s 8ms/step - loss: 4.0690e-05 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9744 Epoch 68/200 235/235 [==============================] - 2s 8ms/step - loss: 3.6409e-05 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9741 Epoch 69/200 235/235 [==============================] - 2s 9ms/step - loss: 3.2474e-05 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9741 Epoch 70/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8983e-05 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9740 Epoch 71/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5818e-05 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9740 Epoch 72/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2957e-05 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9740 Epoch 73/200 235/235 [==============================] - 2s 9ms/step - loss: 2.0407e-05 - accuracy: 1.0000 - val_loss: 0.1851 - val_accuracy: 0.9740 Epoch 74/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8170e-05 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9740 Epoch 75/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6094e-05 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9740 Epoch 76/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4279e-05 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9740 Epoch 77/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2640e-05 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9741 Epoch 78/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1203e-05 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9741 Epoch 79/200 235/235 [==============================] - 2s 8ms/step - loss: 9.9136e-06 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9742 Epoch 80/200 235/235 [==============================] - 2s 8ms/step - loss: 8.7656e-06 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9741 Epoch 81/200 235/235 [==============================] - 2s 8ms/step - loss: 7.7456e-06 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9739 Epoch 82/200 235/235 [==============================] - 2s 8ms/step - loss: 6.8530e-06 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9739 Epoch 83/200 235/235 [==============================] - 2s 9ms/step - loss: 6.0533e-06 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9739 Epoch 84/200 235/235 [==============================] - 2s 8ms/step - loss: 5.3416e-06 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9741 Epoch 85/200 235/235 [==============================] - 2s 9ms/step - loss: 4.7242e-06 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9740 Epoch 86/200 235/235 [==============================] - 2s 8ms/step - loss: 4.1734e-06 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9739 Epoch 87/200 235/235 [==============================] - 2s 8ms/step - loss: 3.6822e-06 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9739 Epoch 88/200 235/235 [==============================] - 2s 8ms/step - loss: 3.2493e-06 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9737 Epoch 89/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8682e-06 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9738 Epoch 90/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5365e-06 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9738 Epoch 91/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2367e-06 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9739 Epoch 92/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9777e-06 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9738 Epoch 93/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7475e-06 - accuracy: 1.0000 - val_loss: 0.2241 - val_accuracy: 0.9736 Epoch 94/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5433e-06 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9737 Epoch 95/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3678e-06 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9739 Epoch 96/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2074e-06 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 0.9735 Epoch 97/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0708e-06 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9739 Epoch 98/200 235/235 [==============================] - 2s 8ms/step - loss: 9.4819e-07 - accuracy: 1.0000 - val_loss: 0.2339 - val_accuracy: 0.9741 Epoch 99/200 235/235 [==============================] - 2s 9ms/step - loss: 8.4005e-07 - accuracy: 1.0000 - val_loss: 0.2356 - val_accuracy: 0.9739 Epoch 100/200 235/235 [==============================] - 2s 8ms/step - loss: 7.4570e-07 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9741 Epoch 101/200 235/235 [==============================] - 2s 8ms/step - loss: 6.6061e-07 - accuracy: 1.0000 - val_loss: 0.2394 - val_accuracy: 0.9740 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 5.8695e-07 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9739 Epoch 103/200 235/235 [==============================] - 2s 8ms/step - loss: 5.2292e-07 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9739 Epoch 104/200 235/235 [==============================] - 2s 8ms/step - loss: 4.6375e-07 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9739 Epoch 105/200 235/235 [==============================] - 2s 8ms/step - loss: 4.1361e-07 - accuracy: 1.0000 - val_loss: 0.2466 - val_accuracy: 0.9739 Epoch 106/200 235/235 [==============================] - 2s 10ms/step - loss: 3.6890e-07 - accuracy: 1.0000 - val_loss: 0.2483 - val_accuracy: 0.9739 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 3.2952e-07 - accuracy: 1.0000 - val_loss: 0.2500 - val_accuracy: 0.9739 Epoch 108/200 235/235 [==============================] - 2s 9ms/step - loss: 2.9483e-07 - accuracy: 1.0000 - val_loss: 0.2519 - val_accuracy: 0.9740 Epoch 109/200 235/235 [==============================] - 2s 9ms/step - loss: 2.6338e-07 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9740 Epoch 110/200 235/235 [==============================] - 2s 8ms/step - loss: 2.3657e-07 - accuracy: 1.0000 - val_loss: 0.2553 - val_accuracy: 0.9739 Epoch 111/200 235/235 [==============================] - 2s 9ms/step - loss: 2.1258e-07 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9738 Epoch 112/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9068e-07 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9739 Epoch 113/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7193e-07 - accuracy: 1.0000 - val_loss: 0.2601 - val_accuracy: 0.9738 Epoch 114/200 235/235 [==============================] - 2s 10ms/step - loss: 1.5517e-07 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9739 Epoch 115/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4024e-07 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9739 Epoch 116/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2701e-07 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9737 Epoch 117/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1551e-07 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9738 Epoch 118/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0498e-07 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9739 Epoch 119/200 235/235 [==============================] - 2s 8ms/step - loss: 9.5806e-08 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9739 Epoch 120/200 235/235 [==============================] - 2s 9ms/step - loss: 8.7490e-08 - accuracy: 1.0000 - val_loss: 0.2701 - val_accuracy: 0.9740 Epoch 121/200 235/235 [==============================] - 2s 8ms/step - loss: 8.0045e-08 - accuracy: 1.0000 - val_loss: 0.2714 - val_accuracy: 0.9740 Epoch 122/200 235/235 [==============================] - 2s 8ms/step - loss: 7.3725e-08 - accuracy: 1.0000 - val_loss: 0.2726 - val_accuracy: 0.9740 Epoch 123/200 235/235 [==============================] - 2s 8ms/step - loss: 6.7717e-08 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9738 Epoch 124/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2557e-08 - accuracy: 1.0000 - val_loss: 0.2750 - val_accuracy: 0.9739 Epoch 125/200 235/235 [==============================] - 2s 8ms/step - loss: 5.7844e-08 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9738 Epoch 126/200 235/235 [==============================] - 2s 8ms/step - loss: 5.3420e-08 - accuracy: 1.0000 - val_loss: 0.2772 - val_accuracy: 0.9738 Epoch 127/200 235/235 [==============================] - 2s 8ms/step - loss: 4.9692e-08 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9737 Epoch 128/200 235/235 [==============================] - 2s 9ms/step - loss: 4.6269e-08 - accuracy: 1.0000 - val_loss: 0.2793 - val_accuracy: 0.9737 Epoch 129/200 235/235 [==============================] - 2s 9ms/step - loss: 4.3144e-08 - accuracy: 1.0000 - val_loss: 0.2802 - val_accuracy: 0.9737 Epoch 130/200 235/235 [==============================] - 2s 8ms/step - loss: 4.0444e-08 - accuracy: 1.0000 - val_loss: 0.2811 - val_accuracy: 0.9736 Epoch 131/200 235/235 [==============================] - 2s 9ms/step - loss: 3.7827e-08 - accuracy: 1.0000 - val_loss: 0.2821 - val_accuracy: 0.9736 Epoch 132/200 235/235 [==============================] - 2s 8ms/step - loss: 3.5518e-08 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9737 Epoch 133/200 235/235 [==============================] - 2s 8ms/step - loss: 3.3410e-08 - accuracy: 1.0000 - val_loss: 0.2837 - val_accuracy: 0.9737 Epoch 134/200 235/235 [==============================] - 2s 8ms/step - loss: 3.1543e-08 - accuracy: 1.0000 - val_loss: 0.2843 - val_accuracy: 0.9738 Epoch 135/200 235/235 [==============================] - 2s 8ms/step - loss: 2.9808e-08 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9738 Epoch 136/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8175e-08 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 0.9738 Epoch 137/200 235/235 [==============================] - 2s 9ms/step - loss: 2.6733e-08 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9738 Epoch 138/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5499e-08 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9738 Epoch 139/200 235/235 [==============================] - 2s 9ms/step - loss: 2.4267e-08 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9737 Epoch 140/200 235/235 [==============================] - 2s 9ms/step - loss: 2.3109e-08 - accuracy: 1.0000 - val_loss: 0.2886 - val_accuracy: 0.9736 Epoch 141/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2127e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9737 Epoch 142/200 235/235 [==============================] - 2s 8ms/step - loss: 2.1162e-08 - accuracy: 1.0000 - val_loss: 0.2897 - val_accuracy: 0.9736 Epoch 143/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0281e-08 - accuracy: 1.0000 - val_loss: 0.2903 - val_accuracy: 0.9738 Epoch 144/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9501e-08 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9738 Epoch 145/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8654e-08 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9738 Epoch 146/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7899e-08 - accuracy: 1.0000 - val_loss: 0.2921 - val_accuracy: 0.9739 Epoch 147/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7240e-08 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9738 Epoch 148/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6620e-08 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9741 Epoch 149/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6069e-08 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9740 Epoch 150/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5501e-08 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9738 Epoch 151/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5046e-08 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9739 Epoch 152/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4522e-08 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.9737 Epoch 153/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4146e-08 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9737 Epoch 154/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3707e-08 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9737 Epoch 155/200 235/235 [==============================] - 3s 11ms/step - loss: 1.3242e-08 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9736 Epoch 156/200 235/235 [==============================] - 3s 11ms/step - loss: 1.2924e-08 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9736 Epoch 157/200 235/235 [==============================] - 2s 10ms/step - loss: 1.2523e-08 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9736 Epoch 158/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2173e-08 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9736 Epoch 159/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1752e-08 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9736 Epoch 160/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1476e-08 - accuracy: 1.0000 - val_loss: 0.2986 - val_accuracy: 0.9737 Epoch 161/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1212e-08 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9737 Epoch 162/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0908e-08 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9737 Epoch 163/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0697e-08 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9737 Epoch 164/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0391e-08 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9737 Epoch 165/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0155e-08 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9739 Epoch 166/200 235/235 [==============================] - 2s 8ms/step - loss: 9.9321e-09 - accuracy: 1.0000 - val_loss: 0.3008 - val_accuracy: 0.9739 Epoch 167/200 235/235 [==============================] - 2s 8ms/step - loss: 9.6997e-09 - accuracy: 1.0000 - val_loss: 0.3012 - val_accuracy: 0.9739 Epoch 168/200 235/235 [==============================] - 2s 8ms/step - loss: 9.4533e-09 - accuracy: 1.0000 - val_loss: 0.3014 - val_accuracy: 0.9739 Epoch 169/200 235/235 [==============================] - 2s 8ms/step - loss: 9.2844e-09 - accuracy: 1.0000 - val_loss: 0.3016 - val_accuracy: 0.9739 Epoch 170/200 235/235 [==============================] - 2s 8ms/step - loss: 9.0440e-09 - accuracy: 1.0000 - val_loss: 0.3019 - val_accuracy: 0.9740 Epoch 171/200 235/235 [==============================] - 2s 8ms/step - loss: 8.8374e-09 - accuracy: 1.0000 - val_loss: 0.3021 - val_accuracy: 0.9740 Epoch 172/200 235/235 [==============================] - 2s 8ms/step - loss: 8.6705e-09 - accuracy: 1.0000 - val_loss: 0.3024 - val_accuracy: 0.9739 Epoch 173/200 235/235 [==============================] - 2s 8ms/step - loss: 8.4718e-09 - accuracy: 1.0000 - val_loss: 0.3027 - val_accuracy: 0.9740 Epoch 174/200 235/235 [==============================] - 2s 8ms/step - loss: 8.2990e-09 - accuracy: 1.0000 - val_loss: 0.3028 - val_accuracy: 0.9740 Epoch 175/200 235/235 [==============================] - 2s 8ms/step - loss: 8.1321e-09 - accuracy: 1.0000 - val_loss: 0.3031 - val_accuracy: 0.9741 Epoch 176/200 235/235 [==============================] - 2s 8ms/step - loss: 8.0129e-09 - accuracy: 1.0000 - val_loss: 0.3033 - val_accuracy: 0.9742 Epoch 177/200 235/235 [==============================] - 2s 8ms/step - loss: 7.8003e-09 - accuracy: 1.0000 - val_loss: 0.3035 - val_accuracy: 0.9742 Epoch 178/200 235/235 [==============================] - 2s 8ms/step - loss: 7.6711e-09 - accuracy: 1.0000 - val_loss: 0.3038 - val_accuracy: 0.9742 Epoch 179/200 235/235 [==============================] - 2s 8ms/step - loss: 7.4883e-09 - accuracy: 1.0000 - val_loss: 0.3041 - val_accuracy: 0.9740 Epoch 180/200 235/235 [==============================] - 2s 8ms/step - loss: 7.3910e-09 - accuracy: 1.0000 - val_loss: 0.3043 - val_accuracy: 0.9742 Epoch 181/200 235/235 [==============================] - 2s 8ms/step - loss: 7.2479e-09 - accuracy: 1.0000 - val_loss: 0.3045 - val_accuracy: 0.9740 Epoch 182/200 235/235 [==============================] - 2s 8ms/step - loss: 7.1247e-09 - accuracy: 1.0000 - val_loss: 0.3046 - val_accuracy: 0.9740 Epoch 183/200 235/235 [==============================] - 2s 8ms/step - loss: 7.0333e-09 - accuracy: 1.0000 - val_loss: 0.3049 - val_accuracy: 0.9741 Epoch 184/200 235/235 [==============================] - 2s 8ms/step - loss: 6.8386e-09 - accuracy: 1.0000 - val_loss: 0.3050 - val_accuracy: 0.9741 Epoch 185/200 235/235 [==============================] - 2s 8ms/step - loss: 6.7810e-09 - accuracy: 1.0000 - val_loss: 0.3051 - val_accuracy: 0.9740 Epoch 186/200 235/235 [==============================] - 2s 8ms/step - loss: 6.6737e-09 - accuracy: 1.0000 - val_loss: 0.3054 - val_accuracy: 0.9740 Epoch 187/200 235/235 [==============================] - 2s 8ms/step - loss: 6.5426e-09 - accuracy: 1.0000 - val_loss: 0.3055 - val_accuracy: 0.9739 Epoch 188/200 235/235 [==============================] - 2s 8ms/step - loss: 6.4393e-09 - accuracy: 1.0000 - val_loss: 0.3055 - val_accuracy: 0.9739 Epoch 189/200 235/235 [==============================] - 2s 8ms/step - loss: 6.3181e-09 - accuracy: 1.0000 - val_loss: 0.3057 - val_accuracy: 0.9739 Epoch 190/200 235/235 [==============================] - 2s 8ms/step - loss: 6.1909e-09 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.9739 Epoch 191/200 235/235 [==============================] - 2s 8ms/step - loss: 6.0558e-09 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.9739 Epoch 192/200 235/235 [==============================] - 2s 8ms/step - loss: 5.9684e-09 - accuracy: 1.0000 - val_loss: 0.3060 - val_accuracy: 0.9739 Epoch 193/200 235/235 [==============================] - 2s 8ms/step - loss: 5.8591e-09 - accuracy: 1.0000 - val_loss: 0.3062 - val_accuracy: 0.9740 Epoch 194/200 235/235 [==============================] - 2s 8ms/step - loss: 5.8035e-09 - accuracy: 1.0000 - val_loss: 0.3063 - val_accuracy: 0.9739 Epoch 195/200 235/235 [==============================] - 2s 8ms/step - loss: 5.6644e-09 - accuracy: 1.0000 - val_loss: 0.3064 - val_accuracy: 0.9739 Epoch 196/200 235/235 [==============================] - 2s 8ms/step - loss: 5.6227e-09 - accuracy: 1.0000 - val_loss: 0.3065 - val_accuracy: 0.9739 Epoch 197/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5253e-09 - accuracy: 1.0000 - val_loss: 0.3066 - val_accuracy: 0.9740 Epoch 198/200 235/235 [==============================] - 2s 8ms/step - loss: 5.4340e-09 - accuracy: 1.0000 - val_loss: 0.3068 - val_accuracy: 0.9738 Epoch 199/200 235/235 [==============================] - 2s 8ms/step - loss: 5.3167e-09 - accuracy: 1.0000 - val_loss: 0.3068 - val_accuracy: 0.9737 Epoch 200/200 235/235 [==============================] - 2s 8ms/step - loss: 5.2571e-09 - accuracy: 1.0000 - val_loss: 0.3070 - val_accuracy: 0.9738 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03707949258387089 Thresholhold -0.04958835244178772 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06157502718269825 Thresholhold -0.06629646569490433 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.11812010034918785 Thresholhold 0.22601225972175598 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. 0. 1. 0. 1. 0. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 1. 0. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 1.] [1. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [1. 0. 0. 0. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [0. 1. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 1. 0. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 1. 0. 1. 1.] [0. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 0. 1. 0.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 0. 1. 1. 1. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 0. 0. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 6/235 [..............................] - ETA: 2s - loss: 7.5542 - accuracy: 0.4401 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0101s vs `on_train_batch_begin` time: 11.3151s). Check your callbacks. 235/235 [==============================] - 72s 12ms/step - loss: 2.1643 - accuracy: 0.9241 - val_loss: 1.5254 - val_accuracy: 0.8979 [ 1.1987437e-07 3.7992351e-07 -1.9329187e-07 ... -1.0124538e-01 -1.5895198e-01 -1.8158959e-01] Sparsity at: 0.0018782870022539444 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4412 - accuracy: 0.9591 - val_loss: 0.4845 - val_accuracy: 0.9519 [-2.1197655e-12 -1.3815716e-12 -3.7385017e-12 ... -9.3455590e-02 -1.2025412e-01 -1.1961582e-01] Sparsity at: 0.0018782870022539444 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.3170 - accuracy: 0.9633 - val_loss: 0.3509 - val_accuracy: 0.9461 [-7.2531770e-18 7.6473021e-18 -5.5772425e-19 ... -8.3159439e-02 -9.5205635e-02 -8.6928986e-02] Sparsity at: 0.0018782870022539444 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2865 - accuracy: 0.9651 - val_loss: 0.3236 - val_accuracy: 0.9500 [ 4.1198545e-23 3.3774622e-23 -5.5448831e-23 ... -8.0384046e-02 -7.4459568e-02 -6.9936521e-02] Sparsity at: 0.0018782870022539444 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2700 - accuracy: 0.9661 - val_loss: 0.3228 - val_accuracy: 0.9463 [ 2.0431059e-28 -1.4075645e-28 3.9012263e-28 ... -7.3833123e-02 -6.2158316e-02 -6.0852002e-02] Sparsity at: 0.0018782870022539444 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2538 - accuracy: 0.9675 - val_loss: 0.2945 - val_accuracy: 0.9534 [-9.8549911e-34 -4.8944090e-34 1.1907741e-33 ... -7.0309490e-02 -5.6259133e-02 -5.3487781e-02] Sparsity at: 0.0018782870022539444 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2450 - accuracy: 0.9688 - val_loss: 0.3052 - val_accuracy: 0.9465 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -6.2099099e-02 -5.3408753e-02 -3.5924356e-02] Sparsity at: 0.0018858001502629603 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2315 - accuracy: 0.9707 - val_loss: 0.2814 - val_accuracy: 0.9529 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -6.4758100e-02 -4.8410501e-02 -3.5459254e-02] Sparsity at: 0.0018858001502629603 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2295 - accuracy: 0.9704 - val_loss: 0.2787 - val_accuracy: 0.9500 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -6.2133372e-02 -4.7365736e-02 -2.8271766e-02] Sparsity at: 0.0018858001502629603 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2207 - accuracy: 0.9711 - val_loss: 0.2897 - val_accuracy: 0.9465 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.8439385e-02 -4.0382292e-02 -2.7753411e-02] Sparsity at: 0.0018858001502629603 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2193 - accuracy: 0.9710 - val_loss: 0.2570 - val_accuracy: 0.9572 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.8699984e-02 -3.5502981e-02 -2.1907805e-02] Sparsity at: 0.0018858001502629603 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2091 - accuracy: 0.9721 - val_loss: 0.2535 - val_accuracy: 0.9577 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.4798331e-02 -3.5884604e-02 -1.6109383e-02] Sparsity at: 0.001889556724267468 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2157 - accuracy: 0.9704 - val_loss: 0.2364 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.5758931e-02 -3.0850254e-02 -1.5591446e-02] Sparsity at: 0.001889556724267468 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2030 - accuracy: 0.9727 - val_loss: 0.2642 - val_accuracy: 0.9527 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.5337116e-02 -2.1567367e-02 -1.5932798e-02] Sparsity at: 0.001889556724267468 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2037 - accuracy: 0.9722 - val_loss: 0.2426 - val_accuracy: 0.9557 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.6176368e-02 -2.9497460e-02 -1.4442829e-02] Sparsity at: 0.001889556724267468 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2000 - accuracy: 0.9722 - val_loss: 0.2692 - val_accuracy: 0.9502 [-2.56036835e-34 -4.89440904e-34 2.81328974e-34 ... -4.88829091e-02 -2.79149693e-02 -1.20111285e-02] Sparsity at: 0.001889556724267468 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1956 - accuracy: 0.9728 - val_loss: 0.3358 - val_accuracy: 0.9243 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.4926699e-02 -2.4626618e-02 -9.8563870e-03] Sparsity at: 0.001893313298271976 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1968 - accuracy: 0.9716 - val_loss: 0.2332 - val_accuracy: 0.9595 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1401673e-02 -2.8528711e-02 -6.5178238e-03] Sparsity at: 0.001893313298271976 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1901 - accuracy: 0.9733 - val_loss: 0.2428 - val_accuracy: 0.9563 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.2622562e-02 -2.7970558e-02 4.9088029e-03] Sparsity at: 0.0018970698722764838 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1889 - accuracy: 0.9740 - val_loss: 0.2417 - val_accuracy: 0.9556 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.0894061e-02 -2.6123958e-02 -3.3460316e-04] Sparsity at: 0.0018970698722764838 Epoch 21/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1880 - accuracy: 0.9734 - val_loss: 0.2934 - val_accuracy: 0.9397 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.2014655e-02 -2.3893103e-02 3.4353866e-03] Sparsity at: 0.0019008264462809918 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1858 - accuracy: 0.9732 - val_loss: 0.2359 - val_accuracy: 0.9565 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.8390683e-02 -2.2767911e-02 3.7614130e-03] Sparsity at: 0.0019008264462809918 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1815 - accuracy: 0.9746 - val_loss: 0.2112 - val_accuracy: 0.9630 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.1107842e-02 -1.7501112e-02 -1.4017588e-04] Sparsity at: 0.0019008264462809918 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1796 - accuracy: 0.9745 - val_loss: 0.2469 - val_accuracy: 0.9508 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.3388225e-02 -2.5842663e-02 1.1775387e-03] Sparsity at: 0.0019008264462809918 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1780 - accuracy: 0.9743 - val_loss: 0.2299 - val_accuracy: 0.9574 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.2246209e-02 -2.5500111e-02 -2.2653832e-05] Sparsity at: 0.0019008264462809918 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1770 - accuracy: 0.9746 - val_loss: 0.2652 - val_accuracy: 0.9464 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.9731627e-02 -2.1007957e-02 -1.4751096e-03] Sparsity at: 0.0019008264462809918 Epoch 27/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1770 - accuracy: 0.9745 - val_loss: 0.2025 - val_accuracy: 0.9663 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.5657299e-02 -2.3970084e-02 -4.9492614e-03] Sparsity at: 0.0019008264462809918 Epoch 28/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1768 - accuracy: 0.9741 - val_loss: 0.2299 - val_accuracy: 0.9570 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.4599617e-02 -2.2288041e-02 4.8921010e-03] Sparsity at: 0.0019008264462809918 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1787 - accuracy: 0.9735 - val_loss: 0.2743 - val_accuracy: 0.9430 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9804041e-02 -2.1474227e-02 -4.6588755e-03] Sparsity at: 0.0019008264462809918 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1722 - accuracy: 0.9750 - val_loss: 0.2227 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1195694e-02 -1.7941864e-02 -5.5273161e-03] Sparsity at: 0.0019008264462809918 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1732 - accuracy: 0.9738 - val_loss: 0.2314 - val_accuracy: 0.9567 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.4053003e-02 -2.2037793e-02 -7.7174329e-03] Sparsity at: 0.0019008264462809918 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1727 - accuracy: 0.9746 - val_loss: 0.2136 - val_accuracy: 0.9629 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.0067699e-02 -1.8462192e-02 -1.0878566e-02] Sparsity at: 0.0019008264462809918 Epoch 33/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1686 - accuracy: 0.9756 - val_loss: 0.2214 - val_accuracy: 0.9597 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1496612e-02 -1.6327551e-02 -4.8108818e-03] Sparsity at: 0.0019008264462809918 Epoch 34/500 235/235 [==============================] - 3s 12ms/step - loss: 0.1679 - accuracy: 0.9760 - val_loss: 0.2186 - val_accuracy: 0.9618 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9948173e-02 -1.3802285e-02 -7.2569847e-03] Sparsity at: 0.0019008264462809918 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1724 - accuracy: 0.9741 - val_loss: 0.2133 - val_accuracy: 0.9617 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8432308e-02 -1.4483869e-02 -1.2326549e-02] Sparsity at: 0.0019008264462809918 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1676 - accuracy: 0.9760 - val_loss: 0.2201 - val_accuracy: 0.9625 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1252580e-02 -1.1529931e-02 -4.5486768e-03] Sparsity at: 0.0019045830202854997 Epoch 37/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1696 - accuracy: 0.9745 - val_loss: 0.2307 - val_accuracy: 0.9587 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1104961e-02 -1.0497442e-02 -9.5685100e-05] Sparsity at: 0.0019045830202854997 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1675 - accuracy: 0.9750 - val_loss: 0.2200 - val_accuracy: 0.9593 [-2.56036835e-34 -4.89440904e-34 2.81328974e-34 ... -3.56575474e-02 -1.40698245e-02 4.39966936e-03] Sparsity at: 0.0019045830202854997 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1707 - accuracy: 0.9748 - val_loss: 0.2483 - val_accuracy: 0.9520 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3602506e-02 -1.0364095e-02 -6.0723494e-03] Sparsity at: 0.0019045830202854997 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1682 - accuracy: 0.9754 - val_loss: 0.2481 - val_accuracy: 0.9529 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.0430974e-02 -1.4037469e-02 -7.6746955e-03] Sparsity at: 0.0019045830202854997 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1641 - accuracy: 0.9758 - val_loss: 0.2461 - val_accuracy: 0.9501 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2790545e-02 -1.1138388e-02 -5.5829273e-03] Sparsity at: 0.0019045830202854997 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1626 - accuracy: 0.9765 - val_loss: 0.1955 - val_accuracy: 0.9667 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3288438e-02 -1.2605021e-02 -7.6571289e-03] Sparsity at: 0.0019045830202854997 Epoch 43/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1628 - accuracy: 0.9758 - val_loss: 0.2641 - val_accuracy: 0.9435 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3022948e-02 -2.0936467e-02 2.2003949e-03] Sparsity at: 0.0019045830202854997 Epoch 44/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1653 - accuracy: 0.9746 - val_loss: 0.2269 - val_accuracy: 0.9591 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8879354e-02 -1.7407566e-02 3.0901851e-03] Sparsity at: 0.0019045830202854997 Epoch 45/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1690 - accuracy: 0.9745 - val_loss: 0.2355 - val_accuracy: 0.9560 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7023574e-02 -2.0252693e-02 -2.3534123e-03] Sparsity at: 0.0019045830202854997 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1614 - accuracy: 0.9767 - val_loss: 0.1940 - val_accuracy: 0.9679 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7376150e-02 -2.0953940e-02 4.7816788e-03] Sparsity at: 0.0019045830202854997 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1617 - accuracy: 0.9762 - val_loss: 0.2295 - val_accuracy: 0.9561 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.0621266e-02 -2.4832113e-02 -4.2614494e-03] Sparsity at: 0.0019045830202854997 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1615 - accuracy: 0.9760 - val_loss: 0.2019 - val_accuracy: 0.9649 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1797068e-02 -1.8224919e-02 5.6891359e-04] Sparsity at: 0.0019045830202854997 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9762 - val_loss: 0.2312 - val_accuracy: 0.9562 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5983011e-02 -1.4551427e-02 -8.1321830e-03] Sparsity at: 0.0019045830202854997 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1627 - accuracy: 0.9760 - val_loss: 0.2069 - val_accuracy: 0.9636 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3961698e-02 -1.6124545e-02 1.4248197e-03] Sparsity at: 0.0019045830202854997 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 4.1276947719963544e-34 Thresholhold -2.560368352343766e-34 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 6.903632221386818e-05 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.018930538429348776 Thresholhold 0.04323918744921684 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.646 tf.Tensor( [[1. 1. 1. 0. 1. 0. 1. 0. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 1. 0. 1. 0. 1. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [1. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 1.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 1. 1. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 1. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 1. 0. 1. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 1. 1. 1. 1. 0. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 1. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 1. 0. 1. 1. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 152s 12ms/step - loss: 0.1639 - accuracy: 0.9750 - val_loss: 0.2418 - val_accuracy: 0.9521 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2285918e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1601 - accuracy: 0.9759 - val_loss: 0.2111 - val_accuracy: 0.9612 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5524461e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1607 - accuracy: 0.9760 - val_loss: 0.2381 - val_accuracy: 0.9534 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4108847e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1576 - accuracy: 0.9766 - val_loss: 0.2205 - val_accuracy: 0.9566 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3357915e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1599 - accuracy: 0.9760 - val_loss: 0.2495 - val_accuracy: 0.9507 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4207929e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1588 - accuracy: 0.9764 - val_loss: 0.2187 - val_accuracy: 0.9592 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8596791e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1643 - accuracy: 0.9749 - val_loss: 0.2103 - val_accuracy: 0.9628 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4256238e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1632 - accuracy: 0.9754 - val_loss: 0.2422 - val_accuracy: 0.9532 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5802297e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1600 - accuracy: 0.9757 - val_loss: 0.2728 - val_accuracy: 0.9426 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7660163e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1583 - accuracy: 0.9770 - val_loss: 0.2436 - val_accuracy: 0.9497 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3370934e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1565 - accuracy: 0.9765 - val_loss: 0.2169 - val_accuracy: 0.9595 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9823163e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1585 - accuracy: 0.9764 - val_loss: 0.2324 - val_accuracy: 0.9541 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8403306e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1588 - accuracy: 0.9761 - val_loss: 0.1999 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6232363e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1592 - accuracy: 0.9758 - val_loss: 0.2122 - val_accuracy: 0.9599 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9594892e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1584 - accuracy: 0.9762 - val_loss: 0.2291 - val_accuracy: 0.9571 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.2852249e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1551 - accuracy: 0.9760 - val_loss: 0.3223 - val_accuracy: 0.9341 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8722508e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1611 - accuracy: 0.9762 - val_loss: 0.2204 - val_accuracy: 0.9603 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5154071e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1551 - accuracy: 0.9768 - val_loss: 0.2128 - val_accuracy: 0.9621 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2491148e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1532 - accuracy: 0.9769 - val_loss: 0.2448 - val_accuracy: 0.9500 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7739255e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1544 - accuracy: 0.9771 - val_loss: 0.2481 - val_accuracy: 0.9484 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6205281e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1594 - accuracy: 0.9754 - val_loss: 0.2512 - val_accuracy: 0.9489 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.6063837e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1570 - accuracy: 0.9761 - val_loss: 0.2326 - val_accuracy: 0.9535 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8455185e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1558 - accuracy: 0.9764 - val_loss: 0.2211 - val_accuracy: 0.9577 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5762023e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 74/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1555 - accuracy: 0.9760 - val_loss: 0.2210 - val_accuracy: 0.9569 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8119309e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1594 - accuracy: 0.9750 - val_loss: 0.2384 - val_accuracy: 0.9513 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8524879e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1540 - accuracy: 0.9770 - val_loss: 0.2414 - val_accuracy: 0.9506 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4880884e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1583 - accuracy: 0.9746 - val_loss: 0.2567 - val_accuracy: 0.9464 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0576400e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1505 - accuracy: 0.9772 - val_loss: 0.2079 - val_accuracy: 0.9613 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8642399e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1557 - accuracy: 0.9762 - val_loss: 0.2514 - val_accuracy: 0.9473 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1079827e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1553 - accuracy: 0.9764 - val_loss: 0.2307 - val_accuracy: 0.9537 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7432704e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1512 - accuracy: 0.9770 - val_loss: 0.2104 - val_accuracy: 0.9607 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3468433e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1539 - accuracy: 0.9763 - val_loss: 0.2120 - val_accuracy: 0.9594 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3807237e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1522 - accuracy: 0.9771 - val_loss: 0.2369 - val_accuracy: 0.9487 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8086157e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9766 - val_loss: 0.2111 - val_accuracy: 0.9606 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3310918e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1530 - accuracy: 0.9773 - val_loss: 0.2162 - val_accuracy: 0.9583 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7753489e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1523 - accuracy: 0.9776 - val_loss: 0.2796 - val_accuracy: 0.9385 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0264689e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1540 - accuracy: 0.9766 - val_loss: 0.2305 - val_accuracy: 0.9524 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5169318e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1520 - accuracy: 0.9766 - val_loss: 0.2124 - val_accuracy: 0.9604 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4791123e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9777 - val_loss: 0.2332 - val_accuracy: 0.9510 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7860077e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 90/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1513 - accuracy: 0.9775 - val_loss: 0.2745 - val_accuracy: 0.9423 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6763523e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1549 - accuracy: 0.9760 - val_loss: 0.2417 - val_accuracy: 0.9493 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6572596e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1523 - accuracy: 0.9769 - val_loss: 0.2319 - val_accuracy: 0.9526 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9733367e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1542 - accuracy: 0.9763 - val_loss: 0.2368 - val_accuracy: 0.9521 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6787398e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1548 - accuracy: 0.9758 - val_loss: 0.2631 - val_accuracy: 0.9456 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5368683e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1538 - accuracy: 0.9766 - val_loss: 0.2074 - val_accuracy: 0.9615 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0767329e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1510 - accuracy: 0.9770 - val_loss: 0.2391 - val_accuracy: 0.9502 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8566016e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1477 - accuracy: 0.9783 - val_loss: 0.2190 - val_accuracy: 0.9539 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0767149e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1514 - accuracy: 0.9765 - val_loss: 0.2257 - val_accuracy: 0.9557 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4315633e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 99/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9756 - val_loss: 0.2319 - val_accuracy: 0.9534 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5027970e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1513 - accuracy: 0.9769 - val_loss: 0.1994 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3564549e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0024530428249436515 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 4.926172928679925e-34 Thresholhold -2.560368352343766e-34 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.00025495871407268944 Thresholhold -9.990911848944961e-07 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.0264767769331149 Thresholhold 0.043087732046842575 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 147s 12ms/step - loss: 0.1474 - accuracy: 0.9779 - val_loss: 0.2269 - val_accuracy: 0.9548 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7340872e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1516 - accuracy: 0.9770 - val_loss: 0.2108 - val_accuracy: 0.9590 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8165491e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1478 - accuracy: 0.9780 - val_loss: 0.2220 - val_accuracy: 0.9544 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7605828e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9767 - val_loss: 0.2846 - val_accuracy: 0.9380 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1117983e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1499 - accuracy: 0.9768 - val_loss: 0.2269 - val_accuracy: 0.9546 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5094514e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1511 - accuracy: 0.9766 - val_loss: 0.2222 - val_accuracy: 0.9569 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7371099e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 107/500 235/235 [==============================] - 3s 12ms/step - loss: 0.1516 - accuracy: 0.9759 - val_loss: 0.2333 - val_accuracy: 0.9532 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7972121e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1479 - accuracy: 0.9773 - val_loss: 0.2288 - val_accuracy: 0.9540 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1929836e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 109/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1493 - accuracy: 0.9769 - val_loss: 0.2341 - val_accuracy: 0.9534 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5879184e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1516 - accuracy: 0.9768 - val_loss: 0.2011 - val_accuracy: 0.9625 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8913232e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9779 - val_loss: 0.2257 - val_accuracy: 0.9559 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8708929e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 112/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1495 - accuracy: 0.9767 - val_loss: 0.2013 - val_accuracy: 0.9608 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1846374e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.002877535687453043 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1487 - accuracy: 0.9779 - val_loss: 0.2094 - val_accuracy: 0.9602 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3272289e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1489 - accuracy: 0.9766 - val_loss: 0.2233 - val_accuracy: 0.9558 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4960888e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9764 - val_loss: 0.2487 - val_accuracy: 0.9475 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3894960e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 116/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1460 - accuracy: 0.9781 - val_loss: 0.2171 - val_accuracy: 0.9567 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6326598e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9773 - val_loss: 0.2103 - val_accuracy: 0.9584 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6206286e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1488 - accuracy: 0.9772 - val_loss: 0.2459 - val_accuracy: 0.9495 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7402652e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1467 - accuracy: 0.9776 - val_loss: 0.2164 - val_accuracy: 0.9603 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4788672e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1460 - accuracy: 0.9777 - val_loss: 0.2075 - val_accuracy: 0.9603 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1660549e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9774 - val_loss: 0.2362 - val_accuracy: 0.9514 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3427127e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1480 - accuracy: 0.9770 - val_loss: 0.2242 - val_accuracy: 0.9557 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3067230e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9779 - val_loss: 0.1938 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7639778e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1478 - accuracy: 0.9771 - val_loss: 0.2203 - val_accuracy: 0.9571 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.6403728e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9787 - val_loss: 0.2200 - val_accuracy: 0.9575 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9369605e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9775 - val_loss: 0.2159 - val_accuracy: 0.9580 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1304657e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1441 - accuracy: 0.9782 - val_loss: 0.2047 - val_accuracy: 0.9620 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1577565e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1486 - accuracy: 0.9767 - val_loss: 0.2132 - val_accuracy: 0.9591 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0260121e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1447 - accuracy: 0.9780 - val_loss: 0.2373 - val_accuracy: 0.9509 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1072462e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9782 - val_loss: 0.2105 - val_accuracy: 0.9593 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0291023e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1448 - accuracy: 0.9775 - val_loss: 0.2108 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9057268e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9770 - val_loss: 0.1934 - val_accuracy: 0.9652 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8031303e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1449 - accuracy: 0.9781 - val_loss: 0.2064 - val_accuracy: 0.9614 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8111161e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1488 - accuracy: 0.9773 - val_loss: 0.2267 - val_accuracy: 0.9557 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3993524e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1451 - accuracy: 0.9778 - val_loss: 0.1995 - val_accuracy: 0.9629 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0957274e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1480 - accuracy: 0.9768 - val_loss: 0.1840 - val_accuracy: 0.9678 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5981564e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1452 - accuracy: 0.9775 - val_loss: 0.2049 - val_accuracy: 0.9618 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5725011e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1465 - accuracy: 0.9778 - val_loss: 0.2254 - val_accuracy: 0.9546 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6130387e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9783 - val_loss: 0.2153 - val_accuracy: 0.9561 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6172237e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9780 - val_loss: 0.1862 - val_accuracy: 0.9660 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3258684e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1461 - accuracy: 0.9772 - val_loss: 0.2048 - val_accuracy: 0.9608 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0771313e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9773 - val_loss: 0.1780 - val_accuracy: 0.9699 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9594475e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9789 - val_loss: 0.2126 - val_accuracy: 0.9599 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2371365e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1472 - accuracy: 0.9772 - val_loss: 0.2026 - val_accuracy: 0.9607 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5736921e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9780 - val_loss: 0.2110 - val_accuracy: 0.9594 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5024628e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1462 - accuracy: 0.9772 - val_loss: 0.2037 - val_accuracy: 0.9607 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7963957e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9784 - val_loss: 0.2119 - val_accuracy: 0.9601 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4010366e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9772 - val_loss: 0.2077 - val_accuracy: 0.9614 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8241154e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9784 - val_loss: 0.1986 - val_accuracy: 0.9625 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0256540e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9778 - val_loss: 0.1997 - val_accuracy: 0.9621 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6774714e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 5.619860892015827e-34 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.0018291454454020459 Thresholhold -0.001795717398636043 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.03759050469673397 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 251s 12ms/step - loss: 0.1432 - accuracy: 0.9777 - val_loss: 0.1988 - val_accuracy: 0.9614 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4348849e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 152/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1441 - accuracy: 0.9777 - val_loss: 0.2182 - val_accuracy: 0.9566 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5921263e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9780 - val_loss: 0.2045 - val_accuracy: 0.9600 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8064080e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1457 - accuracy: 0.9766 - val_loss: 0.2101 - val_accuracy: 0.9581 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9602148e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9772 - val_loss: 0.2207 - val_accuracy: 0.9565 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6086163e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9786 - val_loss: 0.2355 - val_accuracy: 0.9555 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4522109e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1461 - accuracy: 0.9768 - val_loss: 0.1981 - val_accuracy: 0.9643 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1235401e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1469 - accuracy: 0.9770 - val_loss: 0.1932 - val_accuracy: 0.9638 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4198027e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1427 - accuracy: 0.9780 - val_loss: 0.2035 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8868539e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1450 - accuracy: 0.9773 - val_loss: 0.2101 - val_accuracy: 0.9600 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3851944e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9776 - val_loss: 0.2023 - val_accuracy: 0.9643 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.3946560e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1433 - accuracy: 0.9776 - val_loss: 0.2012 - val_accuracy: 0.9590 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1850869e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9779 - val_loss: 0.1866 - val_accuracy: 0.9664 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8175266e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9782 - val_loss: 0.2507 - val_accuracy: 0.9490 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9655440e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9779 - val_loss: 0.2046 - val_accuracy: 0.9616 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8017098e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1446 - accuracy: 0.9777 - val_loss: 0.2014 - val_accuracy: 0.9619 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5049448e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9788 - val_loss: 0.2097 - val_accuracy: 0.9594 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4673475e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9778 - val_loss: 0.2164 - val_accuracy: 0.9592 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3729620e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9780 - val_loss: 0.2034 - val_accuracy: 0.9610 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7897200e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9779 - val_loss: 0.2133 - val_accuracy: 0.9583 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7827686e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1433 - accuracy: 0.9779 - val_loss: 0.2072 - val_accuracy: 0.9618 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0117247e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9772 - val_loss: 0.2201 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6057811e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9778 - val_loss: 0.2276 - val_accuracy: 0.9539 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5762463e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9782 - val_loss: 0.1962 - val_accuracy: 0.9630 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8154655e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9781 - val_loss: 0.2007 - val_accuracy: 0.9610 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0377440e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9767 - val_loss: 0.2197 - val_accuracy: 0.9547 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7622933e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9786 - val_loss: 0.2222 - val_accuracy: 0.9567 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2563994e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9782 - val_loss: 0.2116 - val_accuracy: 0.9605 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8479757e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1477 - accuracy: 0.9764 - val_loss: 0.2039 - val_accuracy: 0.9611 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8755266e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9782 - val_loss: 0.2174 - val_accuracy: 0.9566 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8379736e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9776 - val_loss: 0.2027 - val_accuracy: 0.9621 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9237309e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9788 - val_loss: 0.2314 - val_accuracy: 0.9550 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8424794e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1432 - accuracy: 0.9776 - val_loss: 0.1894 - val_accuracy: 0.9650 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9047945e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9779 - val_loss: 0.2096 - val_accuracy: 0.9579 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8282625e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1469 - accuracy: 0.9770 - val_loss: 0.2093 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4128387e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1425 - accuracy: 0.9785 - val_loss: 0.2018 - val_accuracy: 0.9641 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5119910e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9774 - val_loss: 0.2186 - val_accuracy: 0.9589 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0939251e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9780 - val_loss: 0.1902 - val_accuracy: 0.9636 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9516870e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9783 - val_loss: 0.2309 - val_accuracy: 0.9532 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4989741e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 190/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1425 - accuracy: 0.9788 - val_loss: 0.2091 - val_accuracy: 0.9595 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2596156e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1419 - accuracy: 0.9782 - val_loss: 0.1975 - val_accuracy: 0.9654 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7473664e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9782 - val_loss: 0.2128 - val_accuracy: 0.9594 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.3646470e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9783 - val_loss: 0.2309 - val_accuracy: 0.9560 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7718984e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1444 - accuracy: 0.9779 - val_loss: 0.1888 - val_accuracy: 0.9662 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.3404366e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1434 - accuracy: 0.9782 - val_loss: 0.2126 - val_accuracy: 0.9581 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5420225e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9782 - val_loss: 0.2172 - val_accuracy: 0.9587 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -9.7393952e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9780 - val_loss: 0.1922 - val_accuracy: 0.9644 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0521255e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1454 - accuracy: 0.9772 - val_loss: 0.2177 - val_accuracy: 0.9582 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4909328e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9776 - val_loss: 0.2097 - val_accuracy: 0.9609 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.1881821e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.2083 - val_accuracy: 0.9597 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7777746e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.0028812922614575506 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.0006630604865468515 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.0027292768771942644 Thresholhold 4.479024210013449e-05 Using suggest threshold. Applying new mask Percentage zeros 0.6913667 tf.Tensor( [[1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.06058478864886663 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 283s 12ms/step - loss: 0.1400 - accuracy: 0.9785 - val_loss: 0.2091 - val_accuracy: 0.9584 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0042248e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 202/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9782 - val_loss: 0.2333 - val_accuracy: 0.9531 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8820280e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9778 - val_loss: 0.1991 - val_accuracy: 0.9639 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8870313e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9791 - val_loss: 0.2044 - val_accuracy: 0.9632 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8297318e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1460 - accuracy: 0.9769 - val_loss: 0.2196 - val_accuracy: 0.9584 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -9.9843424e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9784 - val_loss: 0.2234 - val_accuracy: 0.9579 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2245385e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1441 - accuracy: 0.9771 - val_loss: 0.2244 - val_accuracy: 0.9567 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -6.1300495e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9771 - val_loss: 0.1876 - val_accuracy: 0.9659 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4289498e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9778 - val_loss: 0.1960 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3784091e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1419 - accuracy: 0.9783 - val_loss: 0.1930 - val_accuracy: 0.9642 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4537725e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9786 - val_loss: 0.1889 - val_accuracy: 0.9637 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5739435e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9790 - val_loss: 0.2110 - val_accuracy: 0.9589 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2721687e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1436 - accuracy: 0.9775 - val_loss: 0.2224 - val_accuracy: 0.9552 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9474670e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9759 - val_loss: 0.2044 - val_accuracy: 0.9613 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0428546e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9780 - val_loss: 0.1994 - val_accuracy: 0.9624 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5611430e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9775 - val_loss: 0.2110 - val_accuracy: 0.9581 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0070813e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.2153 - val_accuracy: 0.9559 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6013933e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1451 - accuracy: 0.9762 - val_loss: 0.1989 - val_accuracy: 0.9611 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.1088681e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9780 - val_loss: 0.2091 - val_accuracy: 0.9574 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7631242e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9774 - val_loss: 0.1997 - val_accuracy: 0.9605 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3923267e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9789 - val_loss: 0.2161 - val_accuracy: 0.9569 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9308029e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9778 - val_loss: 0.2388 - val_accuracy: 0.9505 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5355762e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9771 - val_loss: 0.1975 - val_accuracy: 0.9633 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4227325e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9774 - val_loss: 0.1874 - val_accuracy: 0.9650 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0333963e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9780 - val_loss: 0.2132 - val_accuracy: 0.9571 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3218682e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9779 - val_loss: 0.2008 - val_accuracy: 0.9616 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6272334e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9792 - val_loss: 0.2054 - val_accuracy: 0.9590 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5948100e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1427 - accuracy: 0.9774 - val_loss: 0.1971 - val_accuracy: 0.9617 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5657643e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9791 - val_loss: 0.2009 - val_accuracy: 0.9609 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8899333e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9779 - val_loss: 0.2064 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2386646e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9780 - val_loss: 0.2467 - val_accuracy: 0.9508 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9658471e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9785 - val_loss: 0.2043 - val_accuracy: 0.9597 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -7.8463554e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9783 - val_loss: 0.2288 - val_accuracy: 0.9550 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8861985e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9784 - val_loss: 0.1950 - val_accuracy: 0.9636 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9484596e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9772 - val_loss: 0.2445 - val_accuracy: 0.9509 [-2.56036835e-34 -4.89440904e-34 2.81328974e-34 ... -1.29702175e-02 0.00000000e+00 -0.00000000e+00] Sparsity at: 0.08079263711495116 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9780 - val_loss: 0.2279 - val_accuracy: 0.9557 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5008717e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9786 - val_loss: 0.2311 - val_accuracy: 0.9539 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4113965e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1416 - accuracy: 0.9774 - val_loss: 0.2066 - val_accuracy: 0.9596 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0237274e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9786 - val_loss: 0.2141 - val_accuracy: 0.9589 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.3684629e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9776 - val_loss: 0.2297 - val_accuracy: 0.9529 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4847353e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9772 - val_loss: 0.2199 - val_accuracy: 0.9542 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5585964e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9783 - val_loss: 0.1988 - val_accuracy: 0.9615 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6001774e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9781 - val_loss: 0.1935 - val_accuracy: 0.9604 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8708110e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9784 - val_loss: 0.2526 - val_accuracy: 0.9463 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5572954e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9775 - val_loss: 0.2234 - val_accuracy: 0.9554 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3900634e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1446 - accuracy: 0.9765 - val_loss: 0.1747 - val_accuracy: 0.9709 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -8.7204371e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9790 - val_loss: 0.2070 - val_accuracy: 0.9587 [-2.56036835e-34 -4.89440904e-34 2.81328974e-34 ... -1.15005085e-02 -0.00000000e+00 -0.00000000e+00] Sparsity at: 0.08079263711495116 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9776 - val_loss: 0.1859 - val_accuracy: 0.9673 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.1117669e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.2471 - val_accuracy: 0.9501 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4919719e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9786 - val_loss: 0.2186 - val_accuracy: 0.9566 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4372821e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.0055774101495831285 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.008124002356425186 Thresholhold -0.00010690836643334478 Using suggest threshold. Applying new mask Percentage zeros 0.6913667 tf.Tensor( [[1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.07689945912113849 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 246s 12ms/step - loss: 0.1371 - accuracy: 0.9791 - val_loss: 0.1988 - val_accuracy: 0.9613 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9926574e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9770 - val_loss: 0.2026 - val_accuracy: 0.9603 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9850316e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9780 - val_loss: 0.2301 - val_accuracy: 0.9538 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9919008e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9783 - val_loss: 0.2038 - val_accuracy: 0.9600 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0868331e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1448 - accuracy: 0.9766 - val_loss: 0.2055 - val_accuracy: 0.9601 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0792633e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9771 - val_loss: 0.2018 - val_accuracy: 0.9598 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0981556e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9778 - val_loss: 0.2395 - val_accuracy: 0.9499 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9541247e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9779 - val_loss: 0.1942 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4323111e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9778 - val_loss: 0.2075 - val_accuracy: 0.9595 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.1474449e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9782 - val_loss: 0.2324 - val_accuracy: 0.9508 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9734265e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9785 - val_loss: 0.2277 - val_accuracy: 0.9532 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5383526e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2512 - val_accuracy: 0.9447 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4590986e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9778 - val_loss: 0.1965 - val_accuracy: 0.9616 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0154109e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9773 - val_loss: 0.2365 - val_accuracy: 0.9491 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.1166678e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9770 - val_loss: 0.1840 - val_accuracy: 0.9667 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5040148e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1408 - accuracy: 0.9773 - val_loss: 0.2208 - val_accuracy: 0.9553 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2368008e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1426 - accuracy: 0.9767 - val_loss: 0.2025 - val_accuracy: 0.9592 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4630641e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9789 - val_loss: 0.2234 - val_accuracy: 0.9539 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2025933e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9782 - val_loss: 0.2465 - val_accuracy: 0.9487 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0954208e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9777 - val_loss: 0.2061 - val_accuracy: 0.9595 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8633414e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9784 - val_loss: 0.1934 - val_accuracy: 0.9643 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8346190e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9779 - val_loss: 0.2181 - val_accuracy: 0.9593 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6156707e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9790 - val_loss: 0.1959 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5639837e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9775 - val_loss: 0.2361 - val_accuracy: 0.9540 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5129525e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.2401 - val_accuracy: 0.9492 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6113627e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9783 - val_loss: 0.2134 - val_accuracy: 0.9584 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5714562e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9784 - val_loss: 0.2387 - val_accuracy: 0.9511 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.0575075e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9788 - val_loss: 0.2352 - val_accuracy: 0.9513 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.1532914e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9775 - val_loss: 0.2064 - val_accuracy: 0.9608 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3198163e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.1887 - val_accuracy: 0.9638 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6746594e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9780 - val_loss: 0.2185 - val_accuracy: 0.9573 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4425127e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9788 - val_loss: 0.2277 - val_accuracy: 0.9544 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1741086e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9775 - val_loss: 0.2080 - val_accuracy: 0.9591 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6630126e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.1900 - val_accuracy: 0.9651 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1140881e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2180 - val_accuracy: 0.9556 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1135535e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9798 - val_loss: 0.2132 - val_accuracy: 0.9544 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9423997e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9778 - val_loss: 0.1876 - val_accuracy: 0.9654 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2583512e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9778 - val_loss: 0.1880 - val_accuracy: 0.9656 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5495255e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9788 - val_loss: 0.2170 - val_accuracy: 0.9539 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9497972e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9782 - val_loss: 0.2186 - val_accuracy: 0.9563 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8728999e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9782 - val_loss: 0.1995 - val_accuracy: 0.9610 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9619714e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9772 - val_loss: 0.2231 - val_accuracy: 0.9552 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0126905e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.2200 - val_accuracy: 0.9576 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3369575e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9786 - val_loss: 0.2073 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5960376e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9784 - val_loss: 0.1943 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5018768e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9788 - val_loss: 0.2449 - val_accuracy: 0.9489 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4718892e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1407 - accuracy: 0.9770 - val_loss: 0.2190 - val_accuracy: 0.9555 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9997351e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.2061 - val_accuracy: 0.9608 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6326047e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9779 - val_loss: 0.2297 - val_accuracy: 0.9525 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3641780e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9782 - val_loss: 0.2174 - val_accuracy: 0.9585 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0602215e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.011956146164944559 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.02037388842071186 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6913667 tf.Tensor( [[1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.08851680539852769 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 242s 12ms/step - loss: 0.1399 - accuracy: 0.9776 - val_loss: 0.1869 - val_accuracy: 0.9649 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3109054e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9779 - val_loss: 0.2134 - val_accuracy: 0.9575 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8581019e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9793 - val_loss: 0.1974 - val_accuracy: 0.9620 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2463856e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9787 - val_loss: 0.2645 - val_accuracy: 0.9426 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8818538e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9782 - val_loss: 0.2470 - val_accuracy: 0.9475 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4545429e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9777 - val_loss: 0.2257 - val_accuracy: 0.9550 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1966511e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9772 - val_loss: 0.2315 - val_accuracy: 0.9555 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2895826e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9795 - val_loss: 0.1975 - val_accuracy: 0.9617 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8791126e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9770 - val_loss: 0.2447 - val_accuracy: 0.9516 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5958786e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9784 - val_loss: 0.2152 - val_accuracy: 0.9579 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5610352e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9784 - val_loss: 0.1860 - val_accuracy: 0.9661 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2951361e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9788 - val_loss: 0.2032 - val_accuracy: 0.9618 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7323251e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9782 - val_loss: 0.1876 - val_accuracy: 0.9667 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7108749e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9777 - val_loss: 0.2028 - val_accuracy: 0.9609 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5774030e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9781 - val_loss: 0.2094 - val_accuracy: 0.9573 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.8658585e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9782 - val_loss: 0.2007 - val_accuracy: 0.9611 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7571140e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9782 - val_loss: 0.2196 - val_accuracy: 0.9554 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7734468e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9782 - val_loss: 0.2539 - val_accuracy: 0.9466 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6704507e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9781 - val_loss: 0.1995 - val_accuracy: 0.9642 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.0943608e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9785 - val_loss: 0.2130 - val_accuracy: 0.9578 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4880321e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9781 - val_loss: 0.1809 - val_accuracy: 0.9661 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9023029e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9781 - val_loss: 0.2028 - val_accuracy: 0.9620 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3633858e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9783 - val_loss: 0.2084 - val_accuracy: 0.9589 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6815133e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9780 - val_loss: 0.2016 - val_accuracy: 0.9615 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4838930e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9782 - val_loss: 0.2225 - val_accuracy: 0.9550 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0898102e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9782 - val_loss: 0.1913 - val_accuracy: 0.9647 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.0584430e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9784 - val_loss: 0.2144 - val_accuracy: 0.9591 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7760984e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9798 - val_loss: 0.1966 - val_accuracy: 0.9618 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3543628e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9788 - val_loss: 0.2242 - val_accuracy: 0.9551 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.3523157e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9778 - val_loss: 0.2127 - val_accuracy: 0.9591 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4794744e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.2049 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7919387e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9785 - val_loss: 0.1929 - val_accuracy: 0.9634 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8479028e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2077 - val_accuracy: 0.9596 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4599774e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9776 - val_loss: 0.1962 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9399632e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9800 - val_loss: 0.2228 - val_accuracy: 0.9559 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7051238e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9772 - val_loss: 0.1976 - val_accuracy: 0.9615 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.2344315e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9788 - val_loss: 0.2115 - val_accuracy: 0.9588 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9027084e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9791 - val_loss: 0.2511 - val_accuracy: 0.9489 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5207154e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9785 - val_loss: 0.1916 - val_accuracy: 0.9638 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9786127e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9774 - val_loss: 0.2080 - val_accuracy: 0.9607 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5547180e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9780 - val_loss: 0.2018 - val_accuracy: 0.9621 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5384245e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9791 - val_loss: 0.1923 - val_accuracy: 0.9635 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2875739e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9787 - val_loss: 0.2494 - val_accuracy: 0.9540 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5110142e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9770 - val_loss: 0.2206 - val_accuracy: 0.9574 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9313115e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9772 - val_loss: 0.2196 - val_accuracy: 0.9564 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5039060e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9769 - val_loss: 0.1874 - val_accuracy: 0.9659 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7603485e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9777 - val_loss: 0.1926 - val_accuracy: 0.9642 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2133021e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 348/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9781 - val_loss: 0.2242 - val_accuracy: 0.9563 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4914635e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 349/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1384 - accuracy: 0.9778 - val_loss: 0.2285 - val_accuracy: 0.9553 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1395856e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 350/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1340 - accuracy: 0.9794 - val_loss: 0.1906 - val_accuracy: 0.9651 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5276208e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.01804403414086142 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.0315618626416323 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6913667 tf.Tensor( [[1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.09637593709484982 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 242s 12ms/step - loss: 0.1374 - accuracy: 0.9779 - val_loss: 0.1978 - val_accuracy: 0.9622 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1191578e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 352/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1354 - accuracy: 0.9783 - val_loss: 0.2059 - val_accuracy: 0.9590 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9156124e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9790 - val_loss: 0.2269 - val_accuracy: 0.9538 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1038186e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9782 - val_loss: 0.2011 - val_accuracy: 0.9625 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9574526e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9784 - val_loss: 0.2029 - val_accuracy: 0.9594 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3136616e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9776 - val_loss: 0.2138 - val_accuracy: 0.9589 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4881342e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9782 - val_loss: 0.1972 - val_accuracy: 0.9625 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4045909e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9785 - val_loss: 0.2229 - val_accuracy: 0.9556 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5729144e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9778 - val_loss: 0.1805 - val_accuracy: 0.9657 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9612677e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9777 - val_loss: 0.2233 - val_accuracy: 0.9552 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7865147e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.2020 - val_accuracy: 0.9614 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7020829e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1841 - val_accuracy: 0.9679 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5996443e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9780 - val_loss: 0.2206 - val_accuracy: 0.9565 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9774617e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9786 - val_loss: 0.2508 - val_accuracy: 0.9461 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4179785e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9789 - val_loss: 0.2080 - val_accuracy: 0.9587 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2145350e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9779 - val_loss: 0.2213 - val_accuracy: 0.9558 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1028211e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 367/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1359 - accuracy: 0.9780 - val_loss: 0.2276 - val_accuracy: 0.9511 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.5201207e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9791 - val_loss: 0.1959 - val_accuracy: 0.9618 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6543187e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9777 - val_loss: 0.1854 - val_accuracy: 0.9642 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1356763e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9785 - val_loss: 0.2127 - val_accuracy: 0.9583 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7537996e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9780 - val_loss: 0.1914 - val_accuracy: 0.9629 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6804922e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9792 - val_loss: 0.3236 - val_accuracy: 0.9280 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.7541758e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9771 - val_loss: 0.1952 - val_accuracy: 0.9615 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.9260179e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9777 - val_loss: 0.2032 - val_accuracy: 0.9604 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6771592e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 375/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9789 - val_loss: 0.2036 - val_accuracy: 0.9651 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -4.3876678e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9795 - val_loss: 0.2113 - val_accuracy: 0.9607 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4705311e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9783 - val_loss: 0.2050 - val_accuracy: 0.9604 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.6289532e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9792 - val_loss: 0.2243 - val_accuracy: 0.9522 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4004416e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9775 - val_loss: 0.1964 - val_accuracy: 0.9627 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6782263e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9794 - val_loss: 0.2277 - val_accuracy: 0.9544 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7520023e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9772 - val_loss: 0.2313 - val_accuracy: 0.9525 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6205799e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9786 - val_loss: 0.2011 - val_accuracy: 0.9614 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0559093e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9776 - val_loss: 0.2230 - val_accuracy: 0.9566 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8286600e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9783 - val_loss: 0.1862 - val_accuracy: 0.9642 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4270076e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9783 - val_loss: 0.1959 - val_accuracy: 0.9605 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0172316e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9788 - val_loss: 0.2137 - val_accuracy: 0.9573 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7580922e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9780 - val_loss: 0.2223 - val_accuracy: 0.9537 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7315075e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 388/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9782 - val_loss: 0.2121 - val_accuracy: 0.9578 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2412516e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9782 - val_loss: 0.2060 - val_accuracy: 0.9619 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.1700323e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9786 - val_loss: 0.1936 - val_accuracy: 0.9633 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3233935e-02 0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9797 - val_loss: 0.1966 - val_accuracy: 0.9623 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9340305e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9779 - val_loss: 0.1950 - val_accuracy: 0.9620 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0886743e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9788 - val_loss: 0.2027 - val_accuracy: 0.9600 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.9419363e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9785 - val_loss: 0.1886 - val_accuracy: 0.9635 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.4742918e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.2038 - val_accuracy: 0.9621 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1377390e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9786 - val_loss: 0.2240 - val_accuracy: 0.9551 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1118246e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9794 - val_loss: 0.2129 - val_accuracy: 0.9597 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7808692e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9782 - val_loss: 0.2369 - val_accuracy: 0.9519 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.0900404e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9793 - val_loss: 0.1914 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5699098e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9772 - val_loss: 0.2032 - val_accuracy: 0.9601 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2026522e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.02167989121451175 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.038286437850723054 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6913667 tf.Tensor( [[1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.10055417756025697 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 1. 1. 0. 0. 0. 0. 0. 1. 1.] [0. 0. 1. 1. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 1. 1. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 0. 1. 1. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 1. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 1. 0.] [0. 0. 1. 1. 0. 0. 0. 0. 0. 0.] [1. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 1. 0. 1. 1. 0. 1. 0.] [0. 0. 1. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 1.] [0. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 1. 1. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 1. 0. 1. 0. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 254s 12ms/step - loss: 0.1353 - accuracy: 0.9778 - val_loss: 0.2402 - val_accuracy: 0.9516 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2561865e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 402/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1349 - accuracy: 0.9785 - val_loss: 0.2144 - val_accuracy: 0.9575 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5631556e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9790 - val_loss: 0.2159 - val_accuracy: 0.9578 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0545743e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9784 - val_loss: 0.1870 - val_accuracy: 0.9648 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.1902237e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9782 - val_loss: 0.1930 - val_accuracy: 0.9646 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4474531e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9787 - val_loss: 0.1940 - val_accuracy: 0.9610 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9076265e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9789 - val_loss: 0.1852 - val_accuracy: 0.9669 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6136156e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.2041 - val_accuracy: 0.9617 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6337961e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9787 - val_loss: 0.1798 - val_accuracy: 0.9671 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4258608e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9794 - val_loss: 0.1905 - val_accuracy: 0.9644 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7521985e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9785 - val_loss: 0.1829 - val_accuracy: 0.9668 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7858838e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9780 - val_loss: 0.1941 - val_accuracy: 0.9626 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6426958e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9777 - val_loss: 0.2133 - val_accuracy: 0.9562 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6062124e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9795 - val_loss: 0.2031 - val_accuracy: 0.9602 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7624266e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9779 - val_loss: 0.1851 - val_accuracy: 0.9664 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6296059e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9779 - val_loss: 0.1891 - val_accuracy: 0.9641 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5451455e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9784 - val_loss: 0.2133 - val_accuracy: 0.9586 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3117118e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9792 - val_loss: 0.2011 - val_accuracy: 0.9595 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5754463e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9778 - val_loss: 0.1866 - val_accuracy: 0.9640 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0274285e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9783 - val_loss: 0.1712 - val_accuracy: 0.9696 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3650607e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9797 - val_loss: 0.1923 - val_accuracy: 0.9636 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6112022e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9784 - val_loss: 0.1794 - val_accuracy: 0.9650 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.7580397e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9783 - val_loss: 0.1758 - val_accuracy: 0.9680 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6160561e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.1786 - val_accuracy: 0.9685 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0713614e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9786 - val_loss: 0.1941 - val_accuracy: 0.9642 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7155344e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9778 - val_loss: 0.1873 - val_accuracy: 0.9634 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.2580068e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9784 - val_loss: 0.1944 - val_accuracy: 0.9624 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6652159e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9798 - val_loss: 0.2037 - val_accuracy: 0.9601 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -8.4677264e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9786 - val_loss: 0.2059 - val_accuracy: 0.9609 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.2019906e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9785 - val_loss: 0.2061 - val_accuracy: 0.9605 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.6731931e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1874 - val_accuracy: 0.9647 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2097006e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9789 - val_loss: 0.1854 - val_accuracy: 0.9653 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0952864e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9789 - val_loss: 0.2111 - val_accuracy: 0.9558 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0105541e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9786 - val_loss: 0.2171 - val_accuracy: 0.9564 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.9947758e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9786 - val_loss: 0.1891 - val_accuracy: 0.9650 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.4919789e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9788 - val_loss: 0.1973 - val_accuracy: 0.9610 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8312830e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9781 - val_loss: 0.1959 - val_accuracy: 0.9637 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5171225e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9789 - val_loss: 0.2489 - val_accuracy: 0.9477 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5629325e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9788 - val_loss: 0.1854 - val_accuracy: 0.9658 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7955912e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9783 - val_loss: 0.2011 - val_accuracy: 0.9615 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4945451e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9797 - val_loss: 0.1845 - val_accuracy: 0.9653 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5203296e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9784 - val_loss: 0.2036 - val_accuracy: 0.9611 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6438249e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9776 - val_loss: 0.2011 - val_accuracy: 0.9619 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4451780e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9783 - val_loss: 0.1796 - val_accuracy: 0.9652 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.3706082e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9782 - val_loss: 0.2072 - val_accuracy: 0.9608 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8338269e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9786 - val_loss: 0.1970 - val_accuracy: 0.9611 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6608400e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9790 - val_loss: 0.1746 - val_accuracy: 0.9690 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -8.2653482e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9777 - val_loss: 0.1752 - val_accuracy: 0.9699 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.2598254e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9789 - val_loss: 0.1783 - val_accuracy: 0.9669 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8088540e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9791 - val_loss: 0.1926 - val_accuracy: 0.9625 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5899008e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9779 - val_loss: 0.1965 - val_accuracy: 0.9619 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.5309747e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9784 - val_loss: 0.2001 - val_accuracy: 0.9608 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -3.1961724e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9786 - val_loss: 0.1856 - val_accuracy: 0.9656 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2286557e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9800 - val_loss: 0.1977 - val_accuracy: 0.9624 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0924710e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9790 - val_loss: 0.2048 - val_accuracy: 0.9613 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2166774e-02 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9786 - val_loss: 0.2011 - val_accuracy: 0.9622 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2945236e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9786 - val_loss: 0.2008 - val_accuracy: 0.9600 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.0608743e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9785 - val_loss: 0.2054 - val_accuracy: 0.9620 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5700335e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9773 - val_loss: 0.1971 - val_accuracy: 0.9629 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -8.8292509e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9783 - val_loss: 0.1698 - val_accuracy: 0.9689 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.2636602e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9789 - val_loss: 0.1854 - val_accuracy: 0.9653 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -5.7639237e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9791 - val_loss: 0.1976 - val_accuracy: 0.9626 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -8.7813744e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9783 - val_loss: 0.1985 - val_accuracy: 0.9626 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -9.9617587e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9783 - val_loss: 0.1820 - val_accuracy: 0.9675 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -7.8467187e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9801 - val_loss: 0.1751 - val_accuracy: 0.9673 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -9.2773698e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9793 - val_loss: 0.1912 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5330495e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9784 - val_loss: 0.2094 - val_accuracy: 0.9596 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0809384e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9787 - val_loss: 0.1750 - val_accuracy: 0.9687 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.0330668e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9786 - val_loss: 0.1946 - val_accuracy: 0.9643 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4218373e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9790 - val_loss: 0.1831 - val_accuracy: 0.9648 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.2071697e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9790 - val_loss: 0.2001 - val_accuracy: 0.9632 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.1375859e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9784 - val_loss: 0.1850 - val_accuracy: 0.9650 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4297594e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 473/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1342 - accuracy: 0.9783 - val_loss: 0.1902 - val_accuracy: 0.9649 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5889972e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9788 - val_loss: 0.1854 - val_accuracy: 0.9653 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -6.1205067e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9793 - val_loss: 0.2552 - val_accuracy: 0.9487 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -6.5993476e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9791 - val_loss: 0.1984 - val_accuracy: 0.9635 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -8.3087897e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9787 - val_loss: 0.2130 - val_accuracy: 0.9572 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -7.6934975e-03 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9784 - val_loss: 0.1914 - val_accuracy: 0.9643 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5138582e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9782 - val_loss: 0.2211 - val_accuracy: 0.9564 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5106151e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9797 - val_loss: 0.1789 - val_accuracy: 0.9660 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7198199e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9788 - val_loss: 0.2028 - val_accuracy: 0.9614 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.8683754e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9786 - val_loss: 0.2030 - val_accuracy: 0.9600 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.8432110e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9778 - val_loss: 0.2010 - val_accuracy: 0.9622 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5458489e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9783 - val_loss: 0.1836 - val_accuracy: 0.9670 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5059527e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9805 - val_loss: 0.2164 - val_accuracy: 0.9569 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2640936e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9784 - val_loss: 0.2170 - val_accuracy: 0.9587 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2943117e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9786 - val_loss: 0.1909 - val_accuracy: 0.9641 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.4153975e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9788 - val_loss: 0.1860 - val_accuracy: 0.9677 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.5981451e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1299 - accuracy: 0.9796 - val_loss: 0.1828 - val_accuracy: 0.9663 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.3238861e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9784 - val_loss: 0.2124 - val_accuracy: 0.9611 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -7.9829395e-03 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9769 - val_loss: 0.1993 - val_accuracy: 0.9631 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.1587327e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9794 - val_loss: 0.2212 - val_accuracy: 0.9555 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.2829201e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9788 - val_loss: 0.1967 - val_accuracy: 0.9636 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.6314967e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9782 - val_loss: 0.1730 - val_accuracy: 0.9692 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -2.0970883e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9780 - val_loss: 0.1899 - val_accuracy: 0.9645 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.3608278e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9790 - val_loss: 0.2224 - val_accuracy: 0.9553 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.3582623e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9790 - val_loss: 0.1897 - val_accuracy: 0.9644 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7563354e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9784 - val_loss: 0.2028 - val_accuracy: 0.9617 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.7810099e-02 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9784 - val_loss: 0.1976 - val_accuracy: 0.9628 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.1768318e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9800 - val_loss: 0.2011 - val_accuracy: 0.9610 [-2.5603684e-34 -4.8944090e-34 2.8132897e-34 ... -1.2480950e-02 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.08079263711495116 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03723478130996227 Thresholhold -0.05253946781158447 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06117202341556549 Thresholhold 0.03103388100862503 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.1142112985253334 Thresholhold -0.009608536958694458 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 6/235 [..............................] - ETA: 2s - loss: 1.5184 - accuracy: 0.5339 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0100s vs `on_train_batch_begin` time: 11.3089s). Check your callbacks. 235/235 [==============================] - 71s 12ms/step - loss: 0.2422 - accuracy: 0.9297 - val_loss: 0.2058 - val_accuracy: 0.9573 [-0.05253947 -0.00531845 -0.04093379 ... 0.1630142 -0.22765085 -0.1300145 ] Sparsity at: 0.028493613824192337 Epoch 2/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0874 - accuracy: 0.9751 - val_loss: 0.0991 - val_accuracy: 0.9686 [-0.05253947 -0.00531845 -0.04093379 ... 0.18007904 -0.24375299 -0.14138964] Sparsity at: 0.028493613824192337 Epoch 3/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0511 - accuracy: 0.9857 - val_loss: 0.0899 - val_accuracy: 0.9708 [-0.05253947 -0.00531845 -0.04093379 ... 0.19417247 -0.25554326 -0.14446865] Sparsity at: 0.028493613824192337 Epoch 4/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0315 - accuracy: 0.9918 - val_loss: 0.0844 - val_accuracy: 0.9737 [-0.05253947 -0.00531845 -0.04093379 ... 0.21089199 -0.26832888 -0.14684612] Sparsity at: 0.028493613824192337 Epoch 5/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0189 - accuracy: 0.9957 - val_loss: 0.0873 - val_accuracy: 0.9731 [-0.05253947 -0.00531845 -0.04093379 ... 0.22065996 -0.2767522 -0.14960967] Sparsity at: 0.028493613824192337 Epoch 6/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0150 - accuracy: 0.9964 - val_loss: 0.0962 - val_accuracy: 0.9718 [-0.05253947 -0.00531845 -0.04093379 ... 0.23070483 -0.286835 -0.15536873] Sparsity at: 0.028493613824192337 Epoch 7/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0121 - accuracy: 0.9970 - val_loss: 0.0874 - val_accuracy: 0.9748 [-0.05253947 -0.00531845 -0.04093379 ... 0.24042109 -0.2943962 -0.16160455] Sparsity at: 0.028493613824192337 Epoch 8/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9979 - val_loss: 0.0886 - val_accuracy: 0.9741 [-0.05253947 -0.00531845 -0.04093379 ... 0.24257672 -0.29986876 -0.16383283] Sparsity at: 0.028493613824192337 Epoch 9/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.1026 - val_accuracy: 0.9738 [-0.05253947 -0.00531845 -0.04093379 ... 0.24477692 -0.30399007 -0.16431989] Sparsity at: 0.028493613824192337 Epoch 10/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0109 - accuracy: 0.9968 - val_loss: 0.1040 - val_accuracy: 0.9726 [-0.05253947 -0.00531845 -0.04093379 ... 0.25685436 -0.3004289 -0.16854183] Sparsity at: 0.028493613824192337 Epoch 11/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.1192 - val_accuracy: 0.9690 [-0.05253947 -0.00531845 -0.04093379 ... 0.25699055 -0.30823794 -0.17546538] Sparsity at: 0.028493613824192337 Epoch 12/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0099 - accuracy: 0.9969 - val_loss: 0.1018 - val_accuracy: 0.9734 [-0.05253947 -0.00531845 -0.04093379 ... 0.25446513 -0.32200634 -0.18417396] Sparsity at: 0.028493613824192337 Epoch 13/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 0.0846 - val_accuracy: 0.9766 [-0.05253947 -0.00531845 -0.04093379 ... 0.27181855 -0.32726783 -0.20164146] Sparsity at: 0.028493613824192337 Epoch 14/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9981 - val_loss: 0.0982 - val_accuracy: 0.9774 [-0.05253947 -0.00531845 -0.04093379 ... 0.28313655 -0.3361384 -0.18875337] Sparsity at: 0.028493613824192337 Epoch 15/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.0882 - val_accuracy: 0.9787 [-0.05253947 -0.00531845 -0.04093379 ... 0.296955 -0.33465752 -0.19792363] Sparsity at: 0.028493613824192337 Epoch 16/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0057 - accuracy: 0.9981 - val_loss: 0.0912 - val_accuracy: 0.9765 [-0.05253947 -0.00531845 -0.04093379 ... 0.2887571 -0.34283307 -0.19516125] Sparsity at: 0.028493613824192337 Epoch 17/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 0.0873 - val_accuracy: 0.9791 [-0.05253947 -0.00531845 -0.04093379 ... 0.2946831 -0.34421405 -0.19301544] Sparsity at: 0.028493613824192337 Epoch 18/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.0932 - val_accuracy: 0.9769 [-0.05253947 -0.00531845 -0.04093379 ... 0.29551652 -0.35016304 -0.19330052] Sparsity at: 0.028493613824192337 Epoch 19/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.1012 - val_accuracy: 0.9782 [-0.05253947 -0.00531845 -0.04093379 ... 0.30277315 -0.35761258 -0.18982498] Sparsity at: 0.028493613824192337 Epoch 20/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0096 - accuracy: 0.9967 - val_loss: 0.1269 - val_accuracy: 0.9711 [-0.05253947 -0.00531845 -0.04093379 ... 0.2966441 -0.36450076 -0.19756731] Sparsity at: 0.028493613824192337 Epoch 21/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0092 - accuracy: 0.9969 - val_loss: 0.0969 - val_accuracy: 0.9786 [-0.05253947 -0.00531845 -0.04093379 ... 0.30469945 -0.3748371 -0.2106808 ] Sparsity at: 0.028493613824192337 Epoch 22/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 0.0994 - val_accuracy: 0.9783 [-0.05253947 -0.00531845 -0.04093379 ... 0.29983428 -0.37453938 -0.20567834] Sparsity at: 0.028493613824192337 Epoch 23/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 0.0820 - val_accuracy: 0.9807 [-0.05253947 -0.00531845 -0.04093379 ... 0.30382502 -0.37038073 -0.2122615 ] Sparsity at: 0.028493613824192337 Epoch 24/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.0796 - val_accuracy: 0.9815 [-0.05253947 -0.00531845 -0.04093379 ... 0.3083112 -0.37620506 -0.21654834] Sparsity at: 0.028493613824192337 Epoch 25/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0756 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.3149418 -0.38134745 -0.22139305] Sparsity at: 0.028493613824192337 Epoch 26/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3848e-04 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.3180193 -0.38515773 -0.22014071] Sparsity at: 0.028493613824192337 Epoch 27/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7906e-04 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.31800076 -0.386262 -0.2203167 ] Sparsity at: 0.028493613824192337 Epoch 28/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3312e-04 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9852 [-0.05253947 -0.00531845 -0.04093379 ... 0.31991935 -0.38648432 -0.22118808] Sparsity at: 0.028493613824192337 Epoch 29/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0225e-04 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.32088023 -0.38670844 -0.22152913] Sparsity at: 0.028493613824192337 Epoch 30/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5092e-05 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9853 [-0.05253947 -0.00531845 -0.04093379 ... 0.32219225 -0.38694754 -0.22246532] Sparsity at: 0.028493613824192337 Epoch 31/500 235/235 [==============================] - 3s 13ms/step - loss: 8.6307e-05 - accuracy: 1.0000 - val_loss: 0.0736 - val_accuracy: 0.9851 [-0.05253947 -0.00531845 -0.04093379 ... 0.32347164 -0.3893588 -0.22330298] Sparsity at: 0.028493613824192337 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0506e-05 - accuracy: 1.0000 - val_loss: 0.0759 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.32495755 -0.38953918 -0.22432923] Sparsity at: 0.028493613824192337 Epoch 33/500 235/235 [==============================] - 3s 13ms/step - loss: 6.4666e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9854 [-0.05253947 -0.00531845 -0.04093379 ... 0.3266862 -0.38976952 -0.22464 ] Sparsity at: 0.028493613824192337 Epoch 34/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7277e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9850 [-0.05253947 -0.00531845 -0.04093379 ... 0.32789427 -0.39120758 -0.22566344] Sparsity at: 0.028493613824192337 Epoch 35/500 235/235 [==============================] - 3s 13ms/step - loss: 4.2608e-05 - accuracy: 1.0000 - val_loss: 0.0760 - val_accuracy: 0.9853 [-0.05253947 -0.00531845 -0.04093379 ... 0.32914403 -0.39171678 -0.2270096 ] Sparsity at: 0.028493613824192337 Epoch 36/500 235/235 [==============================] - 3s 13ms/step - loss: 6.6978e-04 - accuracy: 0.9998 - val_loss: 0.1193 - val_accuracy: 0.9761 [-0.05253947 -0.00531845 -0.04093379 ... 0.3310632 -0.39295506 -0.22753336] Sparsity at: 0.028493613824192337 Epoch 37/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0507 - accuracy: 0.9848 - val_loss: 0.1027 - val_accuracy: 0.9743 [-0.05253947 -0.00531845 -0.04093379 ... 0.3008628 -0.37332273 -0.20221902] Sparsity at: 0.028493613824192337 Epoch 38/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 0.0775 - val_accuracy: 0.9802 [-0.05253947 -0.00531845 -0.04093379 ... 0.3083167 -0.38094267 -0.20853265] Sparsity at: 0.028493613824192337 Epoch 39/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.0700 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.31099388 -0.3858391 -0.21607019] Sparsity at: 0.028493613824192337 Epoch 40/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0711 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.320148 -0.38993904 -0.21512288] Sparsity at: 0.028493613824192337 Epoch 41/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8217e-04 - accuracy: 1.0000 - val_loss: 0.0677 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.32267442 -0.39154312 -0.21741927] Sparsity at: 0.028493613824192337 Epoch 42/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6307e-04 - accuracy: 1.0000 - val_loss: 0.0689 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.32570335 -0.3944111 -0.2203474 ] Sparsity at: 0.028493613824192337 Epoch 43/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7469e-04 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.32962465 -0.39551395 -0.22104532] Sparsity at: 0.028493613824192337 Epoch 44/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1594e-04 - accuracy: 1.0000 - val_loss: 0.0688 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.3319438 -0.39723575 -0.22241756] Sparsity at: 0.028493613824192337 Epoch 45/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7908e-04 - accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.3349972 -0.39817977 -0.2226066 ] Sparsity at: 0.028493613824192337 Epoch 46/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5109e-04 - accuracy: 1.0000 - val_loss: 0.0699 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.3373263 -0.39949596 -0.22429776] Sparsity at: 0.028493613824192337 Epoch 47/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5256e-04 - accuracy: 1.0000 - val_loss: 0.0705 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.3399573 -0.40099177 -0.2258579 ] Sparsity at: 0.028493613824192337 Epoch 48/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2097e-04 - accuracy: 1.0000 - val_loss: 0.0710 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.3425105 -0.40225604 -0.2273976 ] Sparsity at: 0.028493613824192337 Epoch 49/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0066e-04 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.34453195 -0.40333092 -0.22785884] Sparsity at: 0.028493613824192337 Epoch 50/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2262e-05 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.34796718 -0.4040528 -0.22923976] Sparsity at: 0.028493613824192337 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.07339460449208701 Thresholhold -0.05253946781158447 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.08953173582430018 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.2962369120395607 Thresholhold -0.0036686165258288383 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 130s 11ms/step - loss: 7.6591e-05 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9848 [-0.05253947 -0.00531845 -0.04093379 ... 0.34990335 -0.40635416 -0.23016603] Sparsity at: 0.028493613824192337 Epoch 52/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0133 - accuracy: 0.9958 - val_loss: 0.1837 - val_accuracy: 0.9603 [-0.05253947 -0.00531845 -0.04093379 ... 0.33878976 -0.4117443 -0.22430009] Sparsity at: 0.028493613824192337 Epoch 53/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0244 - accuracy: 0.9919 - val_loss: 0.0917 - val_accuracy: 0.9800 [-0.05253947 -0.00531845 -0.04093379 ... 0.3338583 -0.4257702 -0.22917296] Sparsity at: 0.028493613824192337 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9981 - val_loss: 0.0814 - val_accuracy: 0.9807 [-0.05253947 -0.00531845 -0.04093379 ... 0.33153558 -0.427032 -0.22228569] Sparsity at: 0.028493613824192337 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0802 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.33592713 -0.4222424 -0.22849429] Sparsity at: 0.028493613824192337 Epoch 56/500 235/235 [==============================] - 3s 13ms/step - loss: 9.5860e-04 - accuracy: 0.9999 - val_loss: 0.0754 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.34417802 -0.4266879 -0.23742744] Sparsity at: 0.028493613824192337 Epoch 57/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9887e-04 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.34488225 -0.43084544 -0.23713864] Sparsity at: 0.028493613824192337 Epoch 58/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4958e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.34707198 -0.43370712 -0.23955122] Sparsity at: 0.028493613824192337 Epoch 59/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7272e-04 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.34847236 -0.43400165 -0.24058868] Sparsity at: 0.028493613824192337 Epoch 60/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6249e-04 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.34901622 -0.43694764 -0.23942278] Sparsity at: 0.028493613824192337 Epoch 61/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1185e-04 - accuracy: 1.0000 - val_loss: 0.0757 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.3502961 -0.43882793 -0.23932454] Sparsity at: 0.028493613824192337 Epoch 62/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5884e-04 - accuracy: 0.9999 - val_loss: 0.0821 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.35202223 -0.44278568 -0.23950933] Sparsity at: 0.028493613824192337 Epoch 63/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1314 - val_accuracy: 0.9744 [-0.05253947 -0.00531845 -0.04093379 ... 0.3562509 -0.44537985 -0.2400399 ] Sparsity at: 0.028493613824192337 Epoch 64/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.1355 - val_accuracy: 0.9724 [-0.05253947 -0.00531845 -0.04093379 ... 0.35321704 -0.47800648 -0.23570296] Sparsity at: 0.028493613824192337 Epoch 65/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9978 - val_loss: 0.0938 - val_accuracy: 0.9800 [-0.05253947 -0.00531845 -0.04093379 ... 0.36568773 -0.46837044 -0.24137023] Sparsity at: 0.028493613824192337 Epoch 66/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0842 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.36792254 -0.48225638 -0.254048 ] Sparsity at: 0.028493613824192337 Epoch 67/500 235/235 [==============================] - 3s 13ms/step - loss: 8.7240e-04 - accuracy: 0.9998 - val_loss: 0.0840 - val_accuracy: 0.9818 [-0.05253947 -0.00531845 -0.04093379 ... 0.36985406 -0.4843077 -0.25383377] Sparsity at: 0.028493613824192337 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3026e-04 - accuracy: 0.9999 - val_loss: 0.0797 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.36635187 -0.4863404 -0.2503565 ] Sparsity at: 0.028493613824192337 Epoch 69/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8161e-04 - accuracy: 0.9999 - val_loss: 0.0840 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3678257 -0.48731622 -0.25327405] Sparsity at: 0.028493613824192337 Epoch 70/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8801e-04 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.37018576 -0.483412 -0.25637156] Sparsity at: 0.028493613824192337 Epoch 71/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2597e-05 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.3706929 -0.48447233 -0.25799137] Sparsity at: 0.028493613824192337 Epoch 72/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0072e-04 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.37732875 -0.4858268 -0.26530284] Sparsity at: 0.028493613824192337 Epoch 73/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7013e-05 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3755851 -0.4867917 -0.26711783] Sparsity at: 0.028493613824192337 Epoch 74/500 235/235 [==============================] - 3s 13ms/step - loss: 6.3877e-05 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.37774694 -0.48799488 -0.26740852] Sparsity at: 0.028493613824192337 Epoch 75/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.1454 - val_accuracy: 0.9710 [-0.05253947 -0.00531845 -0.04093379 ... 0.3762785 -0.48707724 -0.25615996] Sparsity at: 0.028493613824192337 Epoch 76/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.1097 - val_accuracy: 0.9786 [-0.05253947 -0.00531845 -0.04093379 ... 0.37672153 -0.4982522 -0.25450817] Sparsity at: 0.028493613824192337 Epoch 77/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.0995 - val_accuracy: 0.9804 [-0.05253947 -0.00531845 -0.04093379 ... 0.36842412 -0.5089398 -0.25765958] Sparsity at: 0.028493613824192337 Epoch 78/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0929 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.36826342 -0.51967233 -0.26251867] Sparsity at: 0.028493613824192337 Epoch 79/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8757e-04 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.37044266 -0.52538264 -0.2655473 ] Sparsity at: 0.028493613824192337 Epoch 80/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2037e-04 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.37214622 -0.52664334 -0.26682445] Sparsity at: 0.028493613824192337 Epoch 81/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0753e-04 - accuracy: 0.9999 - val_loss: 0.0895 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3724655 -0.52648777 -0.27156857] Sparsity at: 0.028493613824192337 Epoch 82/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1669e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.37302312 -0.5267605 -0.27142715] Sparsity at: 0.028493613824192337 Epoch 83/500 235/235 [==============================] - 3s 13ms/step - loss: 7.5810e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.37299564 -0.5269647 -0.27208534] Sparsity at: 0.028493613824192337 Epoch 84/500 235/235 [==============================] - 3s 13ms/step - loss: 5.5883e-05 - accuracy: 1.0000 - val_loss: 0.0886 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3739698 -0.5273404 -0.2720937 ] Sparsity at: 0.028493613824192337 Epoch 85/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4062e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9830ss: 5.1 [-0.05253947 -0.00531845 -0.04093379 ... 0.37481597 -0.52749056 -0.27357888] Sparsity at: 0.028493613824192337 Epoch 86/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9425e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.3755159 -0.5271866 -0.27458513] Sparsity at: 0.028493613824192337 Epoch 87/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4203e-04 - accuracy: 0.9999 - val_loss: 0.0950 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.37696552 -0.52789056 -0.27556142] Sparsity at: 0.028493613824192337 Epoch 88/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.1444 - val_accuracy: 0.9714 [-0.05253947 -0.00531845 -0.04093379 ... 0.38525414 -0.52045673 -0.28974032] Sparsity at: 0.028493613824192337 Epoch 89/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.0925 - val_accuracy: 0.9810 [-0.05253947 -0.00531845 -0.04093379 ... 0.3900753 -0.5366335 -0.302805 ] Sparsity at: 0.028493613824192337 Epoch 90/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0899 - val_accuracy: 0.9812 [-0.05253947 -0.00531845 -0.04093379 ... 0.38603365 -0.54318345 -0.30587658] Sparsity at: 0.028493613824192337 Epoch 91/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9413e-04 - accuracy: 0.9999 - val_loss: 0.0847 - val_accuracy: 0.9815 [-0.05253947 -0.00531845 -0.04093379 ... 0.38272133 -0.5479905 -0.30526683] Sparsity at: 0.028493613824192337 Epoch 92/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8766e-04 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9819 [-0.05253947 -0.00531845 -0.04093379 ... 0.38335004 -0.54550236 -0.30562896] Sparsity at: 0.028493613824192337 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1267e-04 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.38616383 -0.5450477 -0.3061652 ] Sparsity at: 0.028493613824192337 Epoch 94/500 235/235 [==============================] - 3s 13ms/step - loss: 8.8604e-05 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.38754 -0.54712737 -0.30576062] Sparsity at: 0.028493613824192337 Epoch 95/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1384e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.39706928 -0.54771453 -0.31391507] Sparsity at: 0.028493613824192337 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1328e-04 - accuracy: 1.0000 - val_loss: 0.0886 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.38748395 -0.5468919 -0.3011827 ] Sparsity at: 0.028493613824192337 Epoch 97/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2541e-04 - accuracy: 0.9999 - val_loss: 0.0896 - val_accuracy: 0.9816 [-0.05253947 -0.00531845 -0.04093379 ... 0.38861644 -0.5503864 -0.31004646] Sparsity at: 0.028493613824192337 Epoch 98/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1136 - val_accuracy: 0.9794 [-0.05253947 -0.00531845 -0.04093379 ... 0.3760282 -0.5527466 -0.31087378] Sparsity at: 0.028493613824192337 Epoch 99/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0074 - accuracy: 0.9977 - val_loss: 0.1134 - val_accuracy: 0.9788 [-0.05253947 -0.00531845 -0.04093379 ... 0.37491187 -0.5411652 -0.30185553] Sparsity at: 0.028493613824192337 Epoch 100/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0032 - accuracy: 0.9989 - val_loss: 0.0894 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.3793986 -0.5447194 -0.30860624] Sparsity at: 0.028493613824192337 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.14127264671771833 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.1556331708409715 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.3954344464909063 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 215s 12ms/step - loss: 9.8961e-04 - accuracy: 0.9998 - val_loss: 0.0901 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.38145888 -0.55757713 -0.31393695] Sparsity at: 0.028493613824192337 Epoch 102/500 235/235 [==============================] - 3s 12ms/step - loss: 6.1206e-04 - accuracy: 0.9999 - val_loss: 0.0959 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.3682108 -0.56251776 -0.30102545] Sparsity at: 0.028493613824192337 Epoch 103/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3033e-04 - accuracy: 0.9999 - val_loss: 0.0901 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.37462536 -0.5611727 -0.30626127] Sparsity at: 0.028493613824192337 Epoch 104/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9533e-04 - accuracy: 0.9999 - val_loss: 0.0914 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.3678348 -0.56070715 -0.3062491 ] Sparsity at: 0.028493613824192337 Epoch 105/500 235/235 [==============================] - 3s 13ms/step - loss: 7.3941e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.3697938 -0.5610731 -0.30813462] Sparsity at: 0.028493613824192337 Epoch 106/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4402e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.37051615 -0.56158155 -0.30885583] Sparsity at: 0.028493613824192337 Epoch 107/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9495e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3721818 -0.5617873 -0.30927542] Sparsity at: 0.028493613824192337 Epoch 108/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4306e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.373222 -0.562404 -0.30962446] Sparsity at: 0.028493613824192337 Epoch 109/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2007e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.37452364 -0.5625685 -0.30957958] Sparsity at: 0.028493613824192337 Epoch 110/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1390e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.37525836 -0.56317014 -0.31019965] Sparsity at: 0.028493613824192337 Epoch 111/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4900e-05 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.37630805 -0.5638951 -0.31110716] Sparsity at: 0.028493613824192337 Epoch 112/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7734e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.377371 -0.5643369 -0.30994532] Sparsity at: 0.028493613824192337 Epoch 113/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2143e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.378138 -0.56666094 -0.30996656] Sparsity at: 0.028493613824192337 Epoch 114/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8295e-05 - accuracy: 1.0000 - val_loss: 0.0907 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.37841523 -0.5664082 -0.31058016] Sparsity at: 0.028493613824192337 Epoch 115/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1457e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9816 [-0.05253947 -0.00531845 -0.04093379 ... 0.37838596 -0.5656528 -0.33028063] Sparsity at: 0.028493613824192337 Epoch 116/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0138 - accuracy: 0.9960 - val_loss: 0.1399 - val_accuracy: 0.9761 [-0.05253947 -0.00531845 -0.04093379 ... 0.33657083 -0.5955993 -0.30402714] Sparsity at: 0.028493613824192337 Epoch 117/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0047 - accuracy: 0.9984 - val_loss: 0.1023 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.33952615 -0.57665896 -0.30474767] Sparsity at: 0.028493613824192337 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1023 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.3407409 -0.57557905 -0.30046615] Sparsity at: 0.028493613824192337 Epoch 119/500 235/235 [==============================] - 4s 16ms/step - loss: 4.0144e-04 - accuracy: 0.9999 - val_loss: 0.0945 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.33259517 -0.57551533 -0.30331314] Sparsity at: 0.028493613824192337 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8018e-04 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.33931792 -0.5758037 -0.30653715] Sparsity at: 0.028493613824192337 Epoch 121/500 235/235 [==============================] - 3s 13ms/step - loss: 9.7532e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.34146696 -0.57623804 -0.30718246] Sparsity at: 0.028493613824192337 Epoch 122/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9860e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.34169415 -0.5773926 -0.30809715] Sparsity at: 0.028493613824192337 Epoch 123/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9356e-04 - accuracy: 0.9999 - val_loss: 0.0984 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.34394762 -0.57964325 -0.30821267] Sparsity at: 0.028493613824192337 Epoch 124/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8900e-04 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.34731606 -0.5828778 -0.30701864] Sparsity at: 0.028493613824192337 Epoch 125/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0822e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.3477226 -0.5828083 -0.30877218] Sparsity at: 0.028493613824192337 Epoch 126/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0984e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.34829974 -0.5838559 -0.3106947 ] Sparsity at: 0.028493613824192337 Epoch 127/500 235/235 [==============================] - 3s 13ms/step - loss: 9.6074e-05 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.34882772 -0.58108807 -0.31183174] Sparsity at: 0.028493613824192337 Epoch 128/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2032e-04 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.34930018 -0.5809999 -0.31287077] Sparsity at: 0.028493613824192337 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9414e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.35030168 -0.5804842 -0.31518677] Sparsity at: 0.028493613824192337 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7100e-05 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.35123676 -0.5819956 -0.31653532] Sparsity at: 0.028493613824192337 Epoch 131/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5069e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.35223976 -0.5799607 -0.3177871 ] Sparsity at: 0.028493613824192337 Epoch 132/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2624e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.35430032 -0.57967615 -0.32100445] Sparsity at: 0.028493613824192337 Epoch 133/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2510e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.35552028 -0.58010876 -0.32347208] Sparsity at: 0.028493613824192337 Epoch 134/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6416e-05 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.3560678 -0.58122295 -0.3238894 ] Sparsity at: 0.028493613824192337 Epoch 135/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5135e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.35677943 -0.5820179 -0.32506236] Sparsity at: 0.028493613824192337 Epoch 136/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1529e-05 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.35833186 -0.5833351 -0.32594395] Sparsity at: 0.028493613824192337 Epoch 137/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1949e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.3582305 -0.58338475 -0.32669228] Sparsity at: 0.028493613824192337 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 9.1549e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.3592685 -0.5834057 -0.32820123] Sparsity at: 0.028493613824192337 Epoch 139/500 235/235 [==============================] - 3s 13ms/step - loss: 8.9147e-06 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.36024037 -0.5839545 -0.32955852] Sparsity at: 0.028493613824192337 Epoch 140/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0990e-06 - accuracy: 1.0000 - val_loss: 0.0958 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.36083782 -0.5843364 -0.3306772 ] Sparsity at: 0.028493613824192337 Epoch 141/500 235/235 [==============================] - 3s 13ms/step - loss: 7.4429e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.36180037 -0.5853038 -0.33166435] Sparsity at: 0.028493613824192337 Epoch 142/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0061 - accuracy: 0.9984 - val_loss: 0.2037 - val_accuracy: 0.9679 [-0.05253947 -0.00531845 -0.04093379 ... 0.3520364 -0.59700835 -0.34609294] Sparsity at: 0.028493613824192337 Epoch 143/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0135 - accuracy: 0.9958 - val_loss: 0.1093 - val_accuracy: 0.9784 [-0.05253947 -0.00531845 -0.04093379 ... 0.35828766 -0.59009224 -0.32892433] Sparsity at: 0.028493613824192337 Epoch 144/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1097 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.35657725 -0.59260356 -0.3127037 ] Sparsity at: 0.028493613824192337 Epoch 145/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6933e-04 - accuracy: 0.9999 - val_loss: 0.1035 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.35459682 -0.59434235 -0.32001728] Sparsity at: 0.028493613824192337 Epoch 146/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7000e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.3553557 -0.59662634 -0.3226922 ] Sparsity at: 0.028493613824192337 Epoch 147/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1891e-04 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.35744587 -0.59439105 -0.3254116 ] Sparsity at: 0.028493613824192337 Epoch 148/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1000e-04 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.35950604 -0.59500223 -0.32667148] Sparsity at: 0.028493613824192337 Epoch 149/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7021e-05 - accuracy: 1.0000 - val_loss: 0.1009 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.359311 -0.59648633 -0.32731012] Sparsity at: 0.028493613824192337 Epoch 150/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3437e-05 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.36032385 -0.5967678 -0.32769474] Sparsity at: 0.028493613824192337 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.2085662078037709 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.22348551991905907 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.47685135402196366 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 219s 13ms/step - loss: 6.9098e-05 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.36178115 -0.5966983 -0.3282972 ] Sparsity at: 0.028493613824192337 Epoch 152/500 235/235 [==============================] - 4s 15ms/step - loss: 4.8256e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.36259553 -0.5968129 -0.32861254] Sparsity at: 0.028493613824192337 Epoch 153/500 235/235 [==============================] - 4s 16ms/step - loss: 3.5419e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.3629191 -0.5959407 -0.32891646] Sparsity at: 0.028493613824192337 Epoch 154/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9828e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.3638738 -0.595998 -0.3300056 ] Sparsity at: 0.028493613824192337 Epoch 155/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0053e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.36476353 -0.5959386 -0.3305032 ] Sparsity at: 0.028493613824192337 Epoch 156/500 235/235 [==============================] - 4s 15ms/step - loss: 2.4078e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.3657621 -0.5959385 -0.33178785] Sparsity at: 0.028493613824192337 Epoch 157/500 235/235 [==============================] - 4s 16ms/step - loss: 6.8435e-04 - accuracy: 0.9999 - val_loss: 0.1129 - val_accuracy: 0.9821 [-0.05253947 -0.00531845 -0.04093379 ... 0.3668842 -0.6250114 -0.33427578] Sparsity at: 0.028493613824192337 Epoch 158/500 235/235 [==============================] - 4s 17ms/step - loss: 0.0085 - accuracy: 0.9971 - val_loss: 0.1241 - val_accuracy: 0.9798 [-0.05253947 -0.00531845 -0.04093379 ... 0.34749416 -0.60533786 -0.34225455] Sparsity at: 0.028493613824192337 Epoch 159/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0040 - accuracy: 0.9985 - val_loss: 0.0973 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.3301137 -0.6007582 -0.34236142] Sparsity at: 0.028493613824192337 Epoch 160/500 235/235 [==============================] - 4s 17ms/step - loss: 6.3565e-04 - accuracy: 0.9998 - val_loss: 0.0962 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.3334383 -0.59871596 -0.34255165] Sparsity at: 0.028493613824192337 Epoch 161/500 235/235 [==============================] - 4s 15ms/step - loss: 2.7321e-04 - accuracy: 0.9999 - val_loss: 0.0933 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.3264051 -0.6047131 -0.3410839 ] Sparsity at: 0.028493613824192337 Epoch 162/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0611e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.3279162 -0.60446775 -0.3414597 ] Sparsity at: 0.028493613824192337 Epoch 163/500 235/235 [==============================] - 4s 16ms/step - loss: 6.7074e-05 - accuracy: 1.0000 - val_loss: 0.0925 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.32852978 -0.6045377 -0.34183392] Sparsity at: 0.028493613824192337 Epoch 164/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3641e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3280881 -0.60439444 -0.3414738 ] Sparsity at: 0.028493613824192337 Epoch 165/500 235/235 [==============================] - 4s 15ms/step - loss: 4.1014e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.32887226 -0.6050874 -0.3424552 ] Sparsity at: 0.028493613824192337 Epoch 166/500 235/235 [==============================] - 4s 15ms/step - loss: 3.7888e-05 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.32990924 -0.60555184 -0.34330603] Sparsity at: 0.028493613824192337 Epoch 167/500 235/235 [==============================] - 4s 15ms/step - loss: 3.1752e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.33029842 -0.60703707 -0.34298778] Sparsity at: 0.028493613824192337 Epoch 168/500 235/235 [==============================] - 4s 15ms/step - loss: 2.7497e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.330209 -0.60737646 -0.34394518] Sparsity at: 0.028493613824192337 Epoch 169/500 235/235 [==============================] - 4s 15ms/step - loss: 3.4915e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.33107764 -0.60687226 -0.34573448] Sparsity at: 0.028493613824192337 Epoch 170/500 235/235 [==============================] - 4s 15ms/step - loss: 2.4893e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3321329 -0.6072875 -0.34455147] Sparsity at: 0.028493613824192337 Epoch 171/500 235/235 [==============================] - 4s 15ms/step - loss: 2.7950e-04 - accuracy: 0.9999 - val_loss: 0.1059 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.33860028 -0.59648293 -0.34592125] Sparsity at: 0.028493613824192337 Epoch 172/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.1260 - val_accuracy: 0.9771 [-0.05253947 -0.00531845 -0.04093379 ... 0.32209408 -0.60915136 -0.34786475] Sparsity at: 0.028493613824192337 Epoch 173/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0052 - accuracy: 0.9982 - val_loss: 0.1143 - val_accuracy: 0.9793 [-0.05253947 -0.00531845 -0.04093379 ... 0.33026496 -0.5899857 -0.34824738] Sparsity at: 0.028493613824192337 Epoch 174/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.1050 - val_accuracy: 0.9817 [-0.05253947 -0.00531845 -0.04093379 ... 0.31998917 -0.60240155 -0.33964372] Sparsity at: 0.028493613824192337 Epoch 175/500 235/235 [==============================] - 4s 15ms/step - loss: 3.6161e-04 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.3218198 -0.600925 -0.3412455 ] Sparsity at: 0.028493613824192337 Epoch 176/500 235/235 [==============================] - 4s 15ms/step - loss: 9.0039e-05 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.31887797 -0.6008649 -0.34359157] Sparsity at: 0.028493613824192337 Epoch 177/500 235/235 [==============================] - 4s 15ms/step - loss: 8.5917e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.32069352 -0.6011213 -0.34463534] Sparsity at: 0.028493613824192337 Epoch 178/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8168e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.3215081 -0.59526014 -0.34602165] Sparsity at: 0.028493613824192337 Epoch 179/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0558e-04 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.323693 -0.60119265 -0.34325206] Sparsity at: 0.028493613824192337 Epoch 180/500 235/235 [==============================] - 4s 16ms/step - loss: 4.0138e-05 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.32525718 -0.6031743 -0.3434317 ] Sparsity at: 0.028493613824192337 Epoch 181/500 235/235 [==============================] - 4s 16ms/step - loss: 2.9912e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3260202 -0.601369 -0.34387928] Sparsity at: 0.028493613824192337 Epoch 182/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4209e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.32714602 -0.6014944 -0.34452263] Sparsity at: 0.028493613824192337 Epoch 183/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1821e-05 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.32824525 -0.59984046 -0.3449477 ] Sparsity at: 0.028493613824192337 Epoch 184/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1044e-05 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.32901123 -0.6020376 -0.34595457] Sparsity at: 0.028493613824192337 Epoch 185/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6034e-05 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.3291937 -0.6026963 -0.3457884 ] Sparsity at: 0.028493613824192337 Epoch 186/500 235/235 [==============================] - 3s 15ms/step - loss: 1.9241e-05 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.329248 -0.60250777 -0.3445007 ] Sparsity at: 0.028493613824192337 Epoch 187/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5084e-05 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.33025324 -0.6019693 -0.34459484] Sparsity at: 0.028493613824192337 Epoch 188/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3761e-05 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3327615 -0.60342944 -0.3457315 ] Sparsity at: 0.028493613824192337 Epoch 189/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1049e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3340987 -0.60358167 -0.34578973] Sparsity at: 0.028493613824192337 Epoch 190/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0575e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.33472753 -0.60347146 -0.34674886] Sparsity at: 0.028493613824192337 Epoch 191/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0338e-05 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.3360264 -0.60477316 -0.34731847] Sparsity at: 0.028493613824192337 Epoch 192/500 235/235 [==============================] - 3s 15ms/step - loss: 8.3158e-06 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.33651447 -0.6046985 -0.34796482] Sparsity at: 0.028493613824192337 Epoch 193/500 235/235 [==============================] - 3s 15ms/step - loss: 7.4904e-06 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.33740076 -0.60759985 -0.3480437 ] Sparsity at: 0.028493613824192337 Epoch 194/500 235/235 [==============================] - 3s 15ms/step - loss: 7.6834e-06 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.33899307 -0.60821646 -0.3485056 ] Sparsity at: 0.028493613824192337 Epoch 195/500 235/235 [==============================] - 3s 15ms/step - loss: 6.8347e-06 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.34085461 -0.6092299 -0.35041448] Sparsity at: 0.028493613824192337 Epoch 196/500 235/235 [==============================] - 3s 15ms/step - loss: 6.9707e-06 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.342357 -0.60969734 -0.3505012 ] Sparsity at: 0.028493613824192337 Epoch 197/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5213e-04 - accuracy: 0.9999 - val_loss: 0.1386 - val_accuracy: 0.9792 [-0.05253947 -0.00531845 -0.04093379 ... 0.3613027 -0.61012095 -0.34253436] Sparsity at: 0.028493613824192337 Epoch 198/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.1245 - val_accuracy: 0.9796 [-0.05253947 -0.00531845 -0.04093379 ... 0.31342274 -0.6205524 -0.31672603] Sparsity at: 0.028493613824192337 Epoch 199/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0034 - accuracy: 0.9987 - val_loss: 0.1139 - val_accuracy: 0.9809 [-0.05253947 -0.00531845 -0.04093379 ... 0.31106874 -0.626597 -0.3182148 ] Sparsity at: 0.028493613824192337 Epoch 200/500 235/235 [==============================] - 4s 15ms/step - loss: 8.4787e-04 - accuracy: 0.9997 - val_loss: 0.1088 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.31580237 -0.6350527 -0.32656768] Sparsity at: 0.028493613824192337 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.29162271014998 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.30315527820678767 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.5731419925485284 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 208s 13ms/step - loss: 3.7498e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.3282217 -0.63859016 -0.32564944] Sparsity at: 0.028493613824192337 Epoch 202/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0873e-04 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.32888654 -0.6358007 -0.32503048] Sparsity at: 0.028493613824192337 Epoch 203/500 235/235 [==============================] - 3s 15ms/step - loss: 4.6290e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.32969365 -0.63650316 -0.32515275] Sparsity at: 0.028493613824192337 Epoch 204/500 235/235 [==============================] - 3s 15ms/step - loss: 4.0478e-05 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.33139792 -0.6365335 -0.32655036] Sparsity at: 0.028493613824192337 Epoch 205/500 235/235 [==============================] - 3s 15ms/step - loss: 3.6199e-05 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.3318752 -0.63715 -0.32727155] Sparsity at: 0.028493613824192337 Epoch 206/500 235/235 [==============================] - 4s 15ms/step - loss: 3.1553e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.33281848 -0.638031 -0.32799023] Sparsity at: 0.028493613824192337 Epoch 207/500 235/235 [==============================] - 4s 15ms/step - loss: 3.0115e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.33477437 -0.63777804 -0.32738146] Sparsity at: 0.028493613824192337 Epoch 208/500 235/235 [==============================] - 3s 15ms/step - loss: 8.0001e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.33446297 -0.63103104 -0.33161157] Sparsity at: 0.028493613824192337 Epoch 209/500 235/235 [==============================] - 4s 15ms/step - loss: 6.5418e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.33621478 -0.63463354 -0.33246684] Sparsity at: 0.028493613824192337 Epoch 210/500 235/235 [==============================] - 3s 15ms/step - loss: 8.3912e-04 - accuracy: 0.9998 - val_loss: 0.1090 - val_accuracy: 0.9812 [-0.05253947 -0.00531845 -0.04093379 ... 0.3418023 -0.6365531 -0.33621305] Sparsity at: 0.028493613824192337 Epoch 211/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.1227 - val_accuracy: 0.9817 [-0.05253947 -0.00531845 -0.04093379 ... 0.35438195 -0.6426361 -0.31660873] Sparsity at: 0.028493613824192337 Epoch 212/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.1178 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.35451314 -0.6412805 -0.3111367 ] Sparsity at: 0.028493613824192337 Epoch 213/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1075 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.35172042 -0.63379884 -0.33461586] Sparsity at: 0.028493613824192337 Epoch 214/500 235/235 [==============================] - 3s 15ms/step - loss: 6.2203e-04 - accuracy: 0.9998 - val_loss: 0.1047 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.35517102 -0.6275447 -0.33985034] Sparsity at: 0.028493613824192337 Epoch 215/500 235/235 [==============================] - 3s 15ms/step - loss: 1.4366e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.35241088 -0.63185316 -0.34323204] Sparsity at: 0.028493613824192337 Epoch 216/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0281e-04 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.35094044 -0.62979823 -0.3399597 ] Sparsity at: 0.028493613824192337 Epoch 217/500 235/235 [==============================] - 3s 15ms/step - loss: 5.7318e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.3518195 -0.63357794 -0.33974054] Sparsity at: 0.028493613824192337 Epoch 218/500 235/235 [==============================] - 3s 15ms/step - loss: 3.9641e-05 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.3519952 -0.6285311 -0.34418476] Sparsity at: 0.028493613824192337 Epoch 219/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0384e-05 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.35185662 -0.6344249 -0.33983135] Sparsity at: 0.028493613824192337 Epoch 220/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7531e-05 - accuracy: 1.0000 - val_loss: 0.1012 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.35170797 -0.63443977 -0.33925202] Sparsity at: 0.028493613824192337 Epoch 221/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6342e-05 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.35202208 -0.63368285 -0.3406762 ] Sparsity at: 0.028493613824192337 Epoch 222/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1716e-05 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.35220933 -0.63387966 -0.3408207 ] Sparsity at: 0.028493613824192337 Epoch 223/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5344e-05 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.35372785 -0.6339604 -0.3402826 ] Sparsity at: 0.028493613824192337 Epoch 224/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3887e-05 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.35705182 -0.63547164 -0.33962247] Sparsity at: 0.028493613824192337 Epoch 225/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1282e-05 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.3573348 -0.6364416 -0.33961615] Sparsity at: 0.028493613824192337 Epoch 226/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0023e-05 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9850 [-0.05253947 -0.00531845 -0.04093379 ... 0.3572184 -0.6374897 -0.33866256] Sparsity at: 0.028493613824192337 Epoch 227/500 235/235 [==============================] - 3s 15ms/step - loss: 7.7817e-06 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9851 [-0.05253947 -0.00531845 -0.04093379 ... 0.35717425 -0.63816357 -0.33911625] Sparsity at: 0.028493613824192337 Epoch 228/500 235/235 [==============================] - 4s 15ms/step - loss: 7.6519e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.35693383 -0.63809127 -0.33912024] Sparsity at: 0.028493613824192337 Epoch 229/500 235/235 [==============================] - 3s 15ms/step - loss: 8.0244e-06 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.35729587 -0.6381897 -0.33962682] Sparsity at: 0.028493613824192337 Epoch 230/500 235/235 [==============================] - 3s 15ms/step - loss: 7.2539e-06 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9850 [-0.05253947 -0.00531845 -0.04093379 ... 0.357503 -0.63829434 -0.33954144] Sparsity at: 0.028493613824192337 Epoch 231/500 235/235 [==============================] - 4s 15ms/step - loss: 5.5227e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9849 [-0.05253947 -0.00531845 -0.04093379 ... 0.358909 -0.6389968 -0.3396948 ] Sparsity at: 0.028493613824192337 Epoch 232/500 235/235 [==============================] - 3s 15ms/step - loss: 5.0536e-06 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9848 [-0.05253947 -0.00531845 -0.04093379 ... 0.3595098 -0.6404461 -0.3397753 ] Sparsity at: 0.028493613824192337 Epoch 233/500 235/235 [==============================] - 3s 15ms/step - loss: 4.5245e-06 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9848 [-0.05253947 -0.00531845 -0.04093379 ... 0.36035746 -0.64044523 -0.33986604] Sparsity at: 0.028493613824192337 Epoch 234/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 0.1612 - val_accuracy: 0.9755 [-0.05253947 -0.00531845 -0.04093379 ... 0.32954872 -0.66707844 -0.2799722 ] Sparsity at: 0.028493613824192337 Epoch 235/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0064 - accuracy: 0.9981 - val_loss: 0.1232 - val_accuracy: 0.9811 [-0.05253947 -0.00531845 -0.04093379 ... 0.3408525 -0.68324816 -0.28585318] Sparsity at: 0.028493613824192337 Epoch 236/500 235/235 [==============================] - 3s 15ms/step - loss: 9.5861e-04 - accuracy: 0.9997 - val_loss: 0.1173 - val_accuracy: 0.9813 [-0.05253947 -0.00531845 -0.04093379 ... 0.33100525 -0.6813621 -0.28572693] Sparsity at: 0.028493613824192337 Epoch 237/500 235/235 [==============================] - 3s 15ms/step - loss: 3.6782e-04 - accuracy: 0.9999 - val_loss: 0.1101 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.33048964 -0.6813524 -0.28304684] Sparsity at: 0.028493613824192337 Epoch 238/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7246e-04 - accuracy: 0.9999 - val_loss: 0.1128 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.33528364 -0.68132913 -0.28538585] Sparsity at: 0.028493613824192337 Epoch 239/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2679e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.33558017 -0.6827939 -0.28762403] Sparsity at: 0.028493613824192337 Epoch 240/500 235/235 [==============================] - 3s 15ms/step - loss: 7.7547e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.33385342 -0.6808557 -0.28706715] Sparsity at: 0.028493613824192337 Epoch 241/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1973e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.33053195 -0.6851457 -0.297317 ] Sparsity at: 0.028493613824192337 Epoch 242/500 235/235 [==============================] - 3s 15ms/step - loss: 4.5545e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3328235 -0.68240345 -0.29785013] Sparsity at: 0.028493613824192337 Epoch 243/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9556e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.33259293 -0.68338627 -0.29834065] Sparsity at: 0.028493613824192337 Epoch 244/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4098e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.33774096 -0.68795055 -0.29890636] Sparsity at: 0.028493613824192337 Epoch 245/500 235/235 [==============================] - 3s 15ms/step - loss: 2.2414e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.338559 -0.6884915 -0.30112162] Sparsity at: 0.028493613824192337 Epoch 246/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5437e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.34398097 -0.68851596 -0.3012117 ] Sparsity at: 0.028493613824192337 Epoch 247/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5777e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.34366265 -0.6878952 -0.30024797] Sparsity at: 0.028493613824192337 Epoch 248/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1671e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.34403443 -0.6883992 -0.30053008] Sparsity at: 0.028493613824192337 Epoch 249/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5884e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.3445571 -0.6888152 -0.2984268 ] Sparsity at: 0.028493613824192337 Epoch 250/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1302e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.34471732 -0.68942577 -0.29928938] Sparsity at: 0.028493613824192337 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.3769573828169399 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.38761267765780616 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.6553642874088581 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 210s 12ms/step - loss: 9.6954e-06 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.34493896 -0.68995523 -0.29846534] Sparsity at: 0.028493613824192337 Epoch 252/500 235/235 [==============================] - 3s 15ms/step - loss: 8.7721e-06 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.3456156 -0.68958616 -0.2995062 ] Sparsity at: 0.028493613824192337 Epoch 253/500 235/235 [==============================] - 3s 15ms/step - loss: 7.8525e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.34663764 -0.6899241 -0.30061305] Sparsity at: 0.028493613824192337 Epoch 254/500 235/235 [==============================] - 3s 15ms/step - loss: 8.2935e-04 - accuracy: 0.9998 - val_loss: 0.1450 - val_accuracy: 0.9783 [-0.05253947 -0.00531845 -0.04093379 ... 0.3524665 -0.7076545 -0.31729183] Sparsity at: 0.028493613824192337 Epoch 255/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0063 - accuracy: 0.9981 - val_loss: 0.1372 - val_accuracy: 0.9791 [-0.05253947 -0.00531845 -0.04093379 ... 0.32999137 -0.7192124 -0.31541118] Sparsity at: 0.028493613824192337 Epoch 256/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1126 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.338248 -0.71698594 -0.3322038 ] Sparsity at: 0.028493613824192337 Epoch 257/500 235/235 [==============================] - 3s 15ms/step - loss: 7.9587e-04 - accuracy: 0.9997 - val_loss: 0.1109 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.3495278 -0.71792465 -0.3319064 ] Sparsity at: 0.028493613824192337 Epoch 258/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5921e-04 - accuracy: 1.0000 - val_loss: 0.1114 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.35124496 -0.71811706 -0.3319571 ] Sparsity at: 0.028493613824192337 Epoch 259/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1160e-04 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.3535951 -0.7194339 -0.3314303 ] Sparsity at: 0.028493613824192337 Epoch 260/500 235/235 [==============================] - 4s 15ms/step - loss: 4.5688e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.3547755 -0.720462 -0.33150408] Sparsity at: 0.028493613824192337 Epoch 261/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7777e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.3551533 -0.7210222 -0.33122313] Sparsity at: 0.028493613824192337 Epoch 262/500 235/235 [==============================] - 4s 16ms/step - loss: 2.0926e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.35554045 -0.7212071 -0.33121547] Sparsity at: 0.028493613824192337 Epoch 263/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1409e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.35599744 -0.72198033 -0.3305833 ] Sparsity at: 0.028493613824192337 Epoch 264/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0071e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.35620958 -0.7265441 -0.33056432] Sparsity at: 0.028493613824192337 Epoch 265/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0599e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.3562115 -0.72717243 -0.32982796] Sparsity at: 0.028493613824192337 Epoch 266/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6300e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.35635972 -0.7277881 -0.32970777] Sparsity at: 0.028493613824192337 Epoch 267/500 235/235 [==============================] - 4s 15ms/step - loss: 1.6851e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.35725048 -0.7285392 -0.33032644] Sparsity at: 0.028493613824192337 Epoch 268/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0784e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.35789302 -0.7288335 -0.33073562] Sparsity at: 0.028493613824192337 Epoch 269/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0321e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.35796896 -0.72834134 -0.3320301 ] Sparsity at: 0.028493613824192337 Epoch 270/500 235/235 [==============================] - 4s 15ms/step - loss: 9.1514e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.3580983 -0.7282561 -0.33198023] Sparsity at: 0.028493613824192337 Epoch 271/500 235/235 [==============================] - 3s 15ms/step - loss: 4.9602e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.358535 -0.7285919 -0.33325428] Sparsity at: 0.028493613824192337 Epoch 272/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.1537 - val_accuracy: 0.9779 [-0.05253947 -0.00531845 -0.04093379 ... 0.35703626 -0.72296417 -0.32113713] Sparsity at: 0.028493613824192337 Epoch 273/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.1177 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.36600822 -0.7376247 -0.32277992] Sparsity at: 0.028493613824192337 Epoch 274/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1157 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.36370164 -0.7394978 -0.31989023] Sparsity at: 0.028493613824192337 Epoch 275/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0157e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.36874124 -0.74058014 -0.31789494] Sparsity at: 0.028493613824192337 Epoch 276/500 235/235 [==============================] - 4s 15ms/step - loss: 9.6139e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.37745672 -0.7409098 -0.32121834] Sparsity at: 0.028493613824192337 Epoch 277/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0056e-04 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.38016918 -0.74551475 -0.3231955 ] Sparsity at: 0.028493613824192337 Epoch 278/500 235/235 [==============================] - 4s 15ms/step - loss: 6.2877e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.37907943 -0.7416529 -0.3238429 ] Sparsity at: 0.028493613824192337 Epoch 279/500 235/235 [==============================] - 4s 15ms/step - loss: 2.4420e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.37871495 -0.7408408 -0.32500654] Sparsity at: 0.028493613824192337 Epoch 280/500 235/235 [==============================] - 4s 15ms/step - loss: 2.3444e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.37938166 -0.74168515 -0.32497957] Sparsity at: 0.028493613824192337 Epoch 281/500 235/235 [==============================] - 4s 15ms/step - loss: 1.9083e-05 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.37948242 -0.741639 -0.32723856] Sparsity at: 0.028493613824192337 Epoch 282/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7909e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.37981892 -0.74137765 -0.32777426] Sparsity at: 0.028493613824192337 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2744e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.380701 -0.74145734 -0.32804298] Sparsity at: 0.028493613824192337 Epoch 284/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2951e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.3805701 -0.7415988 -0.32718894] Sparsity at: 0.028493613824192337 Epoch 285/500 235/235 [==============================] - 3s 15ms/step - loss: 9.2786e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.38056248 -0.7415994 -0.3279441 ] Sparsity at: 0.028493613824192337 Epoch 286/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0351e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.38137338 -0.7418949 -0.32993144] Sparsity at: 0.028493613824192337 Epoch 287/500 235/235 [==============================] - 3s 15ms/step - loss: 7.3427e-06 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.38161573 -0.7420996 -0.33042267] Sparsity at: 0.028493613824192337 Epoch 288/500 235/235 [==============================] - 3s 15ms/step - loss: 9.4875e-06 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.3815148 -0.74457246 -0.3309453 ] Sparsity at: 0.028493613824192337 Epoch 289/500 235/235 [==============================] - 3s 15ms/step - loss: 9.7482e-06 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.3847198 -0.7448215 -0.33227092] Sparsity at: 0.028493613824192337 Epoch 290/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0053 - accuracy: 0.9985 - val_loss: 0.1532 - val_accuracy: 0.9769 [-0.05253947 -0.00531845 -0.04093379 ... 0.32146344 -0.67517334 -0.32523924] Sparsity at: 0.028493613824192337 Epoch 291/500 235/235 [==============================] - 4s 17ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.1166 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.33885777 -0.6704205 -0.3550896 ] Sparsity at: 0.028493613824192337 Epoch 292/500 235/235 [==============================] - 4s 17ms/step - loss: 5.2106e-04 - accuracy: 0.9998 - val_loss: 0.1155 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.3407158 -0.6798178 -0.34401575] Sparsity at: 0.028493613824192337 Epoch 293/500 235/235 [==============================] - 4s 15ms/step - loss: 1.3718e-04 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.34371942 -0.6820976 -0.34668252] Sparsity at: 0.028493613824192337 Epoch 294/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0842e-04 - accuracy: 0.9999 - val_loss: 0.1160 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.338106 -0.683269 -0.34831288] Sparsity at: 0.028493613824192337 Epoch 295/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1565e-04 - accuracy: 0.9999 - val_loss: 0.1214 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.33724385 -0.6833189 -0.3485224 ] Sparsity at: 0.028493613824192337 Epoch 296/500 235/235 [==============================] - 4s 15ms/step - loss: 6.0302e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.33977044 -0.6834201 -0.3491442 ] Sparsity at: 0.028493613824192337 Epoch 297/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3534e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.33964905 -0.6840145 -0.34916776] Sparsity at: 0.028493613824192337 Epoch 298/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3774e-05 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.33950672 -0.6849391 -0.35041755] Sparsity at: 0.028493613824192337 Epoch 299/500 235/235 [==============================] - 3s 15ms/step - loss: 1.8210e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.3398405 -0.6844752 -0.3508929 ] Sparsity at: 0.028493613824192337 Epoch 300/500 235/235 [==============================] - 3s 15ms/step - loss: 1.8564e-05 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.34099564 -0.6845815 -0.35192123] Sparsity at: 0.028493613824192337 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.48870776962442264 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.49260266792279594 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.724636740379438 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 193s 12ms/step - loss: 9.1279e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.34308377 -0.68833977 -0.35071516] Sparsity at: 0.028493613824192337 Epoch 302/500 235/235 [==============================] - 3s 15ms/step - loss: 7.0725e-04 - accuracy: 0.9999 - val_loss: 0.1390 - val_accuracy: 0.9813 [-0.05253947 -0.00531845 -0.04093379 ... 0.3389869 -0.68870175 -0.3536509 ] Sparsity at: 0.028493613824192337 Epoch 303/500 235/235 [==============================] - 3s 15ms/step - loss: 4.8661e-04 - accuracy: 0.9999 - val_loss: 0.1235 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.34880802 -0.69382083 -0.3520747 ] Sparsity at: 0.028493613824192337 Epoch 304/500 235/235 [==============================] - 3s 15ms/step - loss: 6.6366e-04 - accuracy: 0.9998 - val_loss: 0.1358 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.3279773 -0.7143462 -0.34004807] Sparsity at: 0.028493613824192337 Epoch 305/500 235/235 [==============================] - 4s 15ms/step - loss: 6.9825e-04 - accuracy: 0.9998 - val_loss: 0.1237 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.32125637 -0.6942915 -0.3405359 ] Sparsity at: 0.028493613824192337 Epoch 306/500 235/235 [==============================] - 3s 15ms/step - loss: 4.3108e-04 - accuracy: 0.9998 - val_loss: 0.1150 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.33510882 -0.6942015 -0.3290351 ] Sparsity at: 0.028493613824192337 Epoch 307/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4571e-04 - accuracy: 0.9999 - val_loss: 0.1173 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.34337336 -0.695978 -0.32965708] Sparsity at: 0.028493613824192337 Epoch 308/500 235/235 [==============================] - 3s 15ms/step - loss: 6.0004e-05 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.33742163 -0.69844997 -0.31403548] Sparsity at: 0.028493613824192337 Epoch 309/500 235/235 [==============================] - 3s 15ms/step - loss: 4.7362e-05 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.33906758 -0.6991668 -0.31716034] Sparsity at: 0.028493613824192337 Epoch 310/500 235/235 [==============================] - 4s 16ms/step - loss: 5.5784e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.34023178 -0.6957146 -0.3228236 ] Sparsity at: 0.028493613824192337 Epoch 311/500 235/235 [==============================] - 4s 15ms/step - loss: 3.5056e-04 - accuracy: 0.9998 - val_loss: 0.1353 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.34032702 -0.6688547 -0.32275695] Sparsity at: 0.028493613824192337 Epoch 312/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9995 - val_loss: 0.1312 - val_accuracy: 0.9813 [-0.05253947 -0.00531845 -0.04093379 ... 0.34604037 -0.704933 -0.32348305] Sparsity at: 0.028493613824192337 Epoch 313/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1195 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.34613296 -0.70734346 -0.3392192 ] Sparsity at: 0.028493613824192337 Epoch 314/500 235/235 [==============================] - 3s 15ms/step - loss: 5.8326e-04 - accuracy: 0.9998 - val_loss: 0.1265 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.33583698 -0.7027653 -0.3527485 ] Sparsity at: 0.028493613824192337 Epoch 315/500 235/235 [==============================] - 3s 15ms/step - loss: 3.3301e-04 - accuracy: 0.9999 - val_loss: 0.1143 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.34532002 -0.6986958 -0.3542171 ] Sparsity at: 0.028493613824192337 Epoch 316/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3004e-04 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.34780648 -0.7017104 -0.3535276 ] Sparsity at: 0.028493613824192337 Epoch 317/500 235/235 [==============================] - 3s 15ms/step - loss: 5.8694e-05 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.34832108 -0.7029398 -0.35806233] Sparsity at: 0.028493613824192337 Epoch 318/500 235/235 [==============================] - 4s 15ms/step - loss: 2.8451e-04 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.3497257 -0.70175576 -0.36120996] Sparsity at: 0.028493613824192337 Epoch 319/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1306 - val_accuracy: 0.9812 [-0.05253947 -0.00531845 -0.04093379 ... 0.35103017 -0.692668 -0.36321804] Sparsity at: 0.028493613824192337 Epoch 320/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1195 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.35971376 -0.7102315 -0.3783859 ] Sparsity at: 0.028493613824192337 Epoch 321/500 235/235 [==============================] - 4s 15ms/step - loss: 9.0621e-04 - accuracy: 0.9997 - val_loss: 0.1357 - val_accuracy: 0.9821 [-0.05253947 -0.00531845 -0.04093379 ... 0.35009074 -0.71499467 -0.35678914] Sparsity at: 0.028493613824192337 Epoch 322/500 235/235 [==============================] - 4s 16ms/step - loss: 7.5578e-04 - accuracy: 0.9998 - val_loss: 0.1204 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.35772544 -0.7179314 -0.36076006] Sparsity at: 0.028493613824192337 Epoch 323/500 235/235 [==============================] - 4s 15ms/step - loss: 1.6202e-04 - accuracy: 0.9999 - val_loss: 0.1212 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.35026088 -0.72350425 -0.35462517] Sparsity at: 0.028493613824192337 Epoch 324/500 235/235 [==============================] - 4s 15ms/step - loss: 3.4564e-05 - accuracy: 1.0000 - val_loss: 0.1212 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.35281816 -0.723027 -0.354968 ] Sparsity at: 0.028493613824192337 Epoch 325/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5037e-04 - accuracy: 0.9999 - val_loss: 0.1246 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.35527235 -0.7216929 -0.3561836 ] Sparsity at: 0.028493613824192337 Epoch 326/500 235/235 [==============================] - 4s 15ms/step - loss: 2.4162e-04 - accuracy: 0.9999 - val_loss: 0.1254 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.36181143 -0.72182786 -0.35662398] Sparsity at: 0.028493613824192337 Epoch 327/500 235/235 [==============================] - 4s 15ms/step - loss: 3.4500e-05 - accuracy: 1.0000 - val_loss: 0.1227 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.36059892 -0.720914 -0.35696462] Sparsity at: 0.028493613824192337 Epoch 328/500 235/235 [==============================] - 4s 15ms/step - loss: 5.8631e-05 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.36781707 -0.719358 -0.3546518 ] Sparsity at: 0.028493613824192337 Epoch 329/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9531e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.36835074 -0.71910983 -0.3550411 ] Sparsity at: 0.028493613824192337 Epoch 330/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1966e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.36956447 -0.72084105 -0.35652372] Sparsity at: 0.028493613824192337 Epoch 331/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8495e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.37962177 -0.7226904 -0.35691985] Sparsity at: 0.028493613824192337 Epoch 332/500 235/235 [==============================] - 3s 15ms/step - loss: 9.1731e-06 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.37871665 -0.72195965 -0.3568013 ] Sparsity at: 0.028493613824192337 Epoch 333/500 235/235 [==============================] - 3s 15ms/step - loss: 7.1509e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.3799237 -0.7220545 -0.35781285] Sparsity at: 0.028493613824192337 Epoch 334/500 235/235 [==============================] - 3s 15ms/step - loss: 6.7008e-06 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.381627 -0.7225893 -0.35709974] Sparsity at: 0.028493613824192337 Epoch 335/500 235/235 [==============================] - 4s 15ms/step - loss: 5.2533e-06 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.38215622 -0.72276706 -0.3575551 ] Sparsity at: 0.028493613824192337 Epoch 336/500 235/235 [==============================] - 3s 15ms/step - loss: 4.8165e-06 - accuracy: 1.0000 - val_loss: 0.1212 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.382356 -0.7228786 -0.35783023] Sparsity at: 0.028493613824192337 Epoch 337/500 235/235 [==============================] - 3s 15ms/step - loss: 4.2426e-06 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.38277435 -0.72347313 -0.357764 ] Sparsity at: 0.028493613824192337 Epoch 338/500 235/235 [==============================] - 3s 15ms/step - loss: 3.9939e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.38326 -0.7238172 -0.35875434] Sparsity at: 0.028493613824192337 Epoch 339/500 235/235 [==============================] - 3s 15ms/step - loss: 3.4883e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.3835495 -0.72423416 -0.35791403] Sparsity at: 0.028493613824192337 Epoch 340/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1003e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.38385186 -0.7244328 -0.358441 ] Sparsity at: 0.028493613824192337 Epoch 341/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5739e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.3844345 -0.7248448 -0.35858193] Sparsity at: 0.028493613824192337 Epoch 342/500 235/235 [==============================] - 3s 15ms/step - loss: 4.8975e-06 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.3841586 -0.7254179 -0.3581803 ] Sparsity at: 0.028493613824192337 Epoch 343/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7493e-06 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.38512635 -0.72603214 -0.3577346 ] Sparsity at: 0.028493613824192337 Epoch 344/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1948e-05 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.3869214 -0.7258471 -0.37336922] Sparsity at: 0.028493613824192337 Epoch 345/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0075 - accuracy: 0.9979 - val_loss: 0.1408 - val_accuracy: 0.9807 [-0.05253947 -0.00531845 -0.04093379 ... 0.39259848 -0.7517546 -0.31886986] Sparsity at: 0.028493613824192337 Epoch 346/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1267 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.41263568 -0.7569819 -0.33305418] Sparsity at: 0.028493613824192337 Epoch 347/500 235/235 [==============================] - 3s 15ms/step - loss: 6.0096e-04 - accuracy: 0.9998 - val_loss: 0.1229 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.40937153 -0.75262415 -0.32613665] Sparsity at: 0.028493613824192337 Epoch 348/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8938e-04 - accuracy: 0.9999 - val_loss: 0.1210 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.41916695 -0.7529874 -0.33452314] Sparsity at: 0.028493613824192337 Epoch 349/500 235/235 [==============================] - 4s 15ms/step - loss: 1.3218e-04 - accuracy: 0.9999 - val_loss: 0.1240 - val_accuracy: 0.9836 [-0.05253947 -0.00531845 -0.04093379 ... 0.4184474 -0.7554464 -0.33194628] Sparsity at: 0.028493613824192337 Epoch 350/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0932e-04 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.41884583 -0.7566115 -0.33458635] Sparsity at: 0.028493613824192337 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.6035266542716471 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.5966045748912734 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.806086159922458 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 193s 12ms/step - loss: 1.1876e-04 - accuracy: 1.0000 - val_loss: 0.1220 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.40496027 -0.7561971 -0.33538452] Sparsity at: 0.028493613824192337 Epoch 352/500 235/235 [==============================] - 3s 15ms/step - loss: 9.1282e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.4057338 -0.7565623 -0.33624917] Sparsity at: 0.028493613824192337 Epoch 353/500 235/235 [==============================] - 4s 17ms/step - loss: 3.9320e-05 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.40804118 -0.7553667 -0.33572304] Sparsity at: 0.028493613824192337 Epoch 354/500 235/235 [==============================] - 3s 15ms/step - loss: 4.4464e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.40909913 -0.75635564 -0.33643073] Sparsity at: 0.028493613824192337 Epoch 355/500 235/235 [==============================] - 3s 15ms/step - loss: 4.6219e-04 - accuracy: 0.9999 - val_loss: 0.1215 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.40938306 -0.76063615 -0.33669677] Sparsity at: 0.028493613824192337 Epoch 356/500 235/235 [==============================] - 3s 15ms/step - loss: 7.0665e-05 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.41290727 -0.763049 -0.3406392 ] Sparsity at: 0.028493613824192337 Epoch 357/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4436e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.42531255 -0.76381063 -0.3416379 ] Sparsity at: 0.028493613824192337 Epoch 358/500 235/235 [==============================] - 4s 15ms/step - loss: 9.9218e-06 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9842 [-0.05253947 -0.00531845 -0.04093379 ... 0.4251939 -0.7650657 -0.34163862] Sparsity at: 0.028493613824192337 Epoch 359/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0558e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.4253335 -0.7650991 -0.34193328] Sparsity at: 0.028493613824192337 Epoch 360/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0357e-05 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.425843 -0.76449645 -0.3407809 ] Sparsity at: 0.028493613824192337 Epoch 361/500 235/235 [==============================] - 3s 15ms/step - loss: 9.0911e-06 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.42608228 -0.76490545 -0.34106064] Sparsity at: 0.028493613824192337 Epoch 362/500 235/235 [==============================] - 3s 15ms/step - loss: 9.5807e-06 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.42626745 -0.76388735 -0.34287342] Sparsity at: 0.028493613824192337 Epoch 363/500 235/235 [==============================] - 4s 15ms/step - loss: 6.8157e-06 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.42629826 -0.7629148 -0.34240225] Sparsity at: 0.028493613824192337 Epoch 364/500 235/235 [==============================] - 3s 15ms/step - loss: 6.3536e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.42753306 -0.7632398 -0.3426891 ] Sparsity at: 0.028493613824192337 Epoch 365/500 235/235 [==============================] - 3s 15ms/step - loss: 5.2781e-06 - accuracy: 1.0000 - val_loss: 0.1223 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.42799413 -0.7662989 -0.34190804] Sparsity at: 0.028493613824192337 Epoch 366/500 235/235 [==============================] - 4s 15ms/step - loss: 4.2840e-06 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.42803007 -0.7648808 -0.3418253 ] Sparsity at: 0.028493613824192337 Epoch 367/500 235/235 [==============================] - 3s 15ms/step - loss: 4.9721e-06 - accuracy: 1.0000 - val_loss: 0.1223 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.42920774 -0.7696367 -0.34177244] Sparsity at: 0.028493613824192337 Epoch 368/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1740e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.4470256 -0.76815087 -0.33886737] Sparsity at: 0.028493613824192337 Epoch 369/500 235/235 [==============================] - 4s 15ms/step - loss: 7.0091e-06 - accuracy: 1.0000 - val_loss: 0.1202 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.44155318 -0.7667727 -0.34044573] Sparsity at: 0.028493613824192337 Epoch 370/500 235/235 [==============================] - 4s 15ms/step - loss: 4.2506e-06 - accuracy: 1.0000 - val_loss: 0.1208 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.44186962 -0.76630586 -0.3439008 ] Sparsity at: 0.028493613824192337 Epoch 371/500 235/235 [==============================] - 3s 15ms/step - loss: 3.3281e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.44129005 -0.76495844 -0.34414202] Sparsity at: 0.028493613824192337 Epoch 372/500 235/235 [==============================] - 4s 15ms/step - loss: 3.1902e-06 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.4415182 -0.76280767 -0.34461823] Sparsity at: 0.028493613824192337 Epoch 373/500 235/235 [==============================] - 4s 15ms/step - loss: 2.4547e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.44152284 -0.7636279 -0.3447222 ] Sparsity at: 0.028493613824192337 Epoch 374/500 235/235 [==============================] - 4s 15ms/step - loss: 1.7559e-06 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.44142306 -0.7631707 -0.34493458] Sparsity at: 0.028493613824192337 Epoch 375/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8557e-06 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.44151977 -0.7655093 -0.34511808] Sparsity at: 0.028493613824192337 Epoch 376/500 235/235 [==============================] - 4s 15ms/step - loss: 1.6447e-06 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.44126698 -0.76486504 -0.3454091 ] Sparsity at: 0.028493613824192337 Epoch 377/500 235/235 [==============================] - 4s 15ms/step - loss: 1.4869e-06 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.4414364 -0.7650728 -0.34554008] Sparsity at: 0.028493613824192337 Epoch 378/500 235/235 [==============================] - 3s 15ms/step - loss: 1.4741e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9846 [-0.05253947 -0.00531845 -0.04093379 ... 0.44173425 -0.7651301 -0.34639227] Sparsity at: 0.028493613824192337 Epoch 379/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5288e-06 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.43972218 -0.76450163 -0.34711185] Sparsity at: 0.028493613824192337 Epoch 380/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2850e-06 - accuracy: 1.0000 - val_loss: 0.1234 - val_accuracy: 0.9848 [-0.05253947 -0.00531845 -0.04093379 ... 0.43977132 -0.7643255 -0.3479063 ] Sparsity at: 0.028493613824192337 Epoch 381/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0341e-06 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9848 [-0.05253947 -0.00531845 -0.04093379 ... 0.44075084 -0.7652613 -0.34915864] Sparsity at: 0.028493613824192337 Epoch 382/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2714e-06 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.44046453 -0.7654178 -0.34843224] Sparsity at: 0.028493613824192337 Epoch 383/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.1584 - val_accuracy: 0.9805 [-0.05253947 -0.00531845 -0.04093379 ... 0.37593022 -0.7279469 -0.34776244] Sparsity at: 0.028493613824192337 Epoch 384/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1349 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.3572379 -0.7183091 -0.33100137] Sparsity at: 0.028493613824192337 Epoch 385/500 235/235 [==============================] - 3s 15ms/step - loss: 6.8026e-04 - accuracy: 0.9998 - val_loss: 0.1348 - val_accuracy: 0.9817 [-0.05253947 -0.00531845 -0.04093379 ... 0.3636522 -0.71742594 -0.33558244] Sparsity at: 0.028493613824192337 Epoch 386/500 235/235 [==============================] - 3s 15ms/step - loss: 1.9572e-04 - accuracy: 0.9999 - val_loss: 0.1358 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.36515898 -0.71582264 -0.344017 ] Sparsity at: 0.028493613824192337 Epoch 387/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0998e-04 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.36479947 -0.7172393 -0.3438424 ] Sparsity at: 0.028493613824192337 Epoch 388/500 235/235 [==============================] - 3s 15ms/step - loss: 4.1929e-05 - accuracy: 1.0000 - val_loss: 0.1288 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.3670163 -0.7181966 -0.34429932] Sparsity at: 0.028493613824192337 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7746e-05 - accuracy: 1.0000 - val_loss: 0.1267 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.3642123 -0.71691114 -0.34356996] Sparsity at: 0.028493613824192337 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5356e-05 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.3649509 -0.7170459 -0.34395397] Sparsity at: 0.028493613824192337 Epoch 391/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5297e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.3666767 -0.71747935 -0.34585875] Sparsity at: 0.028493613824192337 Epoch 392/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5690e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.36684766 -0.7191423 -0.3467173 ] Sparsity at: 0.028493613824192337 Epoch 393/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2209e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3676588 -0.71904194 -0.347862 ] Sparsity at: 0.028493613824192337 Epoch 394/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3318e-05 - accuracy: 1.0000 - val_loss: 0.1278 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.36802164 -0.71884024 -0.3482263 ] Sparsity at: 0.028493613824192337 Epoch 395/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1534e-04 - accuracy: 0.9999 - val_loss: 0.1315 - val_accuracy: 0.9818 [-0.05253947 -0.00531845 -0.04093379 ... 0.36987072 -0.7164113 -0.33295283] Sparsity at: 0.028493613824192337 Epoch 396/500 235/235 [==============================] - 4s 15ms/step - loss: 6.6981e-04 - accuracy: 0.9998 - val_loss: 0.1337 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.36594144 -0.70562077 -0.3321083 ] Sparsity at: 0.028493613824192337 Epoch 397/500 235/235 [==============================] - 4s 15ms/step - loss: 2.8309e-04 - accuracy: 0.9999 - val_loss: 0.1348 - val_accuracy: 0.9821 [-0.05253947 -0.00531845 -0.04093379 ... 0.36539268 -0.7097641 -0.33957472] Sparsity at: 0.028493613824192337 Epoch 398/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1821e-04 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9818 [-0.05253947 -0.00531845 -0.04093379 ... 0.36932504 -0.72061545 -0.3367694 ] Sparsity at: 0.028493613824192337 Epoch 399/500 235/235 [==============================] - 4s 15ms/step - loss: 7.8329e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.36894634 -0.71312803 -0.35170096] Sparsity at: 0.028493613824192337 Epoch 400/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8901e-05 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.3707043 -0.71456516 -0.3518203 ] Sparsity at: 0.028493613824192337 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.6749689754423756 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6583286934458954 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25283334 tf.Tensor( [[1. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] ... [1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.867380159112713 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 194s 13ms/step - loss: 1.0437e-05 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.36941987 -0.7145854 -0.35241663] Sparsity at: 0.028493613824192337 Epoch 402/500 235/235 [==============================] - 3s 15ms/step - loss: 6.8259e-06 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.3706934 -0.71546215 -0.35319513] Sparsity at: 0.028493613824192337 Epoch 403/500 235/235 [==============================] - 3s 15ms/step - loss: 8.0533e-05 - accuracy: 0.9999 - val_loss: 0.1356 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.37244856 -0.72702944 -0.35329852] Sparsity at: 0.028493613824192337 Epoch 404/500 235/235 [==============================] - 3s 15ms/step - loss: 3.7139e-04 - accuracy: 0.9999 - val_loss: 0.1329 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.38429132 -0.709691 -0.36136505] Sparsity at: 0.028493613824192337 Epoch 405/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1541 - val_accuracy: 0.9805 [-0.05253947 -0.00531845 -0.04093379 ... 0.37165084 -0.7248993 -0.37113595] Sparsity at: 0.028493613824192337 Epoch 406/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.1271 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.388395 -0.72853774 -0.3750149 ] Sparsity at: 0.028493613824192337 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1107e-04 - accuracy: 0.9999 - val_loss: 0.1291 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.3936288 -0.7368216 -0.3659785 ] Sparsity at: 0.028493613824192337 Epoch 408/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3032e-04 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.39517137 -0.73421955 -0.37125292] Sparsity at: 0.028493613824192337 Epoch 409/500 235/235 [==============================] - 3s 15ms/step - loss: 7.0882e-05 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.39350152 -0.73168546 -0.36969844] Sparsity at: 0.028493613824192337 Epoch 410/500 235/235 [==============================] - 4s 15ms/step - loss: 1.8395e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.39331073 -0.7326898 -0.36909068] Sparsity at: 0.028493613824192337 Epoch 411/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0221e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.39668423 -0.7407682 -0.3688209 ] Sparsity at: 0.028493613824192337 Epoch 412/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1756e-04 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.39496773 -0.748358 -0.36587054] Sparsity at: 0.028493613824192337 Epoch 413/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0969e-04 - accuracy: 0.9999 - val_loss: 0.1309 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.3937466 -0.7452521 -0.36350653] Sparsity at: 0.028493613824192337 Epoch 414/500 235/235 [==============================] - 3s 15ms/step - loss: 5.1131e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9840 [-0.05253947 -0.00531845 -0.04093379 ... 0.39799434 -0.7531699 -0.35590062] Sparsity at: 0.028493613824192337 Epoch 415/500 235/235 [==============================] - 3s 15ms/step - loss: 3.2250e-04 - accuracy: 0.9999 - val_loss: 0.1327 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.40281758 -0.7412514 -0.36725128] Sparsity at: 0.028493613824192337 Epoch 416/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3050e-04 - accuracy: 0.9999 - val_loss: 0.1401 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.410807 -0.7416229 -0.3651779 ] Sparsity at: 0.028493613824192337 Epoch 417/500 235/235 [==============================] - 3s 15ms/step - loss: 3.2940e-04 - accuracy: 0.9999 - val_loss: 0.1339 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.40051156 -0.7577681 -0.36604753] Sparsity at: 0.028493613824192337 Epoch 418/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6324e-04 - accuracy: 0.9999 - val_loss: 0.1343 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.3969749 -0.7639135 -0.36612716] Sparsity at: 0.028493613824192337 Epoch 419/500 235/235 [==============================] - 3s 15ms/step - loss: 4.2030e-04 - accuracy: 0.9998 - val_loss: 0.1433 - val_accuracy: 0.9819 [-0.05253947 -0.00531845 -0.04093379 ... 0.40755168 -0.7586378 -0.34532246] Sparsity at: 0.028493613824192337 Epoch 420/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1443 - val_accuracy: 0.9811 [-0.05253947 -0.00531845 -0.04093379 ... 0.4134971 -0.7450418 -0.3199105 ] Sparsity at: 0.028493613824192337 Epoch 421/500 235/235 [==============================] - 4s 15ms/step - loss: 3.4537e-04 - accuracy: 0.9999 - val_loss: 0.1297 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.4034531 -0.7347705 -0.32510707] Sparsity at: 0.028493613824192337 Epoch 422/500 235/235 [==============================] - 4s 15ms/step - loss: 3.7446e-04 - accuracy: 0.9999 - val_loss: 0.1259 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.41830692 -0.7428895 -0.32144773] Sparsity at: 0.028493613824192337 Epoch 423/500 235/235 [==============================] - 4s 15ms/step - loss: 4.1874e-04 - accuracy: 0.9999 - val_loss: 0.1276 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.40457782 -0.7468607 -0.3210215 ] Sparsity at: 0.028493613824192337 Epoch 424/500 235/235 [==============================] - 4s 15ms/step - loss: 5.7915e-05 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.40241107 -0.7481065 -0.32221124] Sparsity at: 0.028493613824192337 Epoch 425/500 235/235 [==============================] - 4s 15ms/step - loss: 2.5385e-05 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.402792 -0.75297254 -0.32371187] Sparsity at: 0.028493613824192337 Epoch 426/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5728e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.41689238 -0.75832766 -0.32438895] Sparsity at: 0.028493613824192337 Epoch 427/500 235/235 [==============================] - 4s 15ms/step - loss: 7.3024e-06 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9830 [-0.05253947 -0.00531845 -0.04093379 ... 0.41739288 -0.7578309 -0.3249548 ] Sparsity at: 0.028493613824192337 Epoch 428/500 235/235 [==============================] - 3s 15ms/step - loss: 5.8485e-06 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.41723558 -0.7582679 -0.32522917] Sparsity at: 0.028493613824192337 Epoch 429/500 235/235 [==============================] - 4s 15ms/step - loss: 8.5290e-06 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.41852644 -0.7586658 -0.3277146 ] Sparsity at: 0.028493613824192337 Epoch 430/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3686e-06 - accuracy: 1.0000 - val_loss: 0.1272 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.4111333 -0.7587286 -0.327701 ] Sparsity at: 0.028493613824192337 Epoch 431/500 235/235 [==============================] - 4s 15ms/step - loss: 3.7925e-06 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.4117323 -0.7587867 -0.32863724] Sparsity at: 0.028493613824192337 Epoch 432/500 235/235 [==============================] - 3s 15ms/step - loss: 3.9764e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.41311213 -0.7581386 -0.3299222 ] Sparsity at: 0.028493613824192337 Epoch 433/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0544e-04 - accuracy: 0.9999 - val_loss: 0.1408 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.42093828 -0.76601624 -0.33279094] Sparsity at: 0.028493613824192337 Epoch 434/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6124e-05 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.42030528 -0.75663525 -0.33137178] Sparsity at: 0.028493613824192337 Epoch 435/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7915e-04 - accuracy: 0.9999 - val_loss: 0.1350 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.41798434 -0.76034266 -0.3319928 ] Sparsity at: 0.028493613824192337 Epoch 436/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1438 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.43176204 -0.77266973 -0.33652893] Sparsity at: 0.028493613824192337 Epoch 437/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.1708 - val_accuracy: 0.9784 [-0.05253947 -0.00531845 -0.04093379 ... 0.44342205 -0.75575805 -0.33219737] Sparsity at: 0.028493613824192337 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 0.1501 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.45365125 -0.76816237 -0.3337154 ] Sparsity at: 0.028493613824192337 Epoch 439/500 235/235 [==============================] - 4s 16ms/step - loss: 1.7110e-04 - accuracy: 0.9999 - val_loss: 0.1412 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.45614928 -0.7660927 -0.33307832] Sparsity at: 0.028493613824192337 Epoch 440/500 235/235 [==============================] - 3s 15ms/step - loss: 6.3487e-05 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9843 [-0.05253947 -0.00531845 -0.04093379 ... 0.45245424 -0.7664272 -0.3291382 ] Sparsity at: 0.028493613824192337 Epoch 441/500 235/235 [==============================] - 3s 15ms/step - loss: 2.8139e-05 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.45102564 -0.7675537 -0.32854694] Sparsity at: 0.028493613824192337 Epoch 442/500 235/235 [==============================] - 3s 15ms/step - loss: 2.8337e-05 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9841 [-0.05253947 -0.00531845 -0.04093379 ... 0.46775946 -0.7678541 -0.34251916] Sparsity at: 0.028493613824192337 Epoch 443/500 235/235 [==============================] - 3s 15ms/step - loss: 3.3209e-05 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9848 [-0.05253947 -0.00531845 -0.04093379 ... 0.46768463 -0.76743776 -0.3403029 ] Sparsity at: 0.028493613824192337 Epoch 444/500 235/235 [==============================] - 4s 15ms/step - loss: 8.6267e-06 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9847 [-0.05253947 -0.00531845 -0.04093379 ... 0.4667296 -0.7669822 -0.33983156] Sparsity at: 0.028493613824192337 Epoch 445/500 235/235 [==============================] - 3s 15ms/step - loss: 9.4922e-06 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.46635967 -0.76714885 -0.33959824] Sparsity at: 0.028493613824192337 Epoch 446/500 235/235 [==============================] - 4s 15ms/step - loss: 6.1517e-06 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.4669156 -0.7671888 -0.33941928] Sparsity at: 0.028493613824192337 Epoch 447/500 235/235 [==============================] - 3s 15ms/step - loss: 5.0258e-06 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9845 [-0.05253947 -0.00531845 -0.04093379 ... 0.46693552 -0.7674959 -0.3403831 ] Sparsity at: 0.028493613824192337 Epoch 448/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5125e-05 - accuracy: 1.0000 - val_loss: 0.1322 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.46668315 -0.7703708 -0.34074643] Sparsity at: 0.028493613824192337 Epoch 449/500 235/235 [==============================] - 3s 15ms/step - loss: 2.8842e-04 - accuracy: 0.9999 - val_loss: 0.1371 - val_accuracy: 0.9837 [-0.05253947 -0.00531845 -0.04093379 ... 0.45929548 -0.76702774 -0.3376823 ] Sparsity at: 0.028493613824192337 Epoch 450/500 235/235 [==============================] - 3s 15ms/step - loss: 6.6382e-04 - accuracy: 0.9998 - val_loss: 0.1516 - val_accuracy: 0.9809 [-0.05253947 -0.00531845 -0.04093379 ... 0.4464498 -0.785883 -0.3476013 ] Sparsity at: 0.028493613824192337 Epoch 451/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.1479 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.43447194 -0.7841925 -0.36065876] Sparsity at: 0.028493613824192337 Epoch 452/500 235/235 [==============================] - 3s 15ms/step - loss: 7.1483e-04 - accuracy: 0.9997 - val_loss: 0.1364 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.4234038 -0.77283496 -0.35752153] Sparsity at: 0.028493613824192337 Epoch 453/500 235/235 [==============================] - 3s 15ms/step - loss: 3.7847e-04 - accuracy: 0.9998 - val_loss: 0.1419 - val_accuracy: 0.9822 [-0.05253947 -0.00531845 -0.04093379 ... 0.4540731 -0.7770975 -0.35945258] Sparsity at: 0.028493613824192337 Epoch 454/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2748e-04 - accuracy: 1.0000 - val_loss: 0.1379 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.45216873 -0.7857477 -0.37768865] Sparsity at: 0.028493613824192337 Epoch 455/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1552e-04 - accuracy: 1.0000 - val_loss: 0.1363 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.45357776 -0.78326845 -0.37596932] Sparsity at: 0.028493613824192337 Epoch 456/500 235/235 [==============================] - 4s 15ms/step - loss: 1.4910e-04 - accuracy: 1.0000 - val_loss: 0.1346 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.45296377 -0.7869139 -0.37749755] Sparsity at: 0.028493613824192337 Epoch 457/500 235/235 [==============================] - 3s 15ms/step - loss: 4.4171e-05 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.45378244 -0.78569096 -0.37550688] Sparsity at: 0.028493613824192337 Epoch 458/500 235/235 [==============================] - 4s 15ms/step - loss: 5.2295e-05 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.45438558 -0.7843429 -0.37581572] Sparsity at: 0.028493613824192337 Epoch 459/500 235/235 [==============================] - 3s 15ms/step - loss: 2.3513e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.44743505 -0.7828709 -0.37561774] Sparsity at: 0.028493613824192337 Epoch 460/500 235/235 [==============================] - 4s 15ms/step - loss: 1.0521e-05 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9844 [-0.05253947 -0.00531845 -0.04093379 ... 0.4481748 -0.78239226 -0.38164914] Sparsity at: 0.028493613824192337 Epoch 461/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1733e-05 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9839 [-0.05253947 -0.00531845 -0.04093379 ... 0.44826797 -0.7827413 -0.37476727] Sparsity at: 0.028493613824192337 Epoch 462/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1007e-04 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.4491203 -0.7834212 -0.380703 ] Sparsity at: 0.028493613824192337 Epoch 463/500 235/235 [==============================] - 3s 15ms/step - loss: 4.7313e-04 - accuracy: 0.9999 - val_loss: 0.1573 - val_accuracy: 0.9807 [-0.05253947 -0.00531845 -0.04093379 ... 0.44775844 -0.7974664 -0.37249154] Sparsity at: 0.028493613824192337 Epoch 464/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9944e-04 - accuracy: 0.9999 - val_loss: 0.1434 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.46233323 -0.7907427 -0.40250686] Sparsity at: 0.028493613824192337 Epoch 465/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0314e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9815 [-0.05253947 -0.00531845 -0.04093379 ... 0.48241386 -0.77869844 -0.43053284] Sparsity at: 0.028493613824192337 Epoch 466/500 235/235 [==============================] - 3s 15ms/step - loss: 5.9290e-04 - accuracy: 0.9998 - val_loss: 0.1502 - val_accuracy: 0.9813 [-0.05253947 -0.00531845 -0.04093379 ... 0.48071018 -0.77188385 -0.40305296] Sparsity at: 0.028493613824192337 Epoch 467/500 235/235 [==============================] - 3s 15ms/step - loss: 2.5307e-04 - accuracy: 0.9999 - val_loss: 0.1560 - val_accuracy: 0.9805 [-0.05253947 -0.00531845 -0.04093379 ... 0.48189926 -0.7822515 -0.4155562 ] Sparsity at: 0.028493613824192337 Epoch 468/500 235/235 [==============================] - 4s 16ms/step - loss: 2.5292e-04 - accuracy: 0.9999 - val_loss: 0.1468 - val_accuracy: 0.9823 [-0.05253947 -0.00531845 -0.04093379 ... 0.4805368 -0.7811953 -0.41130468] Sparsity at: 0.028493613824192337 Epoch 469/500 235/235 [==============================] - 4s 16ms/step - loss: 7.7404e-05 - accuracy: 1.0000 - val_loss: 0.1470 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.47709835 -0.77943796 -0.41125587] Sparsity at: 0.028493613824192337 Epoch 470/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3709e-04 - accuracy: 0.9998 - val_loss: 0.1526 - val_accuracy: 0.9818 [-0.05253947 -0.00531845 -0.04093379 ... 0.4774776 -0.77366155 -0.41163024] Sparsity at: 0.028493613824192337 Epoch 471/500 235/235 [==============================] - 3s 15ms/step - loss: 4.4727e-04 - accuracy: 0.9999 - val_loss: 0.1575 - val_accuracy: 0.9812 [-0.05253947 -0.00531845 -0.04093379 ... 0.47658587 -0.7739178 -0.40960613] Sparsity at: 0.028493613824192337 Epoch 472/500 235/235 [==============================] - 3s 15ms/step - loss: 2.6488e-04 - accuracy: 1.0000 - val_loss: 0.1489 - val_accuracy: 0.9815 [-0.05253947 -0.00531845 -0.04093379 ... 0.47049978 -0.76471066 -0.40972468] Sparsity at: 0.028493613824192337 Epoch 473/500 235/235 [==============================] - 4s 15ms/step - loss: 3.7643e-05 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.4682524 -0.7636404 -0.40440422] Sparsity at: 0.028493613824192337 Epoch 474/500 235/235 [==============================] - 3s 15ms/step - loss: 3.9213e-05 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9819 [-0.05253947 -0.00531845 -0.04093379 ... 0.46853176 -0.76619816 -0.41043293] Sparsity at: 0.028493613824192337 Epoch 475/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9909e-05 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 0.9824 [-0.05253947 -0.00531845 -0.04093379 ... 0.4769757 -0.7653083 -0.4103344 ] Sparsity at: 0.028493613824192337 Epoch 476/500 235/235 [==============================] - 3s 15ms/step - loss: 8.4159e-05 - accuracy: 1.0000 - val_loss: 0.1474 - val_accuracy: 0.9820 [-0.05253947 -0.00531845 -0.04093379 ... 0.47163013 -0.7642421 -0.40839946] Sparsity at: 0.028493613824192337 Epoch 477/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.1547 - val_accuracy: 0.9814 [-0.05253947 -0.00531845 -0.04093379 ... 0.47469926 -0.7604162 -0.4080412 ] Sparsity at: 0.028493613824192337 Epoch 478/500 235/235 [==============================] - 4s 15ms/step - loss: 8.5478e-04 - accuracy: 0.9998 - val_loss: 0.1524 - val_accuracy: 0.9815 [-0.05253947 -0.00531845 -0.04093379 ... 0.47705188 -0.7633717 -0.43099895] Sparsity at: 0.028493613824192337 Epoch 479/500 235/235 [==============================] - 3s 15ms/step - loss: 4.1060e-04 - accuracy: 0.9998 - val_loss: 0.1554 - val_accuracy: 0.9813 [-0.05253947 -0.00531845 -0.04093379 ... 0.4762578 -0.7704749 -0.43019977] Sparsity at: 0.028493613824192337 Epoch 480/500 235/235 [==============================] - 4s 15ms/step - loss: 3.3028e-04 - accuracy: 0.9999 - val_loss: 0.1489 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.46161947 -0.7570636 -0.42566922] Sparsity at: 0.028493613824192337 Epoch 481/500 235/235 [==============================] - 4s 15ms/step - loss: 4.6733e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9812 [-0.05253947 -0.00531845 -0.04093379 ... 0.50503606 -0.7605507 -0.41889885] Sparsity at: 0.028493613824192337 Epoch 482/500 235/235 [==============================] - 3s 15ms/step - loss: 8.6350e-05 - accuracy: 1.0000 - val_loss: 0.1402 - val_accuracy: 0.9828 [-0.05253947 -0.00531845 -0.04093379 ... 0.49830016 -0.76539403 -0.42194146] Sparsity at: 0.028493613824192337 Epoch 483/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9361e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.4935826 -0.76682633 -0.42509323] Sparsity at: 0.028493613824192337 Epoch 484/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1918e-05 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9827 [-0.05253947 -0.00531845 -0.04093379 ... 0.49565995 -0.76823354 -0.42688572] Sparsity at: 0.028493613824192337 Epoch 485/500 235/235 [==============================] - 3s 15ms/step - loss: 7.4953e-05 - accuracy: 0.9999 - val_loss: 0.1393 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.49147967 -0.77344 -0.42743585] Sparsity at: 0.028493613824192337 Epoch 486/500 235/235 [==============================] - 3s 15ms/step - loss: 4.1151e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9825 [-0.05253947 -0.00531845 -0.04093379 ... 0.49260774 -0.7732432 -0.42564327] Sparsity at: 0.028493613824192337 Epoch 487/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1632 - val_accuracy: 0.9803 [-0.05253947 -0.00531845 -0.04093379 ... 0.5001445 -0.79058915 -0.37974396] Sparsity at: 0.028493613824192337 Epoch 488/500 235/235 [==============================] - 3s 15ms/step - loss: 7.9429e-04 - accuracy: 0.9998 - val_loss: 0.1464 - val_accuracy: 0.9832 [-0.05253947 -0.00531845 -0.04093379 ... 0.47101036 -0.7862826 -0.37028828] Sparsity at: 0.028493613824192337 Epoch 489/500 235/235 [==============================] - 4s 15ms/step - loss: 5.0195e-04 - accuracy: 0.9998 - val_loss: 0.1496 - val_accuracy: 0.9819 [-0.05253947 -0.00531845 -0.04093379 ... 0.4698597 -0.7996573 -0.3700886 ] Sparsity at: 0.028493613824192337 Epoch 490/500 235/235 [==============================] - 4s 15ms/step - loss: 7.0435e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9833 [-0.05253947 -0.00531845 -0.04093379 ... 0.47072944 -0.79275405 -0.37202716] Sparsity at: 0.028493613824192337 Epoch 491/500 235/235 [==============================] - 4s 15ms/step - loss: 1.7996e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.47048974 -0.7935129 -0.37207144] Sparsity at: 0.028493613824192337 Epoch 492/500 235/235 [==============================] - 3s 15ms/step - loss: 6.8346e-06 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9835 [-0.05253947 -0.00531845 -0.04093379 ... 0.4697649 -0.79402566 -0.37249804] Sparsity at: 0.028493613824192337 Epoch 493/500 235/235 [==============================] - 4s 15ms/step - loss: 9.9476e-06 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9838 [-0.05253947 -0.00531845 -0.04093379 ... 0.46975625 -0.79390025 -0.37281314] Sparsity at: 0.028493613824192337 Epoch 494/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1428e-05 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9834 [-0.05253947 -0.00531845 -0.04093379 ... 0.46974587 -0.7925006 -0.37250876] Sparsity at: 0.028493613824192337 Epoch 495/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3169e-04 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9829 [-0.05253947 -0.00531845 -0.04093379 ... 0.47021708 -0.7950469 -0.37293392] Sparsity at: 0.028493613824192337 Epoch 496/500 235/235 [==============================] - 3s 15ms/step - loss: 7.1532e-05 - accuracy: 1.0000 - val_loss: 0.1480 - val_accuracy: 0.9821 [-0.05253947 -0.00531845 -0.04093379 ... 0.46999153 -0.79871917 -0.37485737] Sparsity at: 0.028493613824192337 Epoch 497/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7961e-04 - accuracy: 0.9999 - val_loss: 0.1502 - val_accuracy: 0.9826 [-0.05253947 -0.00531845 -0.04093379 ... 0.48269853 -0.80003166 -0.35478765] Sparsity at: 0.028493613824192337 Epoch 498/500 235/235 [==============================] - 3s 15ms/step - loss: 5.0421e-04 - accuracy: 0.9998 - val_loss: 0.1569 - val_accuracy: 0.9821 [-0.05253947 -0.00531845 -0.04093379 ... 0.48291698 -0.79664034 -0.3223419 ] Sparsity at: 0.028493613824192337 Epoch 499/500 235/235 [==============================] - 3s 15ms/step - loss: 5.3367e-04 - accuracy: 0.9998 - val_loss: 0.1659 - val_accuracy: 0.9805 [-0.05253947 -0.00531845 -0.04093379 ... 0.48295888 -0.78966117 -0.35997248] Sparsity at: 0.028493613824192337 Epoch 500/500 235/235 [==============================] - 3s 15ms/step - loss: 4.8086e-04 - accuracy: 0.9998 - val_loss: 0.1496 - val_accuracy: 0.9831 [-0.05253947 -0.00531845 -0.04093379 ... 0.47987184 -0.78292745 -0.3698607 ] Sparsity at: 0.028493613824192337 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.041994860395789146 Thresholhold -0.05940218269824982 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.09000259265303612 Thresholhold 0.044793546199798584 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10164112225174904 Thresholhold -0.008578553795814514 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 1:00:57 - loss: 4.6249 - accuracy: 0.1055WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0070s vs `on_train_batch_begin` time: 2.5328s). Check your callbacks. 235/235 [==============================] - 18s 9ms/step - loss: 1.6116 - accuracy: 0.8512 - val_loss: 0.9586 - val_accuracy: 0.9011 [-5.1867892e-07 3.2510341e-09 -3.8373156e-07 ... -2.1742404e-02 7.4999206e-02 -7.3073901e-02] Sparsity at: 0.03389887339055794 Epoch 2/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9035 - accuracy: 0.8962 - val_loss: 0.8544 - val_accuracy: 0.9004 [-1.63640846e-12 -1.11412697e-13 1.27109055e-12 ... -5.66245578e-02 7.01948553e-02 -1.02945901e-01] Sparsity at: 0.03389887339055794 Epoch 3/500 235/235 [==============================] - 2s 11ms/step - loss: 0.8593 - accuracy: 0.8970 - val_loss: 0.8384 - val_accuracy: 0.8985 [-1.814238e-17 2.594543e-19 7.712466e-18 ... -7.999009e-02 6.481430e-02 -1.186992e-01] Sparsity at: 0.03389887339055794 Epoch 4/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8474 - accuracy: 0.8968 - val_loss: 0.8284 - val_accuracy: 0.8982 [-9.4904549e-23 1.1852956e-24 3.2996739e-23 ... -9.8160937e-02 5.6796581e-02 -1.2494359e-01] Sparsity at: 0.03389887339055794 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8967 - val_loss: 0.8225 - val_accuracy: 0.8987 [ 8.7769503e-29 -6.4780971e-30 -7.7824018e-29 ... -1.0970281e-01 4.7417544e-02 -1.2560837e-01] Sparsity at: 0.03389887339055794 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8356 - accuracy: 0.8971 - val_loss: 0.8190 - val_accuracy: 0.8982 [-1.7431876e-33 3.0090966e-34 6.6428912e-34 ... -1.1537661e-01 3.6847204e-02 -1.2400684e-01] Sparsity at: 0.03389887339055794 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8325 - accuracy: 0.8972 - val_loss: 0.8149 - val_accuracy: 0.9000 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -1.1746345e-01 2.6333677e-02 -1.2117126e-01] Sparsity at: 0.03389887339055794 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8300 - accuracy: 0.8970 - val_loss: 0.8132 - val_accuracy: 0.8991 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -1.17063634e-01 1.55907189e-02 -1.17971309e-01] Sparsity at: 0.03389887339055794 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8283 - accuracy: 0.8974 - val_loss: 0.8120 - val_accuracy: 0.9001 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -1.15020871e-01 4.96495608e-03 -1.14879176e-01] Sparsity at: 0.03389887339055794 Epoch 10/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8267 - accuracy: 0.8973 - val_loss: 0.8095 - val_accuracy: 0.9002 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -1.12723984e-01 -5.65887056e-03 -1.11563139e-01] Sparsity at: 0.03389887339055794 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8256 - accuracy: 0.8972 - val_loss: 0.8075 - val_accuracy: 0.9013 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -1.0996724e-01 -1.6520964e-02 -1.0806606e-01] Sparsity at: 0.03389887339055794 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8244 - accuracy: 0.8974 - val_loss: 0.8076 - val_accuracy: 0.9008 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -1.0701427e-01 -2.7215760e-02 -1.0463226e-01] Sparsity at: 0.03389887339055794 Epoch 13/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8236 - accuracy: 0.8976 - val_loss: 0.8062 - val_accuracy: 0.9015 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -1.03802055e-01 -3.85156162e-02 -1.00779600e-01] Sparsity at: 0.03389887339055794 Epoch 14/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8231 - accuracy: 0.8977 - val_loss: 0.8048 - val_accuracy: 0.9016 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -1.0063963e-01 -4.9483795e-02 -9.7168446e-02] Sparsity at: 0.03389887339055794 Epoch 15/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8222 - accuracy: 0.8980 - val_loss: 0.8044 - val_accuracy: 0.9019 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -9.7607523e-02 -5.9485435e-02 -9.3539394e-02] Sparsity at: 0.03389887339055794 Epoch 16/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8220 - accuracy: 0.8983 - val_loss: 0.8041 - val_accuracy: 0.9014 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -9.4508007e-02 -6.8980098e-02 -9.0206429e-02] Sparsity at: 0.03389887339055794 Epoch 17/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8212 - accuracy: 0.8984 - val_loss: 0.8040 - val_accuracy: 0.9011 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -9.1799170e-02 -7.7286400e-02 -8.7064967e-02] Sparsity at: 0.03389887339055794 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8209 - accuracy: 0.8985 - val_loss: 0.8027 - val_accuracy: 0.9027 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -8.9337848e-02 -8.4185131e-02 -8.4532142e-02] Sparsity at: 0.03389887339055794 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8203 - accuracy: 0.8986 - val_loss: 0.8024 - val_accuracy: 0.9018 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -8.6802363e-02 -8.9671671e-02 -8.2026012e-02] Sparsity at: 0.03389887339055794 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8198 - accuracy: 0.8986 - val_loss: 0.8025 - val_accuracy: 0.9018 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -8.4220380e-02 -9.3932204e-02 -8.0264077e-02] Sparsity at: 0.03389887339055794 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8199 - accuracy: 0.8987 - val_loss: 0.8018 - val_accuracy: 0.9015 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -8.1991687e-02 -9.7548373e-02 -7.8438587e-02] Sparsity at: 0.03389887339055794 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8195 - accuracy: 0.8987 - val_loss: 0.8024 - val_accuracy: 0.9021 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -7.99612477e-02 -1.00618616e-01 -7.69459531e-02] Sparsity at: 0.03389887339055794 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8192 - accuracy: 0.8990 - val_loss: 0.8017 - val_accuracy: 0.9025 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -7.8238539e-02 -1.0330485e-01 -7.5646684e-02] Sparsity at: 0.03389887339055794 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8192 - accuracy: 0.8992 - val_loss: 0.8005 - val_accuracy: 0.9023 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -7.6306343e-02 -1.0567435e-01 -7.4780442e-02] Sparsity at: 0.03389887339055794 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8190 - accuracy: 0.8987 - val_loss: 0.8014 - val_accuracy: 0.9020 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -7.49229714e-02 -1.07650965e-01 -7.38429055e-02] Sparsity at: 0.03389887339055794 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8188 - accuracy: 0.8993 - val_loss: 0.8017 - val_accuracy: 0.9020 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -7.3448040e-02 -1.0967043e-01 -7.2962880e-02] Sparsity at: 0.03389887339055794 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8187 - accuracy: 0.8990 - val_loss: 0.8006 - val_accuracy: 0.9025 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -7.2161205e-02 -1.1192950e-01 -7.2087057e-02] Sparsity at: 0.03389887339055794 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8185 - accuracy: 0.8991 - val_loss: 0.8007 - val_accuracy: 0.9029 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -7.07969218e-02 -1.13647334e-01 -7.13215023e-02] Sparsity at: 0.03389887339055794 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8185 - accuracy: 0.8992 - val_loss: 0.8006 - val_accuracy: 0.9019 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.9616646e-02 -1.1501378e-01 -7.0356563e-02] Sparsity at: 0.03389887339055794 Epoch 30/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8184 - accuracy: 0.8989 - val_loss: 0.8005 - val_accuracy: 0.9029 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -6.82849288e-02 -1.16282366e-01 -6.96369335e-02] Sparsity at: 0.03389887339055794 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8183 - accuracy: 0.8989 - val_loss: 0.8011 - val_accuracy: 0.9022 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.7237139e-02 -1.1749545e-01 -6.8666451e-02] Sparsity at: 0.03389887339055794 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8179 - accuracy: 0.8992 - val_loss: 0.8012 - val_accuracy: 0.9021 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.5859020e-02 -1.1835413e-01 -6.7956232e-02] Sparsity at: 0.03389887339055794 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8180 - accuracy: 0.8992 - val_loss: 0.8007 - val_accuracy: 0.9027 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.4907841e-02 -1.1937189e-01 -6.7061514e-02] Sparsity at: 0.03389887339055794 Epoch 34/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8179 - accuracy: 0.8996 - val_loss: 0.8002 - val_accuracy: 0.9025 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.3616976e-02 -1.1994730e-01 -6.6348381e-02] Sparsity at: 0.03389887339055794 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8181 - accuracy: 0.8991 - val_loss: 0.7999 - val_accuracy: 0.9029 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -6.28290027e-02 -1.20415725e-01 -6.55044168e-02] Sparsity at: 0.03389887339055794 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8175 - accuracy: 0.8992 - val_loss: 0.8007 - val_accuracy: 0.9027 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.1789092e-02 -1.2052637e-01 -6.5280698e-02] Sparsity at: 0.03389887339055794 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8175 - accuracy: 0.8996 - val_loss: 0.8000 - val_accuracy: 0.9026 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.0971141e-02 -1.2054722e-01 -6.4772278e-02] Sparsity at: 0.03389887339055794 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8174 - accuracy: 0.8995 - val_loss: 0.8011 - val_accuracy: 0.9026 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -6.0116373e-02 -1.2057943e-01 -6.4296521e-02] Sparsity at: 0.03389887339055794 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8176 - accuracy: 0.8996 - val_loss: 0.8007 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.9313439e-02 -1.2035989e-01 -6.3906372e-02] Sparsity at: 0.03389887339055794 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8172 - accuracy: 0.8995 - val_loss: 0.8008 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.8514629e-02 -1.1996755e-01 -6.3588679e-02] Sparsity at: 0.03389887339055794 Epoch 41/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8173 - accuracy: 0.8996 - val_loss: 0.7999 - val_accuracy: 0.9033 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -5.76196797e-02 -1.19375385e-01 -6.33844733e-02] Sparsity at: 0.03389887339055794 Epoch 42/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8173 - accuracy: 0.8993 - val_loss: 0.8003 - val_accuracy: 0.9027 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -5.69621995e-02 -1.18990906e-01 -6.31420463e-02] Sparsity at: 0.03389887339055794 Epoch 43/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8170 - accuracy: 0.8996 - val_loss: 0.8009 - val_accuracy: 0.9024 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -5.61681837e-02 -1.18346445e-01 -6.29561394e-02] Sparsity at: 0.03389887339055794 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8170 - accuracy: 0.8997 - val_loss: 0.7997 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.5535611e-02 -1.1779005e-01 -6.2512837e-02] Sparsity at: 0.03389887339055794 Epoch 45/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8170 - accuracy: 0.8997 - val_loss: 0.8001 - val_accuracy: 0.9027 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.4861203e-02 -1.1697276e-01 -6.2190395e-02] Sparsity at: 0.03389887339055794 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8166 - accuracy: 0.8996 - val_loss: 0.7997 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.4020420e-02 -1.1622476e-01 -6.2079724e-02] Sparsity at: 0.03389887339055794 Epoch 47/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8167 - accuracy: 0.9000 - val_loss: 0.8005 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.3064734e-02 -1.1530566e-01 -6.1944101e-02] Sparsity at: 0.03389887339055794 Epoch 48/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8170 - accuracy: 0.8998 - val_loss: 0.8012 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.2496590e-02 -1.1481889e-01 -6.1551772e-02] Sparsity at: 0.03389887339055794 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8169 - accuracy: 0.8995 - val_loss: 0.8007 - val_accuracy: 0.9027 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.1630687e-02 -1.1380547e-01 -6.1330020e-02] Sparsity at: 0.03389887339055794 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8166 - accuracy: 0.8995 - val_loss: 0.8004 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.0834756e-02 -1.1296777e-01 -6.1183617e-02] Sparsity at: 0.03389887339055794 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.008841478533112124 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.029927758149965733 Thresholhold -0.03914691135287285 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.11400627827637067 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 52s 8ms/step - loss: 0.8169 - accuracy: 0.8995 - val_loss: 0.8006 - val_accuracy: 0.9026 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -5.0018761e-02 -1.1243409e-01 -6.0859196e-02] Sparsity at: 0.03389887339055794 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8167 - accuracy: 0.8995 - val_loss: 0.7997 - val_accuracy: 0.9029 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.9329411e-02 -1.1143783e-01 -6.0601756e-02] Sparsity at: 0.03389887339055794 Epoch 53/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8166 - accuracy: 0.8996 - val_loss: 0.8011 - val_accuracy: 0.9027 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.8642311e-02 -1.1079598e-01 -6.0539868e-02] Sparsity at: 0.03389887339055794 Epoch 54/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8166 - accuracy: 0.8998 - val_loss: 0.7996 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.8091397e-02 -1.0989093e-01 -6.0272042e-02] Sparsity at: 0.03389887339055794 Epoch 55/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8164 - accuracy: 0.9000 - val_loss: 0.8005 - val_accuracy: 0.9026 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.7489285e-02 -1.0932671e-01 -6.0045645e-02] Sparsity at: 0.03389887339055794 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8165 - accuracy: 0.8997 - val_loss: 0.7999 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.6922620e-02 -1.0863767e-01 -5.9877496e-02] Sparsity at: 0.03389887339055794 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8163 - accuracy: 0.8997 - val_loss: 0.8004 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.6353284e-02 -1.0794710e-01 -5.9844345e-02] Sparsity at: 0.03389887339055794 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8164 - accuracy: 0.8995 - val_loss: 0.8004 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.5732312e-02 -1.0721869e-01 -5.9489831e-02] Sparsity at: 0.03389887339055794 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8161 - accuracy: 0.8998 - val_loss: 0.7996 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.5561280e-02 -1.0671770e-01 -5.9577957e-02] Sparsity at: 0.03389887339055794 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8159 - accuracy: 0.8997 - val_loss: 0.7996 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.5070834e-02 -1.0609949e-01 -5.9505381e-02] Sparsity at: 0.03389887339055794 Epoch 61/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8160 - accuracy: 0.8999 - val_loss: 0.7998 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.4533413e-02 -1.0556256e-01 -5.9660539e-02] Sparsity at: 0.03389887339055794 Epoch 62/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8161 - accuracy: 0.8997 - val_loss: 0.8002 - val_accuracy: 0.9028 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.42719497e-02 -1.04760684e-01 -5.94633296e-02] Sparsity at: 0.03389887339055794 Epoch 63/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8160 - accuracy: 0.8999 - val_loss: 0.7999 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.4076074e-02 -1.0435625e-01 -5.9546530e-02] Sparsity at: 0.03389887339055794 Epoch 64/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8157 - accuracy: 0.8999 - val_loss: 0.8002 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.4097386e-02 -1.0384041e-01 -5.9653349e-02] Sparsity at: 0.03389887339055794 Epoch 65/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8159 - accuracy: 0.8999 - val_loss: 0.7992 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3751463e-02 -1.0322722e-01 -5.9759129e-02] Sparsity at: 0.03389887339055794 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8162 - accuracy: 0.8997 - val_loss: 0.7990 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3436848e-02 -1.0306304e-01 -5.9770711e-02] Sparsity at: 0.03389887339055794 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.9000 - val_loss: 0.7995 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3260247e-02 -1.0261369e-01 -5.9764374e-02] Sparsity at: 0.03389887339055794 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.9000 - val_loss: 0.7993 - val_accuracy: 0.9029 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3172728e-02 -1.0246329e-01 -5.9830181e-02] Sparsity at: 0.03389887339055794 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8158 - accuracy: 0.8998 - val_loss: 0.7987 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2952046e-02 -1.0223703e-01 -6.0060523e-02] Sparsity at: 0.03389887339055794 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.9005 - val_loss: 0.7998 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2847432e-02 -1.0210761e-01 -5.9955411e-02] Sparsity at: 0.03389887339055794 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8156 - accuracy: 0.9001 - val_loss: 0.8000 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2690706e-02 -1.0215598e-01 -5.9946191e-02] Sparsity at: 0.03389887339055794 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8157 - accuracy: 0.8998 - val_loss: 0.7999 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2593256e-02 -1.0200527e-01 -6.0073420e-02] Sparsity at: 0.03389887339055794 Epoch 73/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8158 - accuracy: 0.9000 - val_loss: 0.7997 - val_accuracy: 0.9035 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.25566882e-02 -1.02058426e-01 -6.01961277e-02] Sparsity at: 0.03389887339055794 Epoch 74/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8157 - accuracy: 0.9000 - val_loss: 0.7994 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2653352e-02 -1.0220907e-01 -6.0107101e-02] Sparsity at: 0.03389887339055794 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.9000 - val_loss: 0.7997 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2721152e-02 -1.0218142e-01 -5.9989989e-02] Sparsity at: 0.03389887339055794 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8156 - accuracy: 0.8997 - val_loss: 0.7993 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2699050e-02 -1.0206069e-01 -6.0101144e-02] Sparsity at: 0.03389887339055794 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8153 - accuracy: 0.9002 - val_loss: 0.7991 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2642426e-02 -1.0209481e-01 -5.9778787e-02] Sparsity at: 0.03389887339055794 Epoch 78/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8159 - accuracy: 0.9000 - val_loss: 0.7989 - val_accuracy: 0.9038 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.26993296e-02 -1.02029786e-01 -5.97770475e-02] Sparsity at: 0.03389887339055794 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.9002 - val_loss: 0.7997 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2686451e-02 -1.0229172e-01 -5.9739932e-02] Sparsity at: 0.03389887339055794 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.8999 - val_loss: 0.7991 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2758383e-02 -1.0228651e-01 -5.9687842e-02] Sparsity at: 0.03389887339055794 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8155 - accuracy: 0.9000 - val_loss: 0.7994 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2721070e-02 -1.0227813e-01 -5.9756547e-02] Sparsity at: 0.03389887339055794 Epoch 82/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8156 - accuracy: 0.8999 - val_loss: 0.7983 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2667691e-02 -1.0236472e-01 -5.9636872e-02] Sparsity at: 0.03389887339055794 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8153 - accuracy: 0.9002 - val_loss: 0.7993 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2820834e-02 -1.0246852e-01 -5.9721407e-02] Sparsity at: 0.03389887339055794 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7994 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3027382e-02 -1.0258234e-01 -5.9431273e-02] Sparsity at: 0.03389887339055794 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8152 - accuracy: 0.9000 - val_loss: 0.7989 - val_accuracy: 0.9036 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.30842824e-02 -1.02748506e-01 -5.93341328e-02] Sparsity at: 0.03389887339055794 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8152 - accuracy: 0.9003 - val_loss: 0.7986 - val_accuracy: 0.9041 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.32263575e-02 -1.02651976e-01 -5.92290089e-02] Sparsity at: 0.03389887339055794 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8152 - accuracy: 0.9002 - val_loss: 0.7989 - val_accuracy: 0.9037 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.30954695e-02 -1.02805525e-01 -5.92073500e-02] Sparsity at: 0.03389887339055794 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9001 - val_loss: 0.7987 - val_accuracy: 0.9035 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.29843776e-02 -1.02934405e-01 -5.94149083e-02] Sparsity at: 0.03389887339055794 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9001 - val_loss: 0.7984 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3267526e-02 -1.0295344e-01 -5.9377521e-02] Sparsity at: 0.03389887339055794 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9005 - val_loss: 0.7988 - val_accuracy: 0.9034 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.31034379e-02 -1.03300735e-01 -5.92039339e-02] Sparsity at: 0.03389887339055794 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7988 - val_accuracy: 0.9041 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3195322e-02 -1.0320137e-01 -5.9198733e-02] Sparsity at: 0.03389887339055794 Epoch 92/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8152 - accuracy: 0.9001 - val_loss: 0.7991 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3269783e-02 -1.0332963e-01 -5.9170473e-02] Sparsity at: 0.03389887339055794 Epoch 93/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3193411e-02 -1.0335717e-01 -5.9079077e-02] Sparsity at: 0.03389887339055794 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8150 - accuracy: 0.9000 - val_loss: 0.7990 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3360170e-02 -1.0341211e-01 -5.8922432e-02] Sparsity at: 0.03389887339055794 Epoch 95/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8150 - accuracy: 0.9003 - val_loss: 0.7990 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3186929e-02 -1.0355022e-01 -5.8969285e-02] Sparsity at: 0.03389887339055794 Epoch 96/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8154 - accuracy: 0.8998 - val_loss: 0.7993 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3404993e-02 -1.0351978e-01 -5.8893465e-02] Sparsity at: 0.03389887339055794 Epoch 97/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8150 - accuracy: 0.9002 - val_loss: 0.7990 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3365113e-02 -1.0374590e-01 -5.8799703e-02] Sparsity at: 0.03389887339055794 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9003 - val_loss: 0.7989 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3394301e-02 -1.0380616e-01 -5.8410849e-02] Sparsity at: 0.03389887339055794 Epoch 99/500 235/235 [==============================] - 2s 11ms/step - loss: 0.8152 - accuracy: 0.9001 - val_loss: 0.7988 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3228216e-02 -1.0352213e-01 -5.8603521e-02] Sparsity at: 0.03389887339055794 Epoch 100/500 235/235 [==============================] - 3s 12ms/step - loss: 0.8150 - accuracy: 0.9004 - val_loss: 0.7988 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3516897e-02 -1.0359441e-01 -5.8454055e-02] Sparsity at: 0.03389887339055794 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.014089003952281964 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.04218507345917066 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.13139845531020988 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 54s 8ms/step - loss: 0.8150 - accuracy: 0.9001 - val_loss: 0.7986 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3408722e-02 -1.0365215e-01 -5.8270115e-02] Sparsity at: 0.03389887339055794 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.8999 - val_loss: 0.7984 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3493111e-02 -1.0369851e-01 -5.8087692e-02] Sparsity at: 0.03389887339055794 Epoch 103/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9002 - val_loss: 0.7990 - val_accuracy: 0.9034 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.35344800e-02 -1.03766024e-01 -5.80144748e-02] Sparsity at: 0.03389887339055794 Epoch 104/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8151 - accuracy: 0.9004 - val_loss: 0.7991 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3485872e-02 -1.0388845e-01 -5.7728324e-02] Sparsity at: 0.03389887339055794 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9003 - val_loss: 0.7987 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3369964e-02 -1.0382074e-01 -5.7680707e-02] Sparsity at: 0.03389887339055794 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9002 - val_loss: 0.7987 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3414984e-02 -1.0373279e-01 -5.7621069e-02] Sparsity at: 0.03389887339055794 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9001 - val_loss: 0.7983 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3463595e-02 -1.0389680e-01 -5.7223033e-02] Sparsity at: 0.03389887339055794 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9002 - val_loss: 0.7981 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3469924e-02 -1.0386919e-01 -5.7355382e-02] Sparsity at: 0.03389887339055794 Epoch 109/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9004 - val_loss: 0.7986 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3421373e-02 -1.0379905e-01 -5.7199921e-02] Sparsity at: 0.03389887339055794 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.9002 - val_loss: 0.7993 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3324940e-02 -1.0367604e-01 -5.7209872e-02] Sparsity at: 0.03389887339055794 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8151 - accuracy: 0.9000 - val_loss: 0.7984 - val_accuracy: 0.9035 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.33785133e-02 -1.03679314e-01 -5.68143949e-02] Sparsity at: 0.03389887339055794 Epoch 112/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9000 - val_loss: 0.7981 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3366689e-02 -1.0370591e-01 -5.7007808e-02] Sparsity at: 0.03389887339055794 Epoch 113/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8147 - accuracy: 0.9005 - val_loss: 0.7982 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3441698e-02 -1.0359780e-01 -5.6747593e-02] Sparsity at: 0.03389887339055794 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9004 - val_loss: 0.7988 - val_accuracy: 0.9035 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.32605334e-02 -1.03409335e-01 -5.69705740e-02] Sparsity at: 0.03389887339055794 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9000 - val_loss: 0.7982 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3318138e-02 -1.0342982e-01 -5.6834307e-02] Sparsity at: 0.03389887339055794 Epoch 116/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8148 - accuracy: 0.9002 - val_loss: 0.7986 - val_accuracy: 0.9034 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.32412885e-02 -1.03304535e-01 -5.67218773e-02] Sparsity at: 0.03389887339055794 Epoch 117/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8151 - accuracy: 0.8998 - val_loss: 0.7981 - val_accuracy: 0.9037 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.32308204e-02 -1.03328034e-01 -5.65019250e-02] Sparsity at: 0.03389887339055794 Epoch 118/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8148 - accuracy: 0.9003 - val_loss: 0.7983 - val_accuracy: 0.9034 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.30994667e-02 -1.03185125e-01 -5.65388575e-02] Sparsity at: 0.03389887339055794 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.3158479e-02 -1.0296783e-01 -5.6642301e-02] Sparsity at: 0.03389887339055794 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9005 - val_loss: 0.7982 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2970240e-02 -1.0286489e-01 -5.6414004e-02] Sparsity at: 0.03389887339055794 Epoch 121/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7985 - val_accuracy: 0.9036 [ 2.24728898e-34 3.00909656e-34 2.58297068e-34 ... -4.28457558e-02 -1.02532186e-01 -5.65643236e-02] Sparsity at: 0.03389887339055794 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8150 - accuracy: 0.8999 - val_loss: 0.7985 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2795397e-02 -1.0252886e-01 -5.6533631e-02] Sparsity at: 0.03389887339055794 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8148 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2820655e-02 -1.0224216e-01 -5.6592166e-02] Sparsity at: 0.03389887339055794 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9005 - val_loss: 0.7981 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2520795e-02 -1.0203333e-01 -5.6734998e-02] Sparsity at: 0.03389887339055794 Epoch 125/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8147 - accuracy: 0.9006 - val_loss: 0.7976 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2683408e-02 -1.0183863e-01 -5.6465697e-02] Sparsity at: 0.03389887339055794 Epoch 126/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7984 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2323146e-02 -1.0170538e-01 -5.6575030e-02] Sparsity at: 0.03389887339055794 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9004 - val_loss: 0.7981 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2393982e-02 -1.0149348e-01 -5.6534257e-02] Sparsity at: 0.03389887339055794 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9006 - val_loss: 0.7979 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2290986e-02 -1.0125504e-01 -5.6540426e-02] Sparsity at: 0.03389887339055794 Epoch 129/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8147 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2168841e-02 -1.0130440e-01 -5.6469820e-02] Sparsity at: 0.03389887339055794 Epoch 130/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8148 - accuracy: 0.9001 - val_loss: 0.7989 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1968044e-02 -1.0121643e-01 -5.6500975e-02] Sparsity at: 0.03389887339055794 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7992 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.2103246e-02 -1.0091434e-01 -5.6479193e-02] Sparsity at: 0.03389887339055794 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8147 - accuracy: 0.9004 - val_loss: 0.7970 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1887861e-02 -1.0072812e-01 -5.6370724e-02] Sparsity at: 0.03389887339055794 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1784987e-02 -1.0064794e-01 -5.6207594e-02] Sparsity at: 0.03389887339055794 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7988 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1575938e-02 -1.0050968e-01 -5.6128304e-02] Sparsity at: 0.03389887339055794 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9008 - val_loss: 0.7981 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1508704e-02 -1.0034810e-01 -5.6251936e-02] Sparsity at: 0.03389887339055794 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7986 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1482348e-02 -1.0032443e-01 -5.5893302e-02] Sparsity at: 0.03389887339055794 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1265231e-02 -1.0019071e-01 -5.5812761e-02] Sparsity at: 0.03389887339055794 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1203286e-02 -1.0004001e-01 -5.5689316e-02] Sparsity at: 0.03389887339055794 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9004 - val_loss: 0.7980 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.1064810e-02 -9.9953301e-02 -5.5468183e-02] Sparsity at: 0.03389887339055794 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9029 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0913761e-02 -9.9871598e-02 -5.5456411e-02] Sparsity at: 0.03389887339055794 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7990 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0681038e-02 -9.9681742e-02 -5.5142451e-02] Sparsity at: 0.03389887339055794 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9002 - val_loss: 0.7983 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0787324e-02 -9.9635944e-02 -5.4876503e-02] Sparsity at: 0.03389887339055794 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0774621e-02 -9.9405967e-02 -5.4792445e-02] Sparsity at: 0.03389887339055794 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0682767e-02 -9.9346727e-02 -5.4538336e-02] Sparsity at: 0.03389887339055794 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7983 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0526766e-02 -9.9196672e-02 -5.4513894e-02] Sparsity at: 0.03389887339055794 Epoch 146/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9004 - val_loss: 0.7981 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0534776e-02 -9.9095128e-02 -5.4254137e-02] Sparsity at: 0.03389887339055794 Epoch 147/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7985 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0429071e-02 -9.9093825e-02 -5.4120582e-02] Sparsity at: 0.03389887339055794 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7971 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0368181e-02 -9.9069409e-02 -5.3941395e-02] Sparsity at: 0.03389887339055794 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9005 - val_loss: 0.7980 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0155347e-02 -9.8817952e-02 -5.3824235e-02] Sparsity at: 0.03389887339055794 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9041 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0206220e-02 -9.8611146e-02 -5.3612702e-02] Sparsity at: 0.03389887339055794 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.02029129779350991 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.05520869099208903 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.14918099394537432 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 52s 9ms/step - loss: 0.8143 - accuracy: 0.9004 - val_loss: 0.7977 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0160734e-02 -9.8526441e-02 -5.3414274e-02] Sparsity at: 0.03389887339055794 Epoch 152/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -4.0012572e-02 -9.8373704e-02 -5.3168286e-02] Sparsity at: 0.03389887339055794 Epoch 153/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9970174e-02 -9.8315604e-02 -5.3018395e-02] Sparsity at: 0.03389887339055794 Epoch 154/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8146 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9984159e-02 -9.8079465e-02 -5.2911773e-02] Sparsity at: 0.03389887339055794 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9005 - val_loss: 0.7979 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9841775e-02 -9.7797677e-02 -5.3076610e-02] Sparsity at: 0.03389887339055794 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9005 - val_loss: 0.7978 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9869893e-02 -9.7771361e-02 -5.3057663e-02] Sparsity at: 0.03389887339055794 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9005 - val_loss: 0.7979 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9757889e-02 -9.7843446e-02 -5.2573107e-02] Sparsity at: 0.03389887339055794 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9566200e-02 -9.7679682e-02 -5.2691698e-02] Sparsity at: 0.03389887339055794 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9520927e-02 -9.7562119e-02 -5.2433077e-02] Sparsity at: 0.03389887339055794 Epoch 160/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9531954e-02 -9.7527303e-02 -5.2402634e-02] Sparsity at: 0.03389887339055794 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9005 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9467428e-02 -9.7221486e-02 -5.2435603e-02] Sparsity at: 0.03389887339055794 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9006 - val_loss: 0.7976 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9368384e-02 -9.7259119e-02 -5.2208368e-02] Sparsity at: 0.03389887339055794 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9002 - val_loss: 0.7985 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9185010e-02 -9.7304963e-02 -5.2055605e-02] Sparsity at: 0.03389887339055794 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8146 - accuracy: 0.9004 - val_loss: 0.7986 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9120469e-02 -9.7124003e-02 -5.2002370e-02] Sparsity at: 0.03389887339055794 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9173972e-02 -9.7064294e-02 -5.1777918e-02] Sparsity at: 0.03389887339055794 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.9044779e-02 -9.6707635e-02 -5.1831044e-02] Sparsity at: 0.03389887339055794 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9002 - val_loss: 0.7985 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8891442e-02 -9.6584521e-02 -5.1631693e-02] Sparsity at: 0.03389887339055794 Epoch 168/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8145 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8880162e-02 -9.6602522e-02 -5.1566467e-02] Sparsity at: 0.03389887339055794 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8936529e-02 -9.6487880e-02 -5.1476356e-02] Sparsity at: 0.03389887339055794 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9004 - val_loss: 0.7981 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8828190e-02 -9.6309483e-02 -5.1377986e-02] Sparsity at: 0.03389887339055794 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7982 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8618475e-02 -9.6259847e-02 -5.1290415e-02] Sparsity at: 0.03389887339055794 Epoch 172/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8145 - accuracy: 0.9008 - val_loss: 0.7971 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8620308e-02 -9.6174836e-02 -5.1033225e-02] Sparsity at: 0.03389887339055794 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8503442e-02 -9.6053489e-02 -5.0986473e-02] Sparsity at: 0.03389887339055794 Epoch 174/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9008 - val_loss: 0.7972 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8421802e-02 -9.6070699e-02 -5.0773028e-02] Sparsity at: 0.03389887339055794 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8304843e-02 -9.5860213e-02 -5.0784688e-02] Sparsity at: 0.03389887339055794 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8256925e-02 -9.5857024e-02 -5.0564509e-02] Sparsity at: 0.03389887339055794 Epoch 177/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9042 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.8131539e-02 -9.5654763e-02 -5.0429154e-02] Sparsity at: 0.03389887339055794 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7858557e-02 -9.5619597e-02 -5.0271366e-02] Sparsity at: 0.03389887339055794 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7970 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7936624e-02 -9.5671259e-02 -5.0046794e-02] Sparsity at: 0.03389887339055794 Epoch 180/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9005 - val_loss: 0.7973 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7678573e-02 -9.5665842e-02 -4.9962055e-02] Sparsity at: 0.03389887339055794 Epoch 181/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7428129e-02 -9.5617063e-02 -5.0066829e-02] Sparsity at: 0.03389887339055794 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7379395e-02 -9.5496528e-02 -5.0057884e-02] Sparsity at: 0.03389887339055794 Epoch 183/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7289985e-02 -9.5511854e-02 -4.9875248e-02] Sparsity at: 0.03389887339055794 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8144 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7304468e-02 -9.5596969e-02 -4.9739204e-02] Sparsity at: 0.03389887339055794 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9000 - val_loss: 0.7975 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7144955e-02 -9.5311917e-02 -4.9604733e-02] Sparsity at: 0.03389887339055794 Epoch 186/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6989454e-02 -9.5435508e-02 -4.9652930e-02] Sparsity at: 0.03389887339055794 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8145 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.7039138e-02 -9.5328264e-02 -4.9320236e-02] Sparsity at: 0.03389887339055794 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6990669e-02 -9.5435224e-02 -4.9284574e-02] Sparsity at: 0.03389887339055794 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6846232e-02 -9.5318988e-02 -4.9229257e-02] Sparsity at: 0.03389887339055794 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6845025e-02 -9.5394447e-02 -4.8909664e-02] Sparsity at: 0.03389887339055794 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6853984e-02 -9.5421620e-02 -4.8937008e-02] Sparsity at: 0.03389887339055794 Epoch 192/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6846444e-02 -9.5321164e-02 -4.8895042e-02] Sparsity at: 0.03389887339055794 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6788758e-02 -9.5355622e-02 -4.8985101e-02] Sparsity at: 0.03389887339055794 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9005 - val_loss: 0.7973 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6745898e-02 -9.5456250e-02 -4.8845507e-02] Sparsity at: 0.03389887339055794 Epoch 195/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6742866e-02 -9.5368162e-02 -4.8822910e-02] Sparsity at: 0.03389887339055794 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6657874e-02 -9.5273271e-02 -4.8721895e-02] Sparsity at: 0.03389887339055794 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6893822e-02 -9.5330276e-02 -4.8658468e-02] Sparsity at: 0.03389887339055794 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9007 - val_loss: 0.7970 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6702529e-02 -9.5401332e-02 -4.8524633e-02] Sparsity at: 0.03389887339055794 Epoch 199/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6791317e-02 -9.5299415e-02 -4.8651472e-02] Sparsity at: 0.03389887339055794 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6822926e-02 -9.5364712e-02 -4.8633400e-02] Sparsity at: 0.03389887339055794 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.02736453349347512 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.07399579934944 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.169265381781198 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 50s 8ms/step - loss: 0.8141 - accuracy: 0.9005 - val_loss: 0.7970 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6766224e-02 -9.5349081e-02 -4.8700295e-02] Sparsity at: 0.03389887339055794 Epoch 202/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6654163e-02 -9.5388561e-02 -4.8548795e-02] Sparsity at: 0.03389887339055794 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6740389e-02 -9.5344596e-02 -4.8460465e-02] Sparsity at: 0.03389887339055794 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9041 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6682975e-02 -9.5422961e-02 -4.8477147e-02] Sparsity at: 0.03389887339055794 Epoch 205/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7975 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6712363e-02 -9.5528968e-02 -4.8388049e-02] Sparsity at: 0.03389887339055794 Epoch 206/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6666282e-02 -9.5661275e-02 -4.8298709e-02] Sparsity at: 0.03389887339055794 Epoch 207/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8146 - accuracy: 0.9002 - val_loss: 0.7970 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6647327e-02 -9.5709555e-02 -4.8337694e-02] Sparsity at: 0.03389887339055794 Epoch 208/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6614973e-02 -9.5689550e-02 -4.8189010e-02] Sparsity at: 0.03389887339055794 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6514372e-02 -9.5547587e-02 -4.8356015e-02] Sparsity at: 0.03389887339055794 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6545422e-02 -9.5651612e-02 -4.8239220e-02] Sparsity at: 0.03389887339055794 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6518127e-02 -9.5698588e-02 -4.8232540e-02] Sparsity at: 0.03389887339055794 Epoch 212/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6425881e-02 -9.5569089e-02 -4.8165847e-02] Sparsity at: 0.03389887339055794 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6330771e-02 -9.5476292e-02 -4.8044495e-02] Sparsity at: 0.03389887339055794 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7983 - val_accuracy: 0.9029 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6186114e-02 -9.5544897e-02 -4.8149280e-02] Sparsity at: 0.03389887339055794 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6222409e-02 -9.5553547e-02 -4.8078544e-02] Sparsity at: 0.03389887339055794 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7967 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6257256e-02 -9.5641248e-02 -4.7827017e-02] Sparsity at: 0.03389887339055794 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6222503e-02 -9.5413141e-02 -4.7789477e-02] Sparsity at: 0.03389887339055794 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6229964e-02 -9.5456518e-02 -4.7731601e-02] Sparsity at: 0.03389887339055794 Epoch 219/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6161963e-02 -9.5515124e-02 -4.7808003e-02] Sparsity at: 0.03389887339055794 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6126863e-02 -9.5618695e-02 -4.7855288e-02] Sparsity at: 0.03389887339055794 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9006 - val_loss: 0.7970 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6202468e-02 -9.5375992e-02 -4.7583513e-02] Sparsity at: 0.03389887339055794 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6060847e-02 -9.5313169e-02 -4.7714949e-02] Sparsity at: 0.03389887339055794 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6002871e-02 -9.5425524e-02 -4.7506902e-02] Sparsity at: 0.03389887339055794 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7976 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.6038239e-02 -9.5601425e-02 -4.7517646e-02] Sparsity at: 0.03389887339055794 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9042 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5968818e-02 -9.5511794e-02 -4.7478750e-02] Sparsity at: 0.03389887339055794 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5871066e-02 -9.5570743e-02 -4.7512822e-02] Sparsity at: 0.03389887339055794 Epoch 227/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7968 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5942599e-02 -9.5443942e-02 -4.7278110e-02] Sparsity at: 0.03389887339055794 Epoch 228/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5953950e-02 -9.5320515e-02 -4.7337536e-02] Sparsity at: 0.03389887339055794 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7972 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5934608e-02 -9.5209248e-02 -4.7216520e-02] Sparsity at: 0.03389887339055794 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5973743e-02 -9.5165089e-02 -4.7222558e-02] Sparsity at: 0.03389887339055794 Epoch 231/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5904784e-02 -9.5220149e-02 -4.7137473e-02] Sparsity at: 0.03389887339055794 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5810012e-02 -9.5180489e-02 -4.7144707e-02] Sparsity at: 0.03389887339055794 Epoch 233/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7975 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5889797e-02 -9.5145136e-02 -4.7049131e-02] Sparsity at: 0.03389887339055794 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5673074e-02 -9.5019743e-02 -4.6992846e-02] Sparsity at: 0.03389887339055794 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5785090e-02 -9.5116653e-02 -4.6951301e-02] Sparsity at: 0.03389887339055794 Epoch 236/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7981 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5729729e-02 -9.4922632e-02 -4.6977296e-02] Sparsity at: 0.03389887339055794 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7964 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5872459e-02 -9.4980769e-02 -4.6689376e-02] Sparsity at: 0.03389887339055794 Epoch 238/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5715520e-02 -9.5009916e-02 -4.6760783e-02] Sparsity at: 0.03389887339055794 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.8999 - val_loss: 0.7977 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5639398e-02 -9.4968215e-02 -4.6792123e-02] Sparsity at: 0.03389887339055794 Epoch 240/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7967 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5708439e-02 -9.4937362e-02 -4.6704542e-02] Sparsity at: 0.03389887339055794 Epoch 241/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5715658e-02 -9.4963834e-02 -4.6583574e-02] Sparsity at: 0.03389887339055794 Epoch 242/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5747267e-02 -9.4868518e-02 -4.6438731e-02] Sparsity at: 0.03389887339055794 Epoch 243/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7965 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5732713e-02 -9.4895750e-02 -4.6354875e-02] Sparsity at: 0.03389887339055794 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9007 - val_loss: 0.7975 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5870828e-02 -9.4912007e-02 -4.6404488e-02] Sparsity at: 0.03389887339055794 Epoch 245/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7975 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5899110e-02 -9.4851337e-02 -4.6195492e-02] Sparsity at: 0.03389887339055794 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5635576e-02 -9.4908312e-02 -4.6327610e-02] Sparsity at: 0.03389887339055794 Epoch 247/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9007 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5510745e-02 -9.4812930e-02 -4.6261530e-02] Sparsity at: 0.03389887339055794 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5489000e-02 -9.4934434e-02 -4.6172898e-02] Sparsity at: 0.03389887339055794 Epoch 249/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5617728e-02 -9.4838835e-02 -4.6212889e-02] Sparsity at: 0.03389887339055794 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7967 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5649642e-02 -9.5029481e-02 -4.6063609e-02] Sparsity at: 0.03389887339055794 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.035545293051153504 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.09501041010693445 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.18689646920878822 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 52s 8ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7969 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5617806e-02 -9.5094390e-02 -4.6234921e-02] Sparsity at: 0.03389887339055794 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5730913e-02 -9.4931237e-02 -4.6082266e-02] Sparsity at: 0.03389887339055794 Epoch 253/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5592459e-02 -9.5175654e-02 -4.5908988e-02] Sparsity at: 0.03389887339055794 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7968 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5549644e-02 -9.5256902e-02 -4.5850161e-02] Sparsity at: 0.03389887339055794 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7967 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5488561e-02 -9.5142990e-02 -4.6026327e-02] Sparsity at: 0.03389887339055794 Epoch 256/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7969 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5485275e-02 -9.5352359e-02 -4.5914993e-02] Sparsity at: 0.03389887339055794 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5459593e-02 -9.5213458e-02 -4.5930501e-02] Sparsity at: 0.03389887339055794 Epoch 258/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5578165e-02 -9.5176637e-02 -4.5899615e-02] Sparsity at: 0.03389887339055794 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7964 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5370272e-02 -9.5058732e-02 -4.5882393e-02] Sparsity at: 0.03389887339055794 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7966 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5681546e-02 -9.5164001e-02 -4.5845345e-02] Sparsity at: 0.03389887339055794 Epoch 261/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5568878e-02 -9.5177382e-02 -4.5810193e-02] Sparsity at: 0.03389887339055794 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5556264e-02 -9.5076583e-02 -4.5826472e-02] Sparsity at: 0.03389887339055794 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5551034e-02 -9.5224850e-02 -4.5927629e-02] Sparsity at: 0.03389887339055794 Epoch 264/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7977 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5350829e-02 -9.5211506e-02 -4.5852963e-02] Sparsity at: 0.03389887339055794 Epoch 265/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5366494e-02 -9.5223732e-02 -4.5784220e-02] Sparsity at: 0.03389887339055794 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9005 - val_loss: 0.7970 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5338186e-02 -9.5380858e-02 -4.5706812e-02] Sparsity at: 0.03389887339055794 Epoch 267/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7965 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5397481e-02 -9.5375113e-02 -4.5691725e-02] Sparsity at: 0.03389887339055794 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9041 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5126481e-02 -9.5453985e-02 -4.5792866e-02] Sparsity at: 0.03389887339055794 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7979 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5170663e-02 -9.5191173e-02 -4.5779787e-02] Sparsity at: 0.03389887339055794 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7967 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5334084e-02 -9.5455982e-02 -4.5629680e-02] Sparsity at: 0.03389887339055794 Epoch 271/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5297435e-02 -9.5394075e-02 -4.5442551e-02] Sparsity at: 0.03389887339055794 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5194211e-02 -9.5356166e-02 -4.5473021e-02] Sparsity at: 0.03389887339055794 Epoch 273/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5214480e-02 -9.5590666e-02 -4.5393050e-02] Sparsity at: 0.03389887339055794 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5112038e-02 -9.5436752e-02 -4.5453407e-02] Sparsity at: 0.03389887339055794 Epoch 275/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5072628e-02 -9.5558852e-02 -4.5651820e-02] Sparsity at: 0.03389887339055794 Epoch 276/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9007 - val_loss: 0.7967 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5105940e-02 -9.5468014e-02 -4.5516830e-02] Sparsity at: 0.03389887339055794 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7972 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5074644e-02 -9.5516369e-02 -4.5503777e-02] Sparsity at: 0.03389887339055794 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5044502e-02 -9.5379956e-02 -4.5790471e-02] Sparsity at: 0.03389887339055794 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5028566e-02 -9.5406018e-02 -4.5601208e-02] Sparsity at: 0.03389887339055794 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5066415e-02 -9.5495582e-02 -4.5434110e-02] Sparsity at: 0.03389887339055794 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7967 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5047494e-02 -9.5373660e-02 -4.5322888e-02] Sparsity at: 0.03389887339055794 Epoch 282/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5069466e-02 -9.5422871e-02 -4.5361780e-02] Sparsity at: 0.03389887339055794 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9005 - val_loss: 0.7964 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5249315e-02 -9.5551983e-02 -4.5328576e-02] Sparsity at: 0.03389887339055794 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5007332e-02 -9.5532939e-02 -4.5345385e-02] Sparsity at: 0.03389887339055794 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5146143e-02 -9.5632568e-02 -4.5161236e-02] Sparsity at: 0.03389887339055794 Epoch 286/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7967 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5075072e-02 -9.5724843e-02 -4.5253091e-02] Sparsity at: 0.03389887339055794 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5112120e-02 -9.5643081e-02 -4.5250937e-02] Sparsity at: 0.03389887339055794 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7972 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4965958e-02 -9.5800288e-02 -4.5286123e-02] Sparsity at: 0.03389887339055794 Epoch 289/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.8999 - val_loss: 0.7962 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5031259e-02 -9.5649160e-02 -4.5255616e-02] Sparsity at: 0.03389887339055794 Epoch 290/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7970 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5092145e-02 -9.5739923e-02 -4.5327332e-02] Sparsity at: 0.03389887339055794 Epoch 291/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4978494e-02 -9.5722571e-02 -4.5004915e-02] Sparsity at: 0.03389887339055794 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4856971e-02 -9.5970541e-02 -4.5212328e-02] Sparsity at: 0.03389887339055794 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7971 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5053559e-02 -9.5789202e-02 -4.5266882e-02] Sparsity at: 0.03389887339055794 Epoch 294/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.5143219e-02 -9.5957994e-02 -4.5177188e-02] Sparsity at: 0.03389887339055794 Epoch 295/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4843873e-02 -9.5676102e-02 -4.5376319e-02] Sparsity at: 0.03389887339055794 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7980 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4879837e-02 -9.5855080e-02 -4.5196690e-02] Sparsity at: 0.03389887339055794 Epoch 297/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4731772e-02 -9.5909670e-02 -4.5119233e-02] Sparsity at: 0.03389887339055794 Epoch 298/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.8999 - val_loss: 0.7975 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4756761e-02 -9.6019350e-02 -4.5176577e-02] Sparsity at: 0.03389887339055794 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7973 - val_accuracy: 0.9041 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4808137e-02 -9.6014977e-02 -4.5063160e-02] Sparsity at: 0.03389887339055794 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4819629e-02 -9.6085303e-02 -4.5012720e-02] Sparsity at: 0.03389887339055794 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.04435477734010984 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.11068923326743096 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.19937929907021257 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 51s 8ms/step - loss: 0.8143 - accuracy: 0.9000 - val_loss: 0.7962 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4797397e-02 -9.5800512e-02 -4.4861566e-02] Sparsity at: 0.03389887339055794 Epoch 302/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7974 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4943394e-02 -9.6041270e-02 -4.4934463e-02] Sparsity at: 0.03389887339055794 Epoch 303/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4885950e-02 -9.5935509e-02 -4.4857688e-02] Sparsity at: 0.03389887339055794 Epoch 304/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4726981e-02 -9.6023098e-02 -4.4909544e-02] Sparsity at: 0.03389887339055794 Epoch 305/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4916848e-02 -9.6007399e-02 -4.4866450e-02] Sparsity at: 0.03389887339055794 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7969 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4770831e-02 -9.6156009e-02 -4.4731978e-02] Sparsity at: 0.03389887339055794 Epoch 307/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7964 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4854479e-02 -9.6200190e-02 -4.4606078e-02] Sparsity at: 0.03389887339055794 Epoch 308/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4713868e-02 -9.6203551e-02 -4.4499710e-02] Sparsity at: 0.03389887339055794 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4713618e-02 -9.6233115e-02 -4.4924371e-02] Sparsity at: 0.03389887339055794 Epoch 310/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7964 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4718137e-02 -9.6289963e-02 -4.4647429e-02] Sparsity at: 0.03389887339055794 Epoch 311/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4685895e-02 -9.6370988e-02 -4.4619747e-02] Sparsity at: 0.03389887339055794 Epoch 312/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4652330e-02 -9.6495420e-02 -4.4804323e-02] Sparsity at: 0.03389887339055794 Epoch 313/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7974 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4587249e-02 -9.6361816e-02 -4.4753689e-02] Sparsity at: 0.03389887339055794 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7978 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4594022e-02 -9.6612059e-02 -4.4690166e-02] Sparsity at: 0.03389887339055794 Epoch 315/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4554962e-02 -9.6487179e-02 -4.4727668e-02] Sparsity at: 0.03389887339055794 Epoch 316/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9002 - val_loss: 0.7978 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4506112e-02 -9.6533008e-02 -4.4648893e-02] Sparsity at: 0.03389887339055794 Epoch 317/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7974 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4443866e-02 -9.6589275e-02 -4.4686474e-02] Sparsity at: 0.03389887339055794 Epoch 318/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4492228e-02 -9.6707433e-02 -4.4591308e-02] Sparsity at: 0.03389887339055794 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8999 - val_loss: 0.7974 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4442347e-02 -9.6671715e-02 -4.4693373e-02] Sparsity at: 0.03389887339055794 Epoch 320/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4525141e-02 -9.6611209e-02 -4.4650257e-02] Sparsity at: 0.03389887339055794 Epoch 321/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8137 - accuracy: 0.9004 - val_loss: 0.7976 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4595732e-02 -9.6739486e-02 -4.4451512e-02] Sparsity at: 0.03389887339055794 Epoch 322/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4630559e-02 -9.6848808e-02 -4.4497199e-02] Sparsity at: 0.03389887339055794 Epoch 323/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4547932e-02 -9.6909791e-02 -4.4492215e-02] Sparsity at: 0.03389887339055794 Epoch 324/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9006 - val_loss: 0.7968 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4590151e-02 -9.6832573e-02 -4.4349544e-02] Sparsity at: 0.03389887339055794 Epoch 325/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4474447e-02 -9.6833356e-02 -4.4484090e-02] Sparsity at: 0.03389887339055794 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7974 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4530651e-02 -9.6835099e-02 -4.4366024e-02] Sparsity at: 0.03389887339055794 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4442931e-02 -9.6881233e-02 -4.4429567e-02] Sparsity at: 0.03389887339055794 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7982 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4487471e-02 -9.6792482e-02 -4.4422358e-02] Sparsity at: 0.03389887339055794 Epoch 329/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.8999 - val_loss: 0.7976 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4553282e-02 -9.6873365e-02 -4.4153761e-02] Sparsity at: 0.03389887339055794 Epoch 330/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.8999 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4503222e-02 -9.6902415e-02 -4.4259042e-02] Sparsity at: 0.03389887339055794 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7974 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4294885e-02 -9.6824251e-02 -4.4309717e-02] Sparsity at: 0.03389887339055794 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7975 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4381229e-02 -9.6809439e-02 -4.4335492e-02] Sparsity at: 0.03389887339055794 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4424666e-02 -9.7084351e-02 -4.4255201e-02] Sparsity at: 0.03389887339055794 Epoch 334/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4483191e-02 -9.7174242e-02 -4.4148780e-02] Sparsity at: 0.03389887339055794 Epoch 335/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4550045e-02 -9.7047716e-02 -4.3985836e-02] Sparsity at: 0.03389887339055794 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7980 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4406908e-02 -9.7309612e-02 -4.3992408e-02] Sparsity at: 0.03389887339055794 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7965 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4412827e-02 -9.7169854e-02 -4.4006787e-02] Sparsity at: 0.03389887339055794 Epoch 338/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7979 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4341406e-02 -9.7310677e-02 -4.3965001e-02] Sparsity at: 0.03389887339055794 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7979 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4288958e-02 -9.7450174e-02 -4.3762885e-02] Sparsity at: 0.03389887339055794 Epoch 340/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7971 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4331642e-02 -9.7471438e-02 -4.3890771e-02] Sparsity at: 0.03389887339055794 Epoch 341/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4296717e-02 -9.7389624e-02 -4.3782044e-02] Sparsity at: 0.03389887339055794 Epoch 342/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4141175e-02 -9.7261921e-02 -4.4168811e-02] Sparsity at: 0.03389887339055794 Epoch 343/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7975 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4265943e-02 -9.7186625e-02 -4.3805435e-02] Sparsity at: 0.03389887339055794 Epoch 344/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4208931e-02 -9.7442105e-02 -4.3690924e-02] Sparsity at: 0.03389887339055794 Epoch 345/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4246147e-02 -9.7375527e-02 -4.3946978e-02] Sparsity at: 0.03389887339055794 Epoch 346/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7969 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4285273e-02 -9.7365528e-02 -4.3918546e-02] Sparsity at: 0.03389887339055794 Epoch 347/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4364570e-02 -9.7446091e-02 -4.3807507e-02] Sparsity at: 0.03389887339055794 Epoch 348/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7967 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4288522e-02 -9.7659193e-02 -4.3912929e-02] Sparsity at: 0.03389887339055794 Epoch 349/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4248292e-02 -9.7485386e-02 -4.3722317e-02] Sparsity at: 0.03389887339055794 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7965 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4196287e-02 -9.7396024e-02 -4.3715805e-02] Sparsity at: 0.03389887339055794 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.052743863487109355 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.12429208335510111 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.21417686976981543 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 51s 8ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7966 - val_accuracy: 0.9042 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4352012e-02 -9.7539194e-02 -4.3598451e-02] Sparsity at: 0.03389887339055794 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4314599e-02 -9.7610652e-02 -4.3607969e-02] Sparsity at: 0.03389887339055794 Epoch 353/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4292400e-02 -9.7494572e-02 -4.3581631e-02] Sparsity at: 0.03389887339055794 Epoch 354/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7974 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4235049e-02 -9.7610846e-02 -4.3565672e-02] Sparsity at: 0.03389887339055794 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7985 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4263253e-02 -9.7436532e-02 -4.3598924e-02] Sparsity at: 0.03389887339055794 Epoch 356/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4184940e-02 -9.7340889e-02 -4.3624602e-02] Sparsity at: 0.03389887339055794 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7967 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4227826e-02 -9.7506076e-02 -4.3547548e-02] Sparsity at: 0.03389887339055794 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4216747e-02 -9.7449049e-02 -4.3502424e-02] Sparsity at: 0.03389887339055794 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4172311e-02 -9.7490683e-02 -4.3405849e-02] Sparsity at: 0.03389887339055794 Epoch 360/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7973 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4276269e-02 -9.7525246e-02 -4.3468878e-02] Sparsity at: 0.03389887339055794 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4174651e-02 -9.7377978e-02 -4.3271024e-02] Sparsity at: 0.03389887339055794 Epoch 362/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4285706e-02 -9.7638935e-02 -4.3281332e-02] Sparsity at: 0.03389887339055794 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7974 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4212306e-02 -9.7392350e-02 -4.3429457e-02] Sparsity at: 0.03389887339055794 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4162395e-02 -9.7629458e-02 -4.3191437e-02] Sparsity at: 0.03389887339055794 Epoch 365/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4181096e-02 -9.7734824e-02 -4.3249957e-02] Sparsity at: 0.03389887339055794 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7966 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4099434e-02 -9.7754508e-02 -4.3237682e-02] Sparsity at: 0.03389887339055794 Epoch 367/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7965 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4158781e-02 -9.7804271e-02 -4.3223567e-02] Sparsity at: 0.03389887339055794 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4111638e-02 -9.7762577e-02 -4.3182202e-02] Sparsity at: 0.03389887339055794 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9005 - val_loss: 0.7967 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4281168e-02 -9.7769648e-02 -4.3119546e-02] Sparsity at: 0.03389887339055794 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4193531e-02 -9.7666502e-02 -4.3246772e-02] Sparsity at: 0.03389887339055794 Epoch 371/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4162913e-02 -9.7703449e-02 -4.3131862e-02] Sparsity at: 0.03389887339055794 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4065649e-02 -9.7926319e-02 -4.3108523e-02] Sparsity at: 0.03389887339055794 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7976 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4127947e-02 -9.7778030e-02 -4.3077670e-02] Sparsity at: 0.03389887339055794 Epoch 374/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8144 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4074351e-02 -9.7829141e-02 -4.3011408e-02] Sparsity at: 0.03389887339055794 Epoch 375/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4054548e-02 -9.7785011e-02 -4.2790920e-02] Sparsity at: 0.03389887339055794 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4001186e-02 -9.7881466e-02 -4.3031055e-02] Sparsity at: 0.03389887339055794 Epoch 377/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.8999 - val_loss: 0.7971 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3961233e-02 -9.7930126e-02 -4.2864677e-02] Sparsity at: 0.03389887339055794 Epoch 378/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4112930e-02 -9.7780302e-02 -4.2862244e-02] Sparsity at: 0.03389887339055794 Epoch 379/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4011547e-02 -9.7965978e-02 -4.3037977e-02] Sparsity at: 0.03389887339055794 Epoch 380/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7968 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3951871e-02 -9.7891696e-02 -4.3034099e-02] Sparsity at: 0.03389887339055794 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3959676e-02 -9.8005764e-02 -4.2922460e-02] Sparsity at: 0.03389887339055794 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4084368e-02 -9.7997084e-02 -4.3019287e-02] Sparsity at: 0.03389887339055794 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4002177e-02 -9.8031886e-02 -4.2913541e-02] Sparsity at: 0.03389887339055794 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4053478e-02 -9.7856842e-02 -4.2811599e-02] Sparsity at: 0.03389887339055794 Epoch 385/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3933617e-02 -9.8102175e-02 -4.2863261e-02] Sparsity at: 0.03389887339055794 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3994645e-02 -9.7985752e-02 -4.2854995e-02] Sparsity at: 0.03389887339055794 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7979 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3935145e-02 -9.8022252e-02 -4.2910121e-02] Sparsity at: 0.03389887339055794 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3961538e-02 -9.8096751e-02 -4.2805966e-02] Sparsity at: 0.03389887339055794 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7975 - val_accuracy: 0.9030 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3820871e-02 -9.8180383e-02 -4.2728234e-02] Sparsity at: 0.03389887339055794 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7969 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3863250e-02 -9.8316744e-02 -4.2488240e-02] Sparsity at: 0.03389887339055794 Epoch 391/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4028519e-02 -9.8252214e-02 -4.2500339e-02] Sparsity at: 0.03389887339055794 Epoch 392/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7970 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3830155e-02 -9.8116055e-02 -4.2651556e-02] Sparsity at: 0.03389887339055794 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7975 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3948086e-02 -9.8080926e-02 -4.2738486e-02] Sparsity at: 0.03389887339055794 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.8998 - val_loss: 0.7978 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3788186e-02 -9.8295294e-02 -4.2450201e-02] Sparsity at: 0.03389887339055794 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9007 - val_loss: 0.7967 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3931240e-02 -9.8315232e-02 -4.2527422e-02] Sparsity at: 0.03389887339055794 Epoch 396/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.4009095e-02 -9.8474383e-02 -4.2499047e-02] Sparsity at: 0.03389887339055794 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3984900e-02 -9.8390952e-02 -4.2577066e-02] Sparsity at: 0.03389887339055794 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7977 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3980556e-02 -9.8338909e-02 -4.2471454e-02] Sparsity at: 0.03389887339055794 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7971 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3913329e-02 -9.8397359e-02 -4.2382736e-02] Sparsity at: 0.03389887339055794 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3771519e-02 -9.8473676e-02 -4.2455129e-02] Sparsity at: 0.03389887339055794 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.057099786329344315 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.1306709387685725 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.22291756162696963 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 52s 8ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7972 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3895817e-02 -9.8488562e-02 -4.2521272e-02] Sparsity at: 0.03389887339055794 Epoch 402/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8138 - accuracy: 0.9001 - val_loss: 0.7973 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3841401e-02 -9.8517023e-02 -4.2242482e-02] Sparsity at: 0.03389887339055794 Epoch 403/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7971 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3847246e-02 -9.8482326e-02 -4.2353295e-02] Sparsity at: 0.03389887339055794 Epoch 404/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8137 - accuracy: 0.9007 - val_loss: 0.7979 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3787936e-02 -9.8648608e-02 -4.2141754e-02] Sparsity at: 0.03389887339055794 Epoch 405/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7965 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3911116e-02 -9.8407306e-02 -4.2297687e-02] Sparsity at: 0.03389887339055794 Epoch 406/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3867877e-02 -9.8565169e-02 -4.2211983e-02] Sparsity at: 0.03389887339055794 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3771940e-02 -9.8551877e-02 -4.2215295e-02] Sparsity at: 0.03389887339055794 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3729360e-02 -9.8579481e-02 -4.2312663e-02] Sparsity at: 0.03389887339055794 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8136 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3793513e-02 -9.8539606e-02 -4.2214945e-02] Sparsity at: 0.03389887339055794 Epoch 410/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9001 - val_loss: 0.7967 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3695366e-02 -9.8623827e-02 -4.2219795e-02] Sparsity at: 0.03389887339055794 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7974 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3801686e-02 -9.8452963e-02 -4.2066209e-02] Sparsity at: 0.03389887339055794 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9003 - val_loss: 0.7975 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3612847e-02 -9.8672025e-02 -4.2159382e-02] Sparsity at: 0.03389887339055794 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9003 - val_loss: 0.7986 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3461779e-02 -9.8621055e-02 -4.2199086e-02] Sparsity at: 0.03389887339055794 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7969 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3701170e-02 -9.8626263e-02 -4.2190455e-02] Sparsity at: 0.03389887339055794 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3752497e-02 -9.8701462e-02 -4.2082854e-02] Sparsity at: 0.03389887339055794 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8999 - val_loss: 0.7965 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3724170e-02 -9.8552339e-02 -4.1962158e-02] Sparsity at: 0.03389887339055794 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7973 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3672780e-02 -9.8629236e-02 -4.2007413e-02] Sparsity at: 0.03389887339055794 Epoch 418/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.8997 - val_loss: 0.7983 - val_accuracy: 0.9028 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3397265e-02 -9.8585725e-02 -4.2155094e-02] Sparsity at: 0.03389887339055794 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7976 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3515714e-02 -9.8487645e-02 -4.1888595e-02] Sparsity at: 0.03389887339055794 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7968 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3506636e-02 -9.8538473e-02 -4.1954197e-02] Sparsity at: 0.03389887339055794 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9005 - val_loss: 0.7978 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3627063e-02 -9.8559923e-02 -4.1992247e-02] Sparsity at: 0.03389887339055794 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7969 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3643913e-02 -9.8599210e-02 -4.1897912e-02] Sparsity at: 0.03389887339055794 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7962 - val_accuracy: 0.9041 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3565946e-02 -9.8294690e-02 -4.1929375e-02] Sparsity at: 0.03389887339055794 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7981 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3456821e-02 -9.8460555e-02 -4.1839413e-02] Sparsity at: 0.03389887339055794 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3577211e-02 -9.8268174e-02 -4.1835126e-02] Sparsity at: 0.03389887339055794 Epoch 426/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9004 - val_loss: 0.7974 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3537619e-02 -9.8361425e-02 -4.2015020e-02] Sparsity at: 0.03389887339055794 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7975 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3623248e-02 -9.8630942e-02 -4.1822635e-02] Sparsity at: 0.03389887339055794 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9001 - val_loss: 0.7976 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3484727e-02 -9.8556668e-02 -4.1832197e-02] Sparsity at: 0.03389887339055794 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9004 - val_loss: 0.7968 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3484090e-02 -9.8386228e-02 -4.1712198e-02] Sparsity at: 0.03389887339055794 Epoch 430/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3183515e-02 -9.8379575e-02 -4.2001665e-02] Sparsity at: 0.03389887339055794 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7966 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3385403e-02 -9.8537073e-02 -4.1758124e-02] Sparsity at: 0.03389887339055794 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7965 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3382837e-02 -9.8590985e-02 -4.1638099e-02] Sparsity at: 0.03389887339055794 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7971 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3401709e-02 -9.8427467e-02 -4.1817617e-02] Sparsity at: 0.03389887339055794 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7977 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3349462e-02 -9.8614946e-02 -4.1713927e-02] Sparsity at: 0.03389887339055794 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7978 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3443537e-02 -9.8493300e-02 -4.1573796e-02] Sparsity at: 0.03389887339055794 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7979 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3224288e-02 -9.8619498e-02 -4.1613799e-02] Sparsity at: 0.03389887339055794 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3298600e-02 -9.8531902e-02 -4.1629866e-02] Sparsity at: 0.03389887339055794 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3341009e-02 -9.8537236e-02 -4.1511521e-02] Sparsity at: 0.03389887339055794 Epoch 439/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8135 - accuracy: 0.9004 - val_loss: 0.7980 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3372898e-02 -9.8537736e-02 -4.1489102e-02] Sparsity at: 0.03389887339055794 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3253733e-02 -9.8515213e-02 -4.1431401e-02] Sparsity at: 0.03389887339055794 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9006 - val_loss: 0.7971 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3296064e-02 -9.8250359e-02 -4.1552152e-02] Sparsity at: 0.03389887339055794 Epoch 442/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7976 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3168200e-02 -9.8508604e-02 -4.1460197e-02] Sparsity at: 0.03389887339055794 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.8999 - val_loss: 0.7977 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3131447e-02 -9.8569706e-02 -4.1339118e-02] Sparsity at: 0.03389887339055794 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7980 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3171963e-02 -9.8383456e-02 -4.1247256e-02] Sparsity at: 0.03389887339055794 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3127006e-02 -9.8380260e-02 -4.1312773e-02] Sparsity at: 0.03389887339055794 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3022691e-02 -9.8342314e-02 -4.1346565e-02] Sparsity at: 0.03389887339055794 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7968 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.3103276e-02 -9.8171256e-02 -4.1225247e-02] Sparsity at: 0.03389887339055794 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8995 - val_loss: 0.7977 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2863699e-02 -9.8137617e-02 -4.1382264e-02] Sparsity at: 0.03389887339055794 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8142 - accuracy: 0.9001 - val_loss: 0.7979 - val_accuracy: 0.9031 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2875180e-02 -9.7793140e-02 -4.1399263e-02] Sparsity at: 0.03389887339055794 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7978 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2995105e-02 -9.7972013e-02 -4.1317664e-02] Sparsity at: 0.03389887339055794 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7967 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2796476e-02 -9.8173089e-02 -4.1315712e-02] Sparsity at: 0.03389887339055794 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7974 - val_accuracy: 0.9034 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2771200e-02 -9.7888947e-02 -4.1428145e-02] Sparsity at: 0.03389887339055794 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2825306e-02 -9.7943068e-02 -4.1350365e-02] Sparsity at: 0.03389887339055794 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2773655e-02 -9.7861797e-02 -4.1438095e-02] Sparsity at: 0.03389887339055794 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7982 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2560099e-02 -9.7882271e-02 -4.1324764e-02] Sparsity at: 0.03389887339055794 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7982 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2684274e-02 -9.7680338e-02 -4.1447867e-02] Sparsity at: 0.03389887339055794 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8999 - val_loss: 0.7980 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2584418e-02 -9.7736590e-02 -4.1477330e-02] Sparsity at: 0.03389887339055794 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8143 - accuracy: 0.9001 - val_loss: 0.7970 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2609370e-02 -9.7734906e-02 -4.1320972e-02] Sparsity at: 0.03389887339055794 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2586563e-02 -9.7586520e-02 -4.1410767e-02] Sparsity at: 0.03389887339055794 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7979 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2380771e-02 -9.7531818e-02 -4.1558821e-02] Sparsity at: 0.03389887339055794 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2592177e-02 -9.7453482e-02 -4.1687291e-02] Sparsity at: 0.03389887339055794 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7973 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2498971e-02 -9.7383112e-02 -4.1759025e-02] Sparsity at: 0.03389887339055794 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9003 - val_loss: 0.7976 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2485206e-02 -9.7343452e-02 -4.1740205e-02] Sparsity at: 0.03389887339055794 Epoch 464/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8142 - accuracy: 0.9000 - val_loss: 0.7975 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2445662e-02 -9.7372174e-02 -4.1723810e-02] Sparsity at: 0.03389887339055794 Epoch 465/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7966 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2429863e-02 -9.7428471e-02 -4.1692205e-02] Sparsity at: 0.03389887339055794 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7980 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2395758e-02 -9.7465284e-02 -4.1868746e-02] Sparsity at: 0.03389887339055794 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.8999 - val_loss: 0.7969 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2290079e-02 -9.7259723e-02 -4.1966259e-02] Sparsity at: 0.03389887339055794 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9003 - val_loss: 0.7969 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2357518e-02 -9.7218126e-02 -4.1879151e-02] Sparsity at: 0.03389887339055794 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2468431e-02 -9.7285219e-02 -4.1787252e-02] Sparsity at: 0.03389887339055794 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9004 - val_loss: 0.7971 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2481883e-02 -9.7273722e-02 -4.1809745e-02] Sparsity at: 0.03389887339055794 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7973 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2374643e-02 -9.7160019e-02 -4.1899446e-02] Sparsity at: 0.03389887339055794 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9006 - val_loss: 0.7972 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2324605e-02 -9.7227447e-02 -4.1919228e-02] Sparsity at: 0.03389887339055794 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.8998 - val_loss: 0.7977 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2324005e-02 -9.7026393e-02 -4.2112291e-02] Sparsity at: 0.03389887339055794 Epoch 474/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9003 - val_loss: 0.7970 - val_accuracy: 0.9040 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2257445e-02 -9.7084872e-02 -4.1847751e-02] Sparsity at: 0.03389887339055794 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2258838e-02 -9.7053260e-02 -4.2122334e-02] Sparsity at: 0.03389887339055794 Epoch 476/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9005 - val_loss: 0.7977 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2405175e-02 -9.7091451e-02 -4.1951727e-02] Sparsity at: 0.03389887339055794 Epoch 477/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7965 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2385971e-02 -9.7107440e-02 -4.1911151e-02] Sparsity at: 0.03389887339055794 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7981 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2050751e-02 -9.7092815e-02 -4.2159550e-02] Sparsity at: 0.03389887339055794 Epoch 479/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2291226e-02 -9.7128980e-02 -4.2024780e-02] Sparsity at: 0.03389887339055794 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2265317e-02 -9.7074740e-02 -4.2164955e-02] Sparsity at: 0.03389887339055794 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2336153e-02 -9.7119838e-02 -4.2179491e-02] Sparsity at: 0.03389887339055794 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8139 - accuracy: 0.9001 - val_loss: 0.7972 - val_accuracy: 0.9032 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2358196e-02 -9.7241424e-02 -4.2186491e-02] Sparsity at: 0.03389887339055794 Epoch 483/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7972 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2261532e-02 -9.7131863e-02 -4.2098816e-02] Sparsity at: 0.03389887339055794 Epoch 484/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7973 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2360382e-02 -9.7139090e-02 -4.2085752e-02] Sparsity at: 0.03389887339055794 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2124233e-02 -9.7216226e-02 -4.2437308e-02] Sparsity at: 0.03389887339055794 Epoch 486/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9002 - val_loss: 0.7987 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2250691e-02 -9.7064272e-02 -4.2238608e-02] Sparsity at: 0.03389887339055794 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8139 - accuracy: 0.9002 - val_loss: 0.7978 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2165430e-02 -9.6924998e-02 -4.2306665e-02] Sparsity at: 0.03389887339055794 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9000 - val_loss: 0.7972 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2230306e-02 -9.6959658e-02 -4.2463355e-02] Sparsity at: 0.03389887339055794 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9037 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2372519e-02 -9.7048096e-02 -4.2283148e-02] Sparsity at: 0.03389887339055794 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9004 - val_loss: 0.7978 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2220703e-02 -9.7035661e-02 -4.2394150e-02] Sparsity at: 0.03389887339055794 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2222286e-02 -9.6831806e-02 -4.2429715e-02] Sparsity at: 0.03389887339055794 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.9002 - val_loss: 0.7977 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2252103e-02 -9.6830562e-02 -4.2398795e-02] Sparsity at: 0.03389887339055794 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7976 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2231782e-02 -9.6809991e-02 -4.2366639e-02] Sparsity at: 0.03389887339055794 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7977 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2308962e-02 -9.6736394e-02 -4.2408559e-02] Sparsity at: 0.03389887339055794 Epoch 495/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8999 - val_loss: 0.7980 - val_accuracy: 0.9035 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2263845e-02 -9.6778102e-02 -4.2350445e-02] Sparsity at: 0.03389887339055794 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8141 - accuracy: 0.9000 - val_loss: 0.7978 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2086045e-02 -9.6758753e-02 -4.2493511e-02] Sparsity at: 0.03389887339055794 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8137 - accuracy: 0.9003 - val_loss: 0.7977 - val_accuracy: 0.9033 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2205243e-02 -9.6739762e-02 -4.2327952e-02] Sparsity at: 0.03389887339055794 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8141 - accuracy: 0.9002 - val_loss: 0.7968 - val_accuracy: 0.9038 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2306924e-02 -9.6956059e-02 -4.2436440e-02] Sparsity at: 0.03389887339055794 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8138 - accuracy: 0.8999 - val_loss: 0.7969 - val_accuracy: 0.9036 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2242112e-02 -9.6948199e-02 -4.2466994e-02] Sparsity at: 0.03389887339055794 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8140 - accuracy: 0.9000 - val_loss: 0.7964 - val_accuracy: 0.9039 [ 2.2472890e-34 3.0090966e-34 2.5829707e-34 ... -3.2124970e-02 -9.6946724e-02 -4.2541802e-02] Sparsity at: 0.03389887339055794 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.041994860395789146 Thresholhold -0.05940218269824982 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.09000259265303612 Thresholhold 0.044793546199798584 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10164112225174904 Thresholhold -0.008578553795814514 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:01 - loss: 2.3990 - accuracy: 0.1016WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_begin` time: 2.4637s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 0.4928 - accuracy: 0.8662 - val_loss: 0.2552 - val_accuracy: 0.9237 [-0.05940218 -0.00601314 -0.04628057 ... 0.03355239 0.10355379 -0.05929007] Sparsity at: 0.03389887339055794 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2327 - accuracy: 0.9321 - val_loss: 0.1939 - val_accuracy: 0.9421 [-0.05940218 -0.00601314 -0.04628057 ... 0.04306844 0.127009 -0.09002554] Sparsity at: 0.03389887339055794 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1780 - accuracy: 0.9482 - val_loss: 0.1591 - val_accuracy: 0.9533 [-0.05940218 -0.00601314 -0.04628057 ... 0.05200673 0.14935198 -0.11586972] Sparsity at: 0.03389887339055794 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1431 - accuracy: 0.9583 - val_loss: 0.1387 - val_accuracy: 0.9583 [-0.05940218 -0.00601314 -0.04628057 ... 0.05814288 0.16725424 -0.13663232] Sparsity at: 0.03389887339055794 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1186 - accuracy: 0.9654 - val_loss: 0.1255 - val_accuracy: 0.9616 [-0.05940218 -0.00601314 -0.04628057 ... 0.06137127 0.18227267 -0.15361361] Sparsity at: 0.03389887339055794 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1001 - accuracy: 0.9706 - val_loss: 0.1165 - val_accuracy: 0.9642 [-0.05940218 -0.00601314 -0.04628057 ... 0.06442316 0.19550368 -0.16942883] Sparsity at: 0.03389887339055794 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0855 - accuracy: 0.9751 - val_loss: 0.1102 - val_accuracy: 0.9659 [-0.05940218 -0.00601314 -0.04628057 ... 0.06771217 0.2068753 -0.18425012] Sparsity at: 0.03389887339055794 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0736 - accuracy: 0.9787 - val_loss: 0.1070 - val_accuracy: 0.9661 [-0.05940218 -0.00601314 -0.04628057 ... 0.07277302 0.2159994 -0.1982556 ] Sparsity at: 0.03389887339055794 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0637 - accuracy: 0.9820 - val_loss: 0.1059 - val_accuracy: 0.9661 [-0.05940218 -0.00601314 -0.04628057 ... 0.0786024 0.22384056 -0.21131212] Sparsity at: 0.03389887339055794 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0552 - accuracy: 0.9848 - val_loss: 0.1052 - val_accuracy: 0.9671 [-0.05940218 -0.00601314 -0.04628057 ... 0.08526144 0.23026066 -0.22348079] Sparsity at: 0.03389887339055794 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0481 - accuracy: 0.9867 - val_loss: 0.1059 - val_accuracy: 0.9674 [-0.05940218 -0.00601314 -0.04628057 ... 0.09223063 0.23614064 -0.2357654 ] Sparsity at: 0.03389887339055794 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0416 - accuracy: 0.9887 - val_loss: 0.1049 - val_accuracy: 0.9678 [-0.05940218 -0.00601314 -0.04628057 ... 0.09893388 0.24136019 -0.24704528] Sparsity at: 0.03389887339055794 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0361 - accuracy: 0.9905 - val_loss: 0.1048 - val_accuracy: 0.9688 [-0.05940218 -0.00601314 -0.04628057 ... 0.10543391 0.24643552 -0.2588473 ] Sparsity at: 0.03389887339055794 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0312 - accuracy: 0.9922 - val_loss: 0.1046 - val_accuracy: 0.9698 [-0.05940218 -0.00601314 -0.04628057 ... 0.11128943 0.25178874 -0.27069703] Sparsity at: 0.03389887339055794 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0269 - accuracy: 0.9938 - val_loss: 0.1058 - val_accuracy: 0.9698 [-0.05940218 -0.00601314 -0.04628057 ... 0.11687295 0.25669625 -0.28307456] Sparsity at: 0.03389887339055794 Epoch 16/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0230 - accuracy: 0.9950 - val_loss: 0.1064 - val_accuracy: 0.9706 [-0.05940218 -0.00601314 -0.04628057 ... 0.12233329 0.2628853 -0.2965134 ] Sparsity at: 0.03389887339055794 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0200 - accuracy: 0.9959 - val_loss: 0.1083 - val_accuracy: 0.9716 [-0.05940218 -0.00601314 -0.04628057 ... 0.12722383 0.26781642 -0.3099829 ] Sparsity at: 0.03389887339055794 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0169 - accuracy: 0.9970 - val_loss: 0.1096 - val_accuracy: 0.9716 [-0.05940218 -0.00601314 -0.04628057 ... 0.13265127 0.27276492 -0.32279322] Sparsity at: 0.03389887339055794 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0143 - accuracy: 0.9977 - val_loss: 0.1120 - val_accuracy: 0.9713 [-0.05940218 -0.00601314 -0.04628057 ... 0.13801153 0.2774157 -0.33601323] Sparsity at: 0.03389887339055794 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0122 - accuracy: 0.9982 - val_loss: 0.1135 - val_accuracy: 0.9717 [-0.05940218 -0.00601314 -0.04628057 ... 0.14326216 0.28218043 -0.34886774] Sparsity at: 0.03389887339055794 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0104 - accuracy: 0.9987 - val_loss: 0.1177 - val_accuracy: 0.9712 [-0.05940218 -0.00601314 -0.04628057 ... 0.14827676 0.28738096 -0.36206338] Sparsity at: 0.03389887339055794 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0091 - accuracy: 0.9988 - val_loss: 0.1221 - val_accuracy: 0.9710 [-0.05940218 -0.00601314 -0.04628057 ... 0.15419894 0.29274356 -0.37602103] Sparsity at: 0.03389887339055794 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0081 - accuracy: 0.9990 - val_loss: 0.1224 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.15875074 0.29662132 -0.38703412] Sparsity at: 0.03389887339055794 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0077 - accuracy: 0.9990 - val_loss: 0.1277 - val_accuracy: 0.9708 [-0.05940218 -0.00601314 -0.04628057 ... 0.16266237 0.30038568 -0.39776897] Sparsity at: 0.03389887339055794 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0079 - accuracy: 0.9986 - val_loss: 0.1350 - val_accuracy: 0.9708 [-0.05940218 -0.00601314 -0.04628057 ... 0.16916683 0.306584 -0.4090132 ] Sparsity at: 0.03389887339055794 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9982 - val_loss: 0.1435 - val_accuracy: 0.9693 [-0.05940218 -0.00601314 -0.04628057 ... 0.17787816 0.3090474 -0.43033794] Sparsity at: 0.03389887339055794 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0082 - accuracy: 0.9979 - val_loss: 0.1572 - val_accuracy: 0.9668 [-0.05940218 -0.00601314 -0.04628057 ... 0.18144172 0.3148685 -0.43937868] Sparsity at: 0.03389887339055794 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0093 - accuracy: 0.9977 - val_loss: 0.1579 - val_accuracy: 0.9663 [-0.05940218 -0.00601314 -0.04628057 ... 0.18820922 0.32477862 -0.45496634] Sparsity at: 0.03389887339055794 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0090 - accuracy: 0.9973 - val_loss: 0.1392 - val_accuracy: 0.9709 [-0.05940218 -0.00601314 -0.04628057 ... 0.19406983 0.32353452 -0.46231714] Sparsity at: 0.03389887339055794 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0054 - accuracy: 0.9990 - val_loss: 0.1437 - val_accuracy: 0.9704 [-0.05940218 -0.00601314 -0.04628057 ... 0.19425677 0.32384244 -0.47047985] Sparsity at: 0.03389887339055794 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9994 - val_loss: 0.1382 - val_accuracy: 0.9714 [-0.05940218 -0.00601314 -0.04628057 ... 0.19487807 0.3296997 -0.4809714 ] Sparsity at: 0.03389887339055794 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.1565 - val_accuracy: 0.9684 [-0.05940218 -0.00601314 -0.04628057 ... 0.19794552 0.3304043 -0.48628515] Sparsity at: 0.03389887339055794 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1387 - val_accuracy: 0.9717 [-0.05940218 -0.00601314 -0.04628057 ... 0.19958706 0.33018023 -0.4919303 ] Sparsity at: 0.03389887339055794 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.1446 - val_accuracy: 0.9713 [-0.05940218 -0.00601314 -0.04628057 ... 0.20211159 0.33018634 -0.49206343] Sparsity at: 0.03389887339055794 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1526 - val_accuracy: 0.9716 [-0.05940218 -0.00601314 -0.04628057 ... 0.20457648 0.33314812 -0.49380362] Sparsity at: 0.03389887339055794 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.1495 - val_accuracy: 0.9709 [-0.05940218 -0.00601314 -0.04628057 ... 0.20947328 0.33832636 -0.49930528] Sparsity at: 0.03389887339055794 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1478 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.21518593 0.33443418 -0.50703704] Sparsity at: 0.03389887339055794 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1563 - val_accuracy: 0.9716 [-0.05940218 -0.00601314 -0.04628057 ... 0.21783085 0.3409901 -0.5192698 ] Sparsity at: 0.03389887339055794 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9506e-04 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9706 [-0.05940218 -0.00601314 -0.04628057 ... 0.22173098 0.34268042 -0.5206231 ] Sparsity at: 0.03389887339055794 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3184e-04 - accuracy: 0.9999 - val_loss: 0.1511 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.2217555 0.3449013 -0.52254677] Sparsity at: 0.03389887339055794 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0106 - accuracy: 0.9965 - val_loss: 0.2183 - val_accuracy: 0.9612 [-0.05940218 -0.00601314 -0.04628057 ... 0.23324452 0.33994022 -0.53029704] Sparsity at: 0.03389887339055794 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0167 - accuracy: 0.9944 - val_loss: 0.1735 - val_accuracy: 0.9689 [-0.05940218 -0.00601314 -0.04628057 ... 0.22699447 0.34736437 -0.54328394] Sparsity at: 0.03389887339055794 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.1556 - val_accuracy: 0.9708 [-0.05940218 -0.00601314 -0.04628057 ... 0.2332056 0.34935996 -0.5521103 ] Sparsity at: 0.03389887339055794 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1579 - val_accuracy: 0.9718 [-0.05940218 -0.00601314 -0.04628057 ... 0.2379061 0.34813896 -0.5615081 ] Sparsity at: 0.03389887339055794 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 9.8159e-04 - accuracy: 0.9999 - val_loss: 0.1523 - val_accuracy: 0.9718 [-0.05940218 -0.00601314 -0.04628057 ... 0.24017219 0.34930378 -0.56211096] Sparsity at: 0.03389887339055794 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 6.2018e-04 - accuracy: 1.0000 - val_loss: 0.1538 - val_accuracy: 0.9718 [-0.05940218 -0.00601314 -0.04628057 ... 0.24123164 0.34891978 -0.5664592 ] Sparsity at: 0.03389887339055794 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3618e-04 - accuracy: 1.0000 - val_loss: 0.1544 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.24187332 0.34923056 -0.569474 ] Sparsity at: 0.03389887339055794 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5330e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.24225368 0.35001963 -0.572502 ] Sparsity at: 0.03389887339055794 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0884e-04 - accuracy: 1.0000 - val_loss: 0.1554 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.24264894 0.3508149 -0.5758019 ] Sparsity at: 0.03389887339055794 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7624e-04 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.24313384 0.35161105 -0.57921016] Sparsity at: 0.03389887339055794 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.12466052184773169 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.18753654881625081 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.43717587914926526 Thresholhold 0.06464134901762009 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 51s 7ms/step - loss: 2.5263e-04 - accuracy: 1.0000 - val_loss: 0.1570 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.24367297 0.3523807 -0.58303964] Sparsity at: 0.036061561158798286 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 2.2820e-04 - accuracy: 1.0000 - val_loss: 0.1580 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.24443471 0.35329822 -0.5868884 ] Sparsity at: 0.036061561158798286 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0809e-04 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.24530917 0.3542586 -0.590967 ] Sparsity at: 0.036061561158798286 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9008e-04 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.24631745 0.3552871 -0.59526014] Sparsity at: 0.036061561158798286 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7388e-04 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.24744877 0.35640043 -0.59972507] Sparsity at: 0.036061561158798286 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5945e-04 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.2486416 0.35758874 -0.60441893] Sparsity at: 0.036061561158798286 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4565e-04 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.2498594 0.3588183 -0.6093436 ] Sparsity at: 0.036061561158798286 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3322e-04 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.2511337 0.36016235 -0.61446464] Sparsity at: 0.036061561158798286 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2169e-04 - accuracy: 1.0000 - val_loss: 0.1663 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.25241202 0.36150485 -0.6198472 ] Sparsity at: 0.036061561158798286 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1109e-04 - accuracy: 1.0000 - val_loss: 0.1677 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.25370044 0.36290064 -0.62545013] Sparsity at: 0.036061561158798286 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0123e-04 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.25521642 0.3643691 -0.6311998 ] Sparsity at: 0.036061561158798286 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 9.2011e-05 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.25663424 0.3658786 -0.637148 ] Sparsity at: 0.036061561158798286 Epoch 63/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3623e-05 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.25815254 0.36742285 -0.6433546 ] Sparsity at: 0.036061561158798286 Epoch 64/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5786e-05 - accuracy: 1.0000 - val_loss: 0.1737 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.25967544 0.3690472 -0.64968497] Sparsity at: 0.036061561158798286 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 6.8594e-05 - accuracy: 1.0000 - val_loss: 0.1754 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.26120433 0.37070265 -0.6562151 ] Sparsity at: 0.036061561158798286 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1934e-05 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.2627519 0.3724224 -0.6629668 ] Sparsity at: 0.036061561158798286 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5940e-05 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.2643473 0.3741994 -0.6698568 ] Sparsity at: 0.036061561158798286 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0343e-05 - accuracy: 1.0000 - val_loss: 0.1804 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.26593775 0.37601003 -0.6769373 ] Sparsity at: 0.036061561158798286 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 4.5286e-05 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.26757812 0.37785307 -0.68411005] Sparsity at: 0.036061561158798286 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0626e-05 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.269156 0.37974578 -0.69150627] Sparsity at: 0.036061561158798286 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6408e-05 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.2708193 0.3816925 -0.69897497] Sparsity at: 0.036061561158798286 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2610e-05 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.27251378 0.38366756 -0.70667696] Sparsity at: 0.036061561158798286 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9146e-05 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.27419886 0.38570395 -0.7144129 ] Sparsity at: 0.036061561158798286 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6020e-05 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.2758885 0.38772577 -0.72228426] Sparsity at: 0.036061561158798286 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3207e-05 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.2775986 0.38985962 -0.73020875] Sparsity at: 0.036061561158798286 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0659e-05 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.27940014 0.392011 -0.7383061 ] Sparsity at: 0.036061561158798286 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8402e-05 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.2810973 0.3941745 -0.7464559 ] Sparsity at: 0.036061561158798286 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6330e-05 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.28283328 0.39637396 -0.75476164] Sparsity at: 0.036061561158798286 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4518e-05 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.2844636 0.39860216 -0.7629115 ] Sparsity at: 0.036061561158798286 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2893e-05 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.2862499 0.40082029 -0.771361 ] Sparsity at: 0.036061561158798286 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1420e-05 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.28783566 0.40309924 -0.779747 ] Sparsity at: 0.036061561158798286 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0115e-05 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.28949818 0.40536532 -0.788187 ] Sparsity at: 0.036061561158798286 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 8.9538e-06 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.29120973 0.4076956 -0.79667705] Sparsity at: 0.036061561158798286 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 7.9362e-06 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.29291773 0.40998065 -0.80517226] Sparsity at: 0.036061561158798286 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0274e-06 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.29466718 0.41232425 -0.8137191 ] Sparsity at: 0.036061561158798286 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2212e-06 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.29637045 0.41463712 -0.82225025] Sparsity at: 0.036061561158798286 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5017e-06 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.29801774 0.4169323 -0.8307521 ] Sparsity at: 0.036061561158798286 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8656e-06 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.29971218 0.41927862 -0.83933234] Sparsity at: 0.036061561158798286 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2986e-06 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.30141833 0.42163005 -0.8478284 ] Sparsity at: 0.036061561158798286 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8024e-06 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.30308437 0.42391056 -0.8564479 ] Sparsity at: 0.036061561158798286 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3664e-06 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.3047794 0.42620647 -0.8649628 ] Sparsity at: 0.036061561158798286 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9741e-06 - accuracy: 1.0000 - val_loss: 0.2264 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.30643582 0.42853895 -0.8735429 ] Sparsity at: 0.036061561158798286 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6304e-06 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.30815512 0.43083894 -0.8820202 ] Sparsity at: 0.036061561158798286 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3265e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.30968112 0.43320584 -0.8905162 ] Sparsity at: 0.036061561158798286 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0561e-06 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.31121066 0.43547422 -0.8989246 ] Sparsity at: 0.036061561158798286 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8199e-06 - accuracy: 1.0000 - val_loss: 0.2345 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.312879 0.43774602 -0.90741193] Sparsity at: 0.036061561158798286 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6108e-06 - accuracy: 1.0000 - val_loss: 0.2366 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.3145279 0.44009018 -0.91578734] Sparsity at: 0.036061561158798286 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4253e-06 - accuracy: 1.0000 - val_loss: 0.2386 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.31607017 0.44235307 -0.9241536 ] Sparsity at: 0.036061561158798286 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2638e-06 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.31765768 0.4446322 -0.9324005 ] Sparsity at: 0.036061561158798286 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1206e-06 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.31922382 0.4468842 -0.94068354] Sparsity at: 0.036061561158798286 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.1777579907073843 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.28248873381016537 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.7064567878092234 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 9.9216e-07 - accuracy: 1.0000 - val_loss: 0.2445 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.32077536 0.44915947 -0.9488824 ] Sparsity at: 0.036061561158798286 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 8.8317e-07 - accuracy: 1.0000 - val_loss: 0.2466 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.32216695 0.45138523 -0.9570187 ] Sparsity at: 0.036061561158798286 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8301e-07 - accuracy: 1.0000 - val_loss: 0.2483 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.3237306 0.45361713 -0.96516657] Sparsity at: 0.036061561158798286 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9567e-07 - accuracy: 1.0000 - val_loss: 0.2504 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.3251415 0.4558646 -0.9731345 ] Sparsity at: 0.036061561158798286 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1827e-07 - accuracy: 1.0000 - val_loss: 0.2521 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.3266561 0.45807195 -0.98110086] Sparsity at: 0.036061561158798286 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5029e-07 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.32811597 0.46028712 -0.98895335] Sparsity at: 0.036061561158798286 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 4.8991e-07 - accuracy: 1.0000 - val_loss: 0.2560 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.32952535 0.46246526 -0.9967266 ] Sparsity at: 0.036061561158798286 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3768e-07 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.33090702 0.46456534 -1.0044128 ] Sparsity at: 0.036061561158798286 Epoch 109/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9142e-07 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.3322797 0.46673286 -1.0120156 ] Sparsity at: 0.036061561158798286 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4989e-07 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.33361828 0.46890292 -1.0194789 ] Sparsity at: 0.036061561158798286 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1303e-07 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.33497915 0.47099724 -1.0268598 ] Sparsity at: 0.036061561158798286 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8056e-07 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.33630833 0.47309563 -1.0341535 ] Sparsity at: 0.036061561158798286 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5206e-07 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.33753088 0.4751327 -1.0413007 ] Sparsity at: 0.036061561158798286 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2632e-07 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.3388323 0.47715908 -1.0483243 ] Sparsity at: 0.036061561158798286 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0369e-07 - accuracy: 1.0000 - val_loss: 0.2705 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.34005743 0.47920534 -1.055242 ] Sparsity at: 0.036061561158798286 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8383e-07 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.34125978 0.48114258 -1.0619344 ] Sparsity at: 0.036061561158798286 Epoch 117/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6627e-07 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9724 [-0.05940218 -0.00601314 -0.04628057 ... 0.3424438 0.48305067 -1.0685158 ] Sparsity at: 0.036061561158798286 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5061e-07 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.34358823 0.48495767 -1.0750087 ] Sparsity at: 0.036061561158798286 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3693e-07 - accuracy: 1.0000 - val_loss: 0.2771 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.3446664 0.48679602 -1.0812649 ] Sparsity at: 0.036061561158798286 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2442e-07 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.34576535 0.48862296 -1.0873562 ] Sparsity at: 0.036061561158798286 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1349e-07 - accuracy: 1.0000 - val_loss: 0.2798 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.34679845 0.49042913 -1.0933244 ] Sparsity at: 0.036061561158798286 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0358e-07 - accuracy: 1.0000 - val_loss: 0.2814 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.34782377 0.492172 -1.0991111 ] Sparsity at: 0.036061561158798286 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 9.4958e-08 - accuracy: 1.0000 - val_loss: 0.2826 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.34882167 0.49385104 -1.1046981 ] Sparsity at: 0.036061561158798286 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7098e-08 - accuracy: 1.0000 - val_loss: 0.2840 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.34984297 0.4955171 -1.1101247 ] Sparsity at: 0.036061561158798286 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0113e-08 - accuracy: 1.0000 - val_loss: 0.2855 - val_accuracy: 0.9720 [-0.05940218 -0.00601314 -0.04628057 ... 0.3507149 0.49710315 -1.1153219 ] Sparsity at: 0.036061561158798286 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 7.3922e-08 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.3515817 0.4986421 -1.1203995 ] Sparsity at: 0.036061561158798286 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 6.8325e-08 - accuracy: 1.0000 - val_loss: 0.2879 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.35243478 0.5001407 -1.1252488 ] Sparsity at: 0.036061561158798286 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3278e-08 - accuracy: 1.0000 - val_loss: 0.2891 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.35319194 0.50155824 -1.1299348 ] Sparsity at: 0.036061561158798286 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8842e-08 - accuracy: 1.0000 - val_loss: 0.2902 - val_accuracy: 0.9721 [-0.05940218 -0.00601314 -0.04628057 ... 0.3539534 0.503002 -1.1344548 ] Sparsity at: 0.036061561158798286 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4679e-08 - accuracy: 1.0000 - val_loss: 0.2913 - val_accuracy: 0.9722 [-0.05940218 -0.00601314 -0.04628057 ... 0.35470918 0.50437796 -1.138862 ] Sparsity at: 0.036061561158798286 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1024e-08 - accuracy: 1.0000 - val_loss: 0.2924 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.3554769 0.505696 -1.1430457 ] Sparsity at: 0.036061561158798286 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 4.7682e-08 - accuracy: 1.0000 - val_loss: 0.2934 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.35615498 0.5069446 -1.1470921 ] Sparsity at: 0.036061561158798286 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4622e-08 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9723 [-0.05940218 -0.00601314 -0.04628057 ... 0.35680446 0.5081799 -1.1509615 ] Sparsity at: 0.036061561158798286 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2065e-08 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.3574548 0.5093881 -1.1546996 ] Sparsity at: 0.036061561158798286 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9470e-08 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.35809693 0.5105564 -1.1582747 ] Sparsity at: 0.036061561158798286 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7336e-08 - accuracy: 1.0000 - val_loss: 0.2973 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.35867622 0.5117009 -1.1617181 ] Sparsity at: 0.036061561158798286 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5165e-08 - accuracy: 1.0000 - val_loss: 0.2981 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.3592626 0.5127769 -1.165033 ] Sparsity at: 0.036061561158798286 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3299e-08 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.35981143 0.51380795 -1.1682234 ] Sparsity at: 0.036061561158798286 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1495e-08 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.36035112 0.51477534 -1.1713048 ] Sparsity at: 0.036061561158798286 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9943e-08 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36085218 0.5157216 -1.1742716 ] Sparsity at: 0.036061561158798286 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8433e-08 - accuracy: 1.0000 - val_loss: 0.3013 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36135888 0.51656955 -1.1771457 ] Sparsity at: 0.036061561158798286 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7005e-08 - accuracy: 1.0000 - val_loss: 0.3019 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36181638 0.5173682 -1.179869 ] Sparsity at: 0.036061561158798286 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5870e-08 - accuracy: 1.0000 - val_loss: 0.3026 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36223406 0.5181281 -1.1825261 ] Sparsity at: 0.036061561158798286 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4656e-08 - accuracy: 1.0000 - val_loss: 0.3033 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36261612 0.51884675 -1.1850615 ] Sparsity at: 0.036061561158798286 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3627e-08 - accuracy: 1.0000 - val_loss: 0.3039 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36298308 0.5195678 -1.1875218 ] Sparsity at: 0.036061561158798286 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2632e-08 - accuracy: 1.0000 - val_loss: 0.3045 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3633582 0.52022207 -1.1898788 ] Sparsity at: 0.036061561158798286 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1770e-08 - accuracy: 1.0000 - val_loss: 0.3053 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3637365 0.52088064 -1.1921483 ] Sparsity at: 0.036061561158798286 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0953e-08 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36409712 0.5214975 -1.1943593 ] Sparsity at: 0.036061561158798286 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0126e-08 - accuracy: 1.0000 - val_loss: 0.3065 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36442307 0.52206886 -1.1965054 ] Sparsity at: 0.036061561158798286 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9403e-08 - accuracy: 1.0000 - val_loss: 0.3071 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36472526 0.52264017 -1.1985694 ] Sparsity at: 0.036061561158798286 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.23543465671956376 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.37804899443405304 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.9533427882879089 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 1.8716e-08 - accuracy: 1.0000 - val_loss: 0.3077 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3650185 0.5231604 -1.2005807 ] Sparsity at: 0.036061561158798286 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8072e-08 - accuracy: 1.0000 - val_loss: 0.3083 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36528185 0.52366614 -1.2025083 ] Sparsity at: 0.036061561158798286 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7486e-08 - accuracy: 1.0000 - val_loss: 0.3087 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36554983 0.5241782 -1.2043424 ] Sparsity at: 0.036061561158798286 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6942e-08 - accuracy: 1.0000 - val_loss: 0.3093 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.3658118 0.5246774 -1.2061359 ] Sparsity at: 0.036061561158798286 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6344e-08 - accuracy: 1.0000 - val_loss: 0.3098 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.36608312 0.52512 -1.2078426 ] Sparsity at: 0.036061561158798286 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5855e-08 - accuracy: 1.0000 - val_loss: 0.3103 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.3663202 0.5255552 -1.2095631 ] Sparsity at: 0.036061561158798286 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5380e-08 - accuracy: 1.0000 - val_loss: 0.3108 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.36656913 0.52596104 -1.2112132 ] Sparsity at: 0.036061561158798286 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4965e-08 - accuracy: 1.0000 - val_loss: 0.3112 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36684233 0.52633184 -1.2128217 ] Sparsity at: 0.036061561158798286 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4528e-08 - accuracy: 1.0000 - val_loss: 0.3117 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36708415 0.5267006 -1.2144184 ] Sparsity at: 0.036061561158798286 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4124e-08 - accuracy: 1.0000 - val_loss: 0.3122 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36732408 0.5270325 -1.2159587 ] Sparsity at: 0.036061561158798286 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3745e-08 - accuracy: 1.0000 - val_loss: 0.3127 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36755633 0.52734774 -1.2174815 ] Sparsity at: 0.036061561158798286 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3347e-08 - accuracy: 1.0000 - val_loss: 0.3131 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.3677866 0.5276297 -1.2189165 ] Sparsity at: 0.036061561158798286 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2998e-08 - accuracy: 1.0000 - val_loss: 0.3135 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.368002 0.527866 -1.2203283 ] Sparsity at: 0.036061561158798286 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2690e-08 - accuracy: 1.0000 - val_loss: 0.3139 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36820224 0.52811056 -1.2217069 ] Sparsity at: 0.036061561158798286 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2364e-08 - accuracy: 1.0000 - val_loss: 0.3143 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36838797 0.5283562 -1.2230624 ] Sparsity at: 0.036061561158798286 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2100e-08 - accuracy: 1.0000 - val_loss: 0.3147 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36857566 0.5285898 -1.2243809 ] Sparsity at: 0.036061561158798286 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1796e-08 - accuracy: 1.0000 - val_loss: 0.3151 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36875585 0.5287983 -1.2256767 ] Sparsity at: 0.036061561158798286 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1514e-08 - accuracy: 1.0000 - val_loss: 0.3155 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36891288 0.52900386 -1.2269247 ] Sparsity at: 0.036061561158798286 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1323e-08 - accuracy: 1.0000 - val_loss: 0.3158 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36907277 0.5291965 -1.2281431 ] Sparsity at: 0.036061561158798286 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1045e-08 - accuracy: 1.0000 - val_loss: 0.3161 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3692305 0.5293664 -1.2293657 ] Sparsity at: 0.036061561158798286 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0759e-08 - accuracy: 1.0000 - val_loss: 0.3165 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36938813 0.52953094 -1.230559 ] Sparsity at: 0.036061561158798286 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0580e-08 - accuracy: 1.0000 - val_loss: 0.3168 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.3695263 0.5296838 -1.2317338 ] Sparsity at: 0.036061561158798286 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0339e-08 - accuracy: 1.0000 - val_loss: 0.3171 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.36966577 0.5298433 -1.2329025 ] Sparsity at: 0.036061561158798286 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0153e-08 - accuracy: 1.0000 - val_loss: 0.3175 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36982587 0.52999115 -1.2340353 ] Sparsity at: 0.036061561158798286 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 9.8924e-09 - accuracy: 1.0000 - val_loss: 0.3178 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.36996588 0.5300925 -1.23515 ] Sparsity at: 0.036061561158798286 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7315e-09 - accuracy: 1.0000 - val_loss: 0.3181 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3701052 0.5301961 -1.2362359 ] Sparsity at: 0.036061561158798286 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 9.5546e-09 - accuracy: 1.0000 - val_loss: 0.3184 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37023664 0.5302996 -1.237342 ] Sparsity at: 0.036061561158798286 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 9.3857e-09 - accuracy: 1.0000 - val_loss: 0.3187 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37035912 0.53039736 -1.2383842 ] Sparsity at: 0.036061561158798286 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 9.1732e-09 - accuracy: 1.0000 - val_loss: 0.3189 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37050477 0.53049564 -1.2393944 ] Sparsity at: 0.036061561158798286 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 9.0559e-09 - accuracy: 1.0000 - val_loss: 0.3191 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37061685 0.530596 -1.2404022 ] Sparsity at: 0.036061561158798286 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 8.8255e-09 - accuracy: 1.0000 - val_loss: 0.3194 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37075382 0.5306733 -1.2413902 ] Sparsity at: 0.036061561158798286 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 8.7023e-09 - accuracy: 1.0000 - val_loss: 0.3197 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37089917 0.530762 -1.242333 ] Sparsity at: 0.036061561158798286 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5870e-09 - accuracy: 1.0000 - val_loss: 0.3199 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37102923 0.5308503 -1.2432815 ] Sparsity at: 0.036061561158798286 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3943e-09 - accuracy: 1.0000 - val_loss: 0.3202 - val_accuracy: 0.9736 [-0.05940218 -0.00601314 -0.04628057 ... 0.3711411 0.53092235 -1.2442253 ] Sparsity at: 0.036061561158798286 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2473e-09 - accuracy: 1.0000 - val_loss: 0.3204 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.3712636 0.53098273 -1.2451632 ] Sparsity at: 0.036061561158798286 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 8.1440e-09 - accuracy: 1.0000 - val_loss: 0.3206 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.3713761 0.5310613 -1.2460926 ] Sparsity at: 0.036061561158798286 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 7.9870e-09 - accuracy: 1.0000 - val_loss: 0.3210 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37146538 0.53110147 -1.2470101 ] Sparsity at: 0.036061561158798286 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 7.8400e-09 - accuracy: 1.0000 - val_loss: 0.3212 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37154433 0.531137 -1.2479092 ] Sparsity at: 0.036061561158798286 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7387e-09 - accuracy: 1.0000 - val_loss: 0.3214 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37164173 0.53117794 -1.2488012 ] Sparsity at: 0.036061561158798286 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5777e-09 - accuracy: 1.0000 - val_loss: 0.3216 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37173265 0.5312083 -1.2496612 ] Sparsity at: 0.036061561158798286 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5201e-09 - accuracy: 1.0000 - val_loss: 0.3218 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.3718344 0.5312695 -1.2505108 ] Sparsity at: 0.036061561158798286 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3651e-09 - accuracy: 1.0000 - val_loss: 0.3220 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37194136 0.53131557 -1.2513653 ] Sparsity at: 0.036061561158798286 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 7.2300e-09 - accuracy: 1.0000 - val_loss: 0.3221 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37201995 0.53134334 -1.2521983 ] Sparsity at: 0.036061561158798286 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 7.1386e-09 - accuracy: 1.0000 - val_loss: 0.3224 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.3721058 0.5313626 -1.2530528 ] Sparsity at: 0.036061561158798286 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0671e-09 - accuracy: 1.0000 - val_loss: 0.3225 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37220564 0.5314044 -1.2538744 ] Sparsity at: 0.036061561158798286 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9499e-09 - accuracy: 1.0000 - val_loss: 0.3226 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37228486 0.53141016 -1.2546892 ] Sparsity at: 0.036061561158798286 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 6.8347e-09 - accuracy: 1.0000 - val_loss: 0.3228 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37235153 0.53143054 -1.2554952 ] Sparsity at: 0.036061561158798286 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 6.7155e-09 - accuracy: 1.0000 - val_loss: 0.3230 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37244567 0.53141725 -1.2562755 ] Sparsity at: 0.036061561158798286 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 6.6559e-09 - accuracy: 1.0000 - val_loss: 0.3231 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.3725268 0.5314011 -1.2570539 ] Sparsity at: 0.036061561158798286 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5049e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.3725928 0.53135365 -1.2578161 ] Sparsity at: 0.036061561158798286 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.29506325402118705 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.4555327289969071 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 1.1360591471881776 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 6.4691e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.3726702 0.5313338 -1.2585928 ] Sparsity at: 0.036061561158798286 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 6.3340e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37271845 0.531315 -1.2593386 ] Sparsity at: 0.036061561158798286 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2585e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.3727929 0.5312752 -1.260086 ] Sparsity at: 0.036061561158798286 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1750e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37284914 0.5312293 -1.2608207 ] Sparsity at: 0.036061561158798286 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1075e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37290972 0.5311705 -1.2615368 ] Sparsity at: 0.036061561158798286 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 5.9823e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37295794 0.53113633 -1.2622548 ] Sparsity at: 0.036061561158798286 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 5.8830e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37300938 0.5310511 -1.262961 ] Sparsity at: 0.036061561158798286 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 5.8393e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9735 [-0.05940218 -0.00601314 -0.04628057 ... 0.37305403 0.53095496 -1.2636534 ] Sparsity at: 0.036061561158798286 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7499e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37309292 0.5308613 -1.2643573 ] Sparsity at: 0.036061561158798286 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6863e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37315693 0.53077614 -1.2650367 ] Sparsity at: 0.036061561158798286 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6028e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37319955 0.5306883 -1.265726 ] Sparsity at: 0.036061561158798286 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5889e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37325287 0.5306084 -1.2663789 ] Sparsity at: 0.036061561158798286 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 5.4955e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.3733038 0.53052443 -1.2670349 ] Sparsity at: 0.036061561158798286 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4061e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.3733592 0.5304374 -1.2676969 ] Sparsity at: 0.036061561158798286 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3525e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37339056 0.5303309 -1.2683264 ] Sparsity at: 0.036061561158798286 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2929e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.3734238 0.5302341 -1.268955 ] Sparsity at: 0.036061561158798286 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2671e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9734 [-0.05940218 -0.00601314 -0.04628057 ... 0.37348118 0.53014904 -1.2695562 ] Sparsity at: 0.036061561158798286 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1816e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37352023 0.53004664 -1.2701916 ] Sparsity at: 0.036061561158798286 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1101e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37355566 0.5299307 -1.2708026 ] Sparsity at: 0.036061561158798286 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0525e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37360355 0.52983177 -1.2713892 ] Sparsity at: 0.036061561158798286 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0068e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.37361625 0.5297242 -1.2719827 ] Sparsity at: 0.036061561158798286 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9671e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9733 [-0.05940218 -0.00601314 -0.04628057 ... 0.373659 0.52962226 -1.2725705 ] Sparsity at: 0.036061561158798286 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 4.8876e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37370038 0.5295085 -1.2731571 ] Sparsity at: 0.036061561158798286 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 4.8240e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37373602 0.5293984 -1.2737247 ] Sparsity at: 0.036061561158798286 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7843e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3737719 0.5292881 -1.2742908 ] Sparsity at: 0.036061561158798286 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 4.7723e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37380368 0.52917385 -1.2748594 ] Sparsity at: 0.036061561158798286 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6551e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37383416 0.5290347 -1.2754354 ] Sparsity at: 0.036061561158798286 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6412e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3738923 0.52889943 -1.275976 ] Sparsity at: 0.036061561158798286 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5876e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3739303 0.52879244 -1.2765222 ] Sparsity at: 0.036061561158798286 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5439e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37397715 0.528663 -1.2770733 ] Sparsity at: 0.036061561158798286 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4902e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3740148 0.52851105 -1.277631 ] Sparsity at: 0.036061561158798286 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4167e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37407872 0.52834666 -1.2781562 ] Sparsity at: 0.036061561158798286 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3770e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37411058 0.528196 -1.2786919 ] Sparsity at: 0.036061561158798286 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3313e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37414593 0.5280488 -1.279211 ] Sparsity at: 0.036061561158798286 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2935e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37417608 0.5279016 -1.2797203 ] Sparsity at: 0.036061561158798286 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2677e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37422577 0.5277442 -1.2802407 ] Sparsity at: 0.036061561158798286 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1902e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37427002 0.5275785 -1.2807403 ] Sparsity at: 0.036061561158798286 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1604e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37428844 0.52741057 -1.2812531 ] Sparsity at: 0.036061561158798286 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1425e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37431946 0.5272479 -1.2817591 ] Sparsity at: 0.036061561158798286 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1087e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37435773 0.5270909 -1.2822707 ] Sparsity at: 0.036061561158798286 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0531e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37436128 0.52691054 -1.2827698 ] Sparsity at: 0.036061561158798286 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0611e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37439308 0.526747 -1.2832632 ] Sparsity at: 0.036061561158798286 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0392e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37441924 0.5265964 -1.2837512 ] Sparsity at: 0.036061561158798286 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9399e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37442172 0.52643144 -1.2842286 ] Sparsity at: 0.036061561158798286 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9856e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37444612 0.52626836 -1.2847204 ] Sparsity at: 0.036061561158798286 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8902e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3744725 0.5260815 -1.285209 ] Sparsity at: 0.036061561158798286 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8763e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37449235 0.52592057 -1.2856705 ] Sparsity at: 0.036061561158798286 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8584e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745018 0.5257238 -1.2861542 ] Sparsity at: 0.036061561158798286 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8028e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37450033 0.5255538 -1.2866144 ] Sparsity at: 0.036061561158798286 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7611e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37451905 0.52535456 -1.287095 ] Sparsity at: 0.036061561158798286 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.3589129205544168 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.5320120862464677 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 1.288071074504856 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 3.7869e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37453255 0.52518344 -1.2875634 ] Sparsity at: 0.036061561158798286 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 3.7193e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37453893 0.5249683 -1.2880385 ] Sparsity at: 0.036061561158798286 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7233e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455785 0.5247879 -1.2884748 ] Sparsity at: 0.036061561158798286 Epoch 254/500 235/235 [==============================] - 2s 10ms/step - loss: 3.6558e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37454426 0.5245993 -1.2889473 ] Sparsity at: 0.036061561158798286 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6220e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745522 0.52438486 -1.2893964 ] Sparsity at: 0.036061561158798286 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6279e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745384 0.5242022 -1.289853 ] Sparsity at: 0.036061561158798286 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5763e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455633 0.5240113 -1.2903045 ] Sparsity at: 0.036061561158798286 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5544e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745623 0.523799 -1.2907453 ] Sparsity at: 0.036061561158798286 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5624e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745711 0.52361375 -1.2912056 ] Sparsity at: 0.036061561158798286 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5008e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37457135 0.5233972 -1.2916387 ] Sparsity at: 0.036061561158798286 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4829e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37457293 0.5231888 -1.2920777 ] Sparsity at: 0.036061561158798286 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4491e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37457472 0.52298445 -1.2925158 ] Sparsity at: 0.036061561158798286 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3836e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455934 0.52275056 -1.2929486 ] Sparsity at: 0.036061561158798286 Epoch 264/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4491e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37454963 0.5225236 -1.2933884 ] Sparsity at: 0.036061561158798286 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3716e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37454018 0.52229095 -1.2938296 ] Sparsity at: 0.036061561158798286 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3935e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745457 0.5220874 -1.2942737 ] Sparsity at: 0.036061561158798286 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3796e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37453622 0.5218652 -1.2946929 ] Sparsity at: 0.036061561158798286 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3498e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455365 0.5216467 -1.295136 ] Sparsity at: 0.036061561158798286 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2604e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745576 0.52142465 -1.2955457 ] Sparsity at: 0.036061561158798286 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3359e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455934 0.5212357 -1.2959728 ] Sparsity at: 0.036061561158798286 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2922e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37456656 0.521047 -1.2963836 ] Sparsity at: 0.036061561158798286 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2485e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455577 0.52081543 -1.2968068 ] Sparsity at: 0.036061561158798286 Epoch 273/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2584e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37455684 0.5206027 -1.29723 ] Sparsity at: 0.036061561158798286 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2047e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745533 0.52038664 -1.2976494 ] Sparsity at: 0.036061561158798286 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2028e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3745627 0.52016073 -1.2980654 ] Sparsity at: 0.036061561158798286 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1710e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37454444 0.51992947 -1.2984579 ] Sparsity at: 0.036061561158798286 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1511e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37451687 0.5197071 -1.2988719 ] Sparsity at: 0.036061561158798286 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1610e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37451735 0.519455 -1.2992994 ] Sparsity at: 0.036061561158798286 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1133e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37449437 0.5192113 -1.2996916 ] Sparsity at: 0.036061561158798286 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0994e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3744859 0.5189654 -1.3000956 ] Sparsity at: 0.036061561158798286 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0537e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37446934 0.51872987 -1.3004866 ] Sparsity at: 0.036061561158798286 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0577e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37447476 0.51847893 -1.3008807 ] Sparsity at: 0.036061561158798286 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0180e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37444812 0.51823044 -1.301266 ] Sparsity at: 0.036061561158798286 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0518e-09 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37442675 0.517988 -1.3016759 ] Sparsity at: 0.036061561158798286 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.374419 0.51774865 -1.3020513 ] Sparsity at: 0.036061561158798286 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9902e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.3744029 0.51751006 -1.3024378 ] Sparsity at: 0.036061561158798286 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0220e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37438506 0.51726395 -1.3028284 ] Sparsity at: 0.036061561158798286 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9544e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37437615 0.5170244 -1.3032199 ] Sparsity at: 0.036061561158798286 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9822e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37437955 0.5167742 -1.303588 ] Sparsity at: 0.036061561158798286 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9524e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.37437275 0.5165289 -1.3039718 ] Sparsity at: 0.036061561158798286 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9067e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37435326 0.51628906 -1.304344 ] Sparsity at: 0.036061561158798286 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9484e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.37432626 0.51604944 -1.3047217 ] Sparsity at: 0.036061561158798286 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9504e-09 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37432408 0.51579857 -1.305094 ] Sparsity at: 0.036061561158798286 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8908e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.3743226 0.51555115 -1.3054786 ] Sparsity at: 0.036061561158798286 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8829e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.37430257 0.5153222 -1.305834 ] Sparsity at: 0.036061561158798286 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8590e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37428576 0.5150695 -1.3062319 ] Sparsity at: 0.036061561158798286 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9008e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37427464 0.5148128 -1.3066056 ] Sparsity at: 0.036061561158798286 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8670e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37427315 0.51457566 -1.3069621 ] Sparsity at: 0.036061561158798286 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8392e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37424603 0.5143179 -1.3073198 ] Sparsity at: 0.036061561158798286 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8253e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9725 [-0.05940218 -0.00601314 -0.04628057 ... 0.3742357 0.5140678 -1.3076735 ] Sparsity at: 0.036061561158798286 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.4284913756669084 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.6062501564149656 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 1.4495890268796359 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 2.8213e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37421086 0.51382315 -1.3080279 ] Sparsity at: 0.036061561158798286 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 2.8690e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37420675 0.5135829 -1.3084192 ] Sparsity at: 0.036061561158798286 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37420508 0.5133288 -1.3087713 ] Sparsity at: 0.036061561158798286 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7796e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37419993 0.5130727 -1.3091253 ] Sparsity at: 0.036061561158798286 Epoch 305/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7935e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37418437 0.5128076 -1.3094857 ] Sparsity at: 0.036061561158798286 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37415418 0.5125172 -1.3098589 ] Sparsity at: 0.036061561158798286 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7359e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37413776 0.5122655 -1.3101856 ] Sparsity at: 0.036061561158798286 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7577e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37412596 0.51200855 -1.3105512 ] Sparsity at: 0.036061561158798286 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3741374 0.51174074 -1.3108892 ] Sparsity at: 0.036061561158798286 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7597e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37411097 0.51148605 -1.3112578 ] Sparsity at: 0.036061561158798286 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7100e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37408814 0.5112492 -1.311597 ] Sparsity at: 0.036061561158798286 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3740715 0.511002 -1.3119444 ] Sparsity at: 0.036061561158798286 Epoch 313/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6683e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3740448 0.5107361 -1.3123186 ] Sparsity at: 0.036061561158798286 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37403712 0.5104731 -1.3126812 ] Sparsity at: 0.036061561158798286 Epoch 315/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7299e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3740164 0.510207 -1.3130152 ] Sparsity at: 0.036061561158798286 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6425e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3739978 0.50993425 -1.3133696 ] Sparsity at: 0.036061561158798286 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6464e-09 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37398255 0.5096798 -1.3136939 ] Sparsity at: 0.036061561158798286 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7100e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37395242 0.5094016 -1.3140361 ] Sparsity at: 0.036061561158798286 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6365e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37392282 0.50914365 -1.3143903 ] Sparsity at: 0.036061561158798286 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6484e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9726 [-0.05940218 -0.00601314 -0.04628057 ... 0.37389714 0.50886065 -1.314751 ] Sparsity at: 0.036061561158798286 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3738661 0.5086197 -1.315081 ] Sparsity at: 0.036061561158798286 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6405e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37384456 0.508336 -1.3154365 ] Sparsity at: 0.036061561158798286 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6484e-09 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37381685 0.5081033 -1.3157908 ] Sparsity at: 0.036061561158798286 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6306e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.3737876 0.50780493 -1.3161472 ] Sparsity at: 0.036061561158798286 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5729e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37375033 0.50752443 -1.3164933 ] Sparsity at: 0.036061561158798286 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5570e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.373706 0.5072209 -1.3168185 ] Sparsity at: 0.036061561158798286 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6385e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37369674 0.5069379 -1.317186 ] Sparsity at: 0.036061561158798286 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5551e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37367252 0.50666493 -1.3175242 ] Sparsity at: 0.036061561158798286 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5829e-09 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37363446 0.5063862 -1.3178571 ] Sparsity at: 0.036061561158798286 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5868e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37362015 0.50613254 -1.3181936 ] Sparsity at: 0.036061561158798286 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9727 [-0.05940218 -0.00601314 -0.04628057 ... 0.37359676 0.5058522 -1.3185344 ] Sparsity at: 0.036061561158798286 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5570e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37358427 0.5055932 -1.3188852 ] Sparsity at: 0.036061561158798286 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5332e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3735248 0.5052797 -1.319237 ] Sparsity at: 0.036061561158798286 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5829e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37347952 0.50502115 -1.319602 ] Sparsity at: 0.036061561158798286 Epoch 335/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5650e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37346476 0.5047515 -1.3199508 ] Sparsity at: 0.036061561158798286 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5133e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37342778 0.5044435 -1.3202884 ] Sparsity at: 0.036061561158798286 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5113e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37338725 0.50414145 -1.3206134 ] Sparsity at: 0.036061561158798286 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5471e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37335595 0.50383985 -1.3209686 ] Sparsity at: 0.036061561158798286 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5590e-09 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.373305 0.5035547 -1.3213125 ] Sparsity at: 0.036061561158798286 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4736e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3732648 0.50323546 -1.3216718 ] Sparsity at: 0.036061561158798286 Epoch 341/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5630e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37322775 0.5029671 -1.322041 ] Sparsity at: 0.036061561158798286 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4855e-09 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37320125 0.50266546 -1.3223925 ] Sparsity at: 0.036061561158798286 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4815e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3731738 0.50238395 -1.3227296 ] Sparsity at: 0.036061561158798286 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5312e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37312672 0.5020975 -1.3230311 ] Sparsity at: 0.036061561158798286 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4498e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37309828 0.50178486 -1.3233533 ] Sparsity at: 0.036061561158798286 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4776e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3730637 0.5014854 -1.3236705 ] Sparsity at: 0.036061561158798286 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4736e-09 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37304652 0.50119287 -1.3240117 ] Sparsity at: 0.036061561158798286 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5153e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37302724 0.5009105 -1.3243608 ] Sparsity at: 0.036061561158798286 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5233e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37298167 0.50063175 -1.3247061 ] Sparsity at: 0.036061561158798286 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4994e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37296593 0.5003334 -1.3250321 ] Sparsity at: 0.036061561158798286 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.4925995599953019 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.6619620323722089 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 1.592738571268356 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 2.4577e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37293306 0.50002563 -1.3253579 ] Sparsity at: 0.036061561158798286 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 2.5014e-09 - accuracy: 1.0000 - val_loss: 0.3258 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37291762 0.49973774 -1.325719 ] Sparsity at: 0.036061561158798286 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37286827 0.49941763 -1.3260385 ] Sparsity at: 0.036061561158798286 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4915e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.372805 0.49911255 -1.3263956 ] Sparsity at: 0.036061561158798286 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4259e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37277442 0.49881005 -1.3267217 ] Sparsity at: 0.036061561158798286 Epoch 356/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4597e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37272745 0.49850523 -1.3270619 ] Sparsity at: 0.036061561158798286 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5411e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37269673 0.4982139 -1.3274156 ] Sparsity at: 0.036061561158798286 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37266386 0.49790472 -1.3277262 ] Sparsity at: 0.036061561158798286 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4299e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37262422 0.4975905 -1.3280598 ] Sparsity at: 0.036061561158798286 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4378e-09 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37258506 0.49726906 -1.3283741 ] Sparsity at: 0.036061561158798286 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37255433 0.49696404 -1.3287165 ] Sparsity at: 0.036061561158798286 Epoch 362/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5133e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37253544 0.4966704 -1.3290659 ] Sparsity at: 0.036061561158798286 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37249577 0.49634728 -1.3293978 ] Sparsity at: 0.036061561158798286 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4557e-09 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3724566 0.49606547 -1.3297406 ] Sparsity at: 0.036061561158798286 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4438e-09 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37240618 0.49576166 -1.3300943 ] Sparsity at: 0.036061561158798286 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4478e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37235138 0.49544197 -1.3304348 ] Sparsity at: 0.036061561158798286 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37230977 0.49512142 -1.3307742 ] Sparsity at: 0.036061561158798286 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37225637 0.4948127 -1.331085 ] Sparsity at: 0.036061561158798286 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4656e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37224564 0.49451214 -1.3314052 ] Sparsity at: 0.036061561158798286 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4239e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37220725 0.49418625 -1.3317423 ] Sparsity at: 0.036061561158798286 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37214747 0.49387428 -1.3320663 ] Sparsity at: 0.036061561158798286 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4259e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37212116 0.49353817 -1.3324158 ] Sparsity at: 0.036061561158798286 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3720977 0.49320933 -1.3327707 ] Sparsity at: 0.036061561158798286 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37207803 0.4929086 -1.3331056 ] Sparsity at: 0.036061561158798286 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37203288 0.49258214 -1.3334275 ] Sparsity at: 0.036061561158798286 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3862e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.3719744 0.49223903 -1.3337499 ] Sparsity at: 0.036061561158798286 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37194026 0.49191517 -1.3341159 ] Sparsity at: 0.036061561158798286 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4219e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37190172 0.49160987 -1.3344594 ] Sparsity at: 0.036061561158798286 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37185317 0.49130413 -1.3348198 ] Sparsity at: 0.036061561158798286 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37179795 0.4909519 -1.3351437 ] Sparsity at: 0.036061561158798286 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37175176 0.49062988 -1.3354775 ] Sparsity at: 0.036061561158798286 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4060e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3716944 0.49028948 -1.3358223 ] Sparsity at: 0.036061561158798286 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4239e-09 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.37167186 0.48997888 -1.3361493 ] Sparsity at: 0.036061561158798286 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37163135 0.48963788 -1.3364975 ] Sparsity at: 0.036061561158798286 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4199e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37158456 0.48931134 -1.3368276 ] Sparsity at: 0.036061561158798286 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3715391 0.4889384 -1.3371792 ] Sparsity at: 0.036061561158798286 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.3714875 0.48861992 -1.3375167 ] Sparsity at: 0.036061561158798286 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37146088 0.488283 -1.3378537 ] Sparsity at: 0.036061561158798286 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37140742 0.48795485 -1.3381863 ] Sparsity at: 0.036061561158798286 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4498e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37136656 0.48763773 -1.3385106 ] Sparsity at: 0.036061561158798286 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37130266 0.48729149 -1.3388795 ] Sparsity at: 0.036061561158798286 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37123728 0.48697668 -1.3392172 ] Sparsity at: 0.036061561158798286 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4199e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37120995 0.48662707 -1.3395646 ] Sparsity at: 0.036061561158798286 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3711828 0.4863032 -1.3399073 ] Sparsity at: 0.036061561158798286 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37115878 0.4859548 -1.3402303 ] Sparsity at: 0.036061561158798286 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37108728 0.4856252 -1.3405764 ] Sparsity at: 0.036061561158798286 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4339e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37105125 0.4852889 -1.3409209 ] Sparsity at: 0.036061561158798286 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37097406 0.48496714 -1.3412572 ] Sparsity at: 0.036061561158798286 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3709361 0.48463473 -1.3416064 ] Sparsity at: 0.036061561158798286 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37088767 0.48430985 -1.341956 ] Sparsity at: 0.036061561158798286 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.5341157585249476 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6928729482168308 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.24682617 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 1.679269350849495 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10078125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 1. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 44s 7ms/step - loss: 2.3822e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37083155 0.48395914 -1.3422847 ] Sparsity at: 0.036061561158798286 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 2.3623e-09 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37077317 0.48360687 -1.3426182 ] Sparsity at: 0.036061561158798286 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4060e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37071675 0.4832838 -1.3429725 ] Sparsity at: 0.036061561158798286 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3706646 0.482978 -1.3433253 ] Sparsity at: 0.036061561158798286 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3782e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.3705867 0.48265088 -1.3436669 ] Sparsity at: 0.036061561158798286 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9732 [-0.05940218 -0.00601314 -0.04628057 ... 0.37056762 0.4822972 -1.3440218 ] Sparsity at: 0.036061561158798286 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37050194 0.48196664 -1.3443604 ] Sparsity at: 0.036061561158798286 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37046096 0.4816298 -1.3446752 ] Sparsity at: 0.036061561158798286 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3704135 0.48127213 -1.3450248 ] Sparsity at: 0.036061561158798286 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37035987 0.48093697 -1.3453923 ] Sparsity at: 0.036061561158798286 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.37032816 0.48057675 -1.3457308 ] Sparsity at: 0.036061561158798286 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4339e-09 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37028325 0.48025593 -1.3460753 ] Sparsity at: 0.036061561158798286 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37021753 0.47994345 -1.3464363 ] Sparsity at: 0.036061561158798286 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3701749 0.47960788 -1.3467789 ] Sparsity at: 0.036061561158798286 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.37013832 0.47925252 -1.3471206 ] Sparsity at: 0.036061561158798286 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3286e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.37009537 0.47892454 -1.3474555 ] Sparsity at: 0.036061561158798286 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3862e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3700527 0.4785859 -1.3478229 ] Sparsity at: 0.036061561158798286 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36999473 0.478222 -1.3481259 ] Sparsity at: 0.036061561158798286 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3699416 0.47786346 -1.3484771 ] Sparsity at: 0.036061561158798286 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3698776 0.47752187 -1.3488457 ] Sparsity at: 0.036061561158798286 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36983067 0.47714835 -1.3491694 ] Sparsity at: 0.036061561158798286 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3697891 0.4767999 -1.3495294 ] Sparsity at: 0.036061561158798286 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3623e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36975327 0.47645083 -1.3498878 ] Sparsity at: 0.036061561158798286 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3696886 0.4761272 -1.3502464 ] Sparsity at: 0.036061561158798286 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3544e-09 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3696453 0.47577834 -1.3505902 ] Sparsity at: 0.036061561158798286 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36959687 0.4754619 -1.3509238 ] Sparsity at: 0.036061561158798286 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36953333 0.47511104 -1.3512607 ] Sparsity at: 0.036061561158798286 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36947006 0.47477496 -1.3516172 ] Sparsity at: 0.036061561158798286 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3694455 0.4744407 -1.3519588 ] Sparsity at: 0.036061561158798286 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3693838 0.47409728 -1.352285 ] Sparsity at: 0.036061561158798286 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3325e-09 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36933798 0.47371745 -1.3526344 ] Sparsity at: 0.036061561158798286 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36925972 0.47338074 -1.3529731 ] Sparsity at: 0.036061561158798286 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36919358 0.47304606 -1.353327 ] Sparsity at: 0.036061561158798286 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3544e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36913842 0.47269753 -1.3536979 ] Sparsity at: 0.036061561158798286 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3305e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36907166 0.4723477 -1.354055 ] Sparsity at: 0.036061561158798286 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3743e-09 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36902648 0.47201052 -1.3543962 ] Sparsity at: 0.036061561158798286 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36894777 0.4716593 -1.35473 ] Sparsity at: 0.036061561158798286 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3688989 0.47129542 -1.3550823 ] Sparsity at: 0.036061561158798286 Epoch 439/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36883956 0.47092095 -1.355445 ] Sparsity at: 0.036061561158798286 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4100e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36880812 0.47058257 -1.3557858 ] Sparsity at: 0.036061561158798286 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3687541 0.47022122 -1.3561058 ] Sparsity at: 0.036061561158798286 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36870337 0.46985197 -1.356464 ] Sparsity at: 0.036061561158798286 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3743e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36864144 0.46947452 -1.356834 ] Sparsity at: 0.036061561158798286 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36857134 0.46912 -1.357185 ] Sparsity at: 0.036061561158798286 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3685082 0.46874776 -1.3575491 ] Sparsity at: 0.036061561158798286 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36844727 0.46838197 -1.3579353 ] Sparsity at: 0.036061561158798286 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36836934 0.4679863 -1.3582811 ] Sparsity at: 0.036061561158798286 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4001e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3683137 0.4676689 -1.3586267 ] Sparsity at: 0.036061561158798286 Epoch 449/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36823508 0.4672976 -1.3589816 ] Sparsity at: 0.036061561158798286 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3681587 0.46693128 -1.3593597 ] Sparsity at: 0.036061561158798286 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36812142 0.4665764 -1.3596872 ] Sparsity at: 0.036061561158798286 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3365e-09 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3680569 0.46619973 -1.3600336 ] Sparsity at: 0.036061561158798286 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4021e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36803848 0.46586603 -1.3603746 ] Sparsity at: 0.036061561158798286 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3679608 0.4655147 -1.3607421 ] Sparsity at: 0.036061561158798286 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36789814 0.46516043 -1.3610622 ] Sparsity at: 0.036061561158798286 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36784092 0.4647933 -1.3614473 ] Sparsity at: 0.036061561158798286 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3677795 0.4644324 -1.3618213 ] Sparsity at: 0.036061561158798286 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3584e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36770603 0.4640646 -1.3621607 ] Sparsity at: 0.036061561158798286 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36762768 0.4636927 -1.3625153 ] Sparsity at: 0.036061561158798286 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4180e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3675584 0.4633437 -1.3628732 ] Sparsity at: 0.036061561158798286 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3643e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3675012 0.4629514 -1.363202 ] Sparsity at: 0.036061561158798286 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36747074 0.46261394 -1.3635626 ] Sparsity at: 0.036061561158798286 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3127e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3673833 0.4622463 -1.3639078 ] Sparsity at: 0.036061561158798286 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36730486 0.46190125 -1.3642585 ] Sparsity at: 0.036061561158798286 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36722836 0.46153262 -1.364602 ] Sparsity at: 0.036061561158798286 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36714482 0.46113583 -1.3649713 ] Sparsity at: 0.036061561158798286 Epoch 467/500 235/235 [==============================] - 2s 10ms/step - loss: 2.3325e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.36706755 0.46076614 -1.3653077 ] Sparsity at: 0.036061561158798286 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4140e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9728 [-0.05940218 -0.00601314 -0.04628057 ... 0.3669996 0.4604226 -1.3656886 ] Sparsity at: 0.036061561158798286 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.36693448 0.46006322 -1.3660595 ] Sparsity at: 0.036061561158798286 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3683e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36686295 0.45970774 -1.3664263 ] Sparsity at: 0.036061561158798286 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3667913 0.4593291 -1.3667823 ] Sparsity at: 0.036061561158798286 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4001e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3667364 0.45897734 -1.3671376 ] Sparsity at: 0.036061561158798286 Epoch 473/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.366681 0.45860302 -1.3675181 ] Sparsity at: 0.036061561158798286 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3666014 0.45821998 -1.3678557 ] Sparsity at: 0.036061561158798286 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36653644 0.45786613 -1.3682197 ] Sparsity at: 0.036061561158798286 Epoch 476/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36644208 0.45747906 -1.3685647 ] Sparsity at: 0.036061561158798286 Epoch 477/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36638945 0.4571207 -1.3689424 ] Sparsity at: 0.036061561158798286 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.366341 0.45675144 -1.3692974 ] Sparsity at: 0.036061561158798286 Epoch 479/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36623833 0.45635846 -1.369663 ] Sparsity at: 0.036061561158798286 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3603e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36614075 0.45598853 -1.3700464 ] Sparsity at: 0.036061561158798286 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36609447 0.4556002 -1.370387 ] Sparsity at: 0.036061561158798286 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3405e-09 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36599296 0.45522544 -1.3707418 ] Sparsity at: 0.036061561158798286 Epoch 483/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3842e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3659192 0.4548735 -1.3711203 ] Sparsity at: 0.036061561158798286 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9729 [-0.05940218 -0.00601314 -0.04628057 ... 0.3658312 0.45448342 -1.3715 ] Sparsity at: 0.036061561158798286 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3657436 0.45409387 -1.3718641 ] Sparsity at: 0.036061561158798286 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3656828 0.4537486 -1.3722153 ] Sparsity at: 0.036061561158798286 Epoch 487/500 235/235 [==============================] - 2s 10ms/step - loss: 2.3544e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36558267 0.45338312 -1.372578 ] Sparsity at: 0.036061561158798286 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4041e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36550596 0.45301488 -1.3729552 ] Sparsity at: 0.036061561158798286 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3882e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.3654373 0.45263305 -1.3733386 ] Sparsity at: 0.036061561158798286 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36535847 0.45225954 -1.3736976 ] Sparsity at: 0.036061561158798286 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3802e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36529544 0.4518772 -1.3740878 ] Sparsity at: 0.036061561158798286 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36520702 0.45152915 -1.3744223 ] Sparsity at: 0.036061561158798286 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3584e-09 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36515135 0.45115298 -1.3747709 ] Sparsity at: 0.036061561158798286 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3623e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36506167 0.4507676 -1.375137 ] Sparsity at: 0.036061561158798286 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.3649902 0.4503905 -1.37551 ] Sparsity at: 0.036061561158798286 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.3649184 0.45002145 -1.3758777 ] Sparsity at: 0.036061561158798286 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4140e-09 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36485904 0.44965377 -1.376249 ] Sparsity at: 0.036061561158798286 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3524e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36477286 0.4492806 -1.3766314 ] Sparsity at: 0.036061561158798286 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9731 [-0.05940218 -0.00601314 -0.04628057 ... 0.36469167 0.44891402 -1.37698 ] Sparsity at: 0.036061561158798286 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9730 [-0.05940218 -0.00601314 -0.04628057 ... 0.36462858 0.44854546 -1.3773383 ] Sparsity at: 0.036061561158798286 Epoch 1/500 235/235 [==============================] - 6s 15ms/step - loss: 0.1403 - accuracy: 0.9783 - val_loss: 0.1873 - val_accuracy: 0.9651 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1387 - accuracy: 0.9782 - val_loss: 0.1958 - val_accuracy: 0.9632 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1340 - accuracy: 0.9802 - val_loss: 0.2116 - val_accuracy: 0.9588 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1397 - accuracy: 0.9782 - val_loss: 0.1913 - val_accuracy: 0.9629 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9798 - val_loss: 0.2005 - val_accuracy: 0.9613 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9800 - val_loss: 0.2257 - val_accuracy: 0.9540 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2263 - val_accuracy: 0.9546 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1433 - accuracy: 0.9778 - val_loss: 0.1851 - val_accuracy: 0.9669 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1388 - accuracy: 0.9790 - val_loss: 0.1904 - val_accuracy: 0.9627 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9793 - val_loss: 0.1901 - val_accuracy: 0.9653 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2118 - val_accuracy: 0.9588 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9786 - val_loss: 0.2235 - val_accuracy: 0.9557 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.2041 - val_accuracy: 0.9611 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9794 - val_loss: 0.1801 - val_accuracy: 0.9684 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9792 - val_loss: 0.2342 - val_accuracy: 0.9528 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.2231 - val_accuracy: 0.9547 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9788 - val_loss: 0.1919 - val_accuracy: 0.9640 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1391 - accuracy: 0.9788 - val_loss: 0.1892 - val_accuracy: 0.9644 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9804 - val_loss: 0.1988 - val_accuracy: 0.9632 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9784 - val_loss: 0.2229 - val_accuracy: 0.9561 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9792 - val_loss: 0.2131 - val_accuracy: 0.9582 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1404 - accuracy: 0.9787 - val_loss: 0.2037 - val_accuracy: 0.9623 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9787 - val_loss: 0.2463 - val_accuracy: 0.9506 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1395 - accuracy: 0.9786 - val_loss: 0.1975 - val_accuracy: 0.9637 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2048 - val_accuracy: 0.9604 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9794 - val_loss: 0.2202 - val_accuracy: 0.9559 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9806 - val_loss: 0.1845 - val_accuracy: 0.9670 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.2082 - val_accuracy: 0.9607 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9797 - val_loss: 0.2040 - val_accuracy: 0.9617 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9798 - val_loss: 0.2005 - val_accuracy: 0.9636 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.1859 - val_accuracy: 0.9658 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9792 - val_loss: 0.1854 - val_accuracy: 0.9668 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9795 - val_loss: 0.1904 - val_accuracy: 0.9656 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9789 - val_loss: 0.1952 - val_accuracy: 0.9648 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9785 - val_loss: 0.2161 - val_accuracy: 0.9568 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9792 - val_loss: 0.2101 - val_accuracy: 0.9602 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9803 - val_loss: 0.2150 - val_accuracy: 0.9581 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9788 - val_loss: 0.2102 - val_accuracy: 0.9590 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9788 - val_loss: 0.2399 - val_accuracy: 0.9498 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1391 - accuracy: 0.9787 - val_loss: 0.2240 - val_accuracy: 0.9549 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1388 - accuracy: 0.9789 - val_loss: 0.1893 - val_accuracy: 0.9652 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9798 - val_loss: 0.1970 - val_accuracy: 0.9608 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9783 - val_loss: 0.1779 - val_accuracy: 0.9677 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9787 - val_loss: 0.1842 - val_accuracy: 0.9661 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1373 - accuracy: 0.9794 - val_loss: 0.2602 - val_accuracy: 0.9450 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1359 - accuracy: 0.9797 - val_loss: 0.2042 - val_accuracy: 0.9587 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1359 - accuracy: 0.9797 - val_loss: 0.2571 - val_accuracy: 0.9477 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1414 - accuracy: 0.9779 - val_loss: 0.1932 - val_accuracy: 0.9655 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1369 - accuracy: 0.9793 - val_loss: 0.1956 - val_accuracy: 0.9640 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2351 - val_accuracy: 0.9528 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.2418 - val_accuracy: 0.9494 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9789 - val_loss: 0.1742 - val_accuracy: 0.9685 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9794 - val_loss: 0.1958 - val_accuracy: 0.9611 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9794 - val_loss: 0.1890 - val_accuracy: 0.9638 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9788 - val_loss: 0.2076 - val_accuracy: 0.9612 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1370 - accuracy: 0.9789 - val_loss: 0.2106 - val_accuracy: 0.9582 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1370 - accuracy: 0.9793 - val_loss: 0.2237 - val_accuracy: 0.9542 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9792 - val_loss: 0.2138 - val_accuracy: 0.9601 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.1834 - val_accuracy: 0.9672 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9795 - val_loss: 0.2028 - val_accuracy: 0.9635 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1387 - accuracy: 0.9790 - val_loss: 0.1870 - val_accuracy: 0.9638 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1330 - accuracy: 0.9808 - val_loss: 0.2053 - val_accuracy: 0.9625 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9784 - val_loss: 0.1949 - val_accuracy: 0.9650 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9790 - val_loss: 0.2478 - val_accuracy: 0.9468 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.1917 - val_accuracy: 0.9650 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9801 - val_loss: 0.2344 - val_accuracy: 0.9495 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9785 - val_loss: 0.2420 - val_accuracy: 0.9488 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9790 - val_loss: 0.2098 - val_accuracy: 0.9588 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9783 - val_loss: 0.1903 - val_accuracy: 0.9645 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9802 - val_loss: 0.2147 - val_accuracy: 0.9569 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9798 - val_loss: 0.2125 - val_accuracy: 0.9576 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1344 - accuracy: 0.9800 - val_loss: 0.2387 - val_accuracy: 0.9523 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9795 - val_loss: 0.2173 - val_accuracy: 0.9564 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1355 - accuracy: 0.9798 - val_loss: 0.2255 - val_accuracy: 0.9541 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9796 - val_loss: 0.1874 - val_accuracy: 0.9663 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9792 - val_loss: 0.2018 - val_accuracy: 0.9617 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1323 - accuracy: 0.9807 - val_loss: 0.2060 - val_accuracy: 0.9592 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1368 - accuracy: 0.9786 - val_loss: 0.3392 - val_accuracy: 0.9240 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1355 - accuracy: 0.9801 - val_loss: 0.2371 - val_accuracy: 0.9523 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.1959 - val_accuracy: 0.9631 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1397 - accuracy: 0.9775 - val_loss: 0.2244 - val_accuracy: 0.9542 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1390 - accuracy: 0.9794 - val_loss: 0.2300 - val_accuracy: 0.9530 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.2263 - val_accuracy: 0.9550 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1355 - accuracy: 0.9795 - val_loss: 0.2218 - val_accuracy: 0.9546 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.1891 - val_accuracy: 0.9631 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1369 - accuracy: 0.9792 - val_loss: 0.2006 - val_accuracy: 0.9615 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1344 - accuracy: 0.9799 - val_loss: 0.1894 - val_accuracy: 0.9646 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9783 - val_loss: 0.2056 - val_accuracy: 0.9627 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9805 - val_loss: 0.2141 - val_accuracy: 0.9568 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9794 - val_loss: 0.2044 - val_accuracy: 0.9606 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1334 - accuracy: 0.9799 - val_loss: 0.2450 - val_accuracy: 0.9490 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1335 - accuracy: 0.9801 - val_loss: 0.2308 - val_accuracy: 0.9518 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9782 - val_loss: 0.2086 - val_accuracy: 0.9570 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9792 - val_loss: 0.2727 - val_accuracy: 0.9393 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2316 - val_accuracy: 0.9526 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9789 - val_loss: 0.2023 - val_accuracy: 0.9596 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2094 - val_accuracy: 0.9584 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9787 - val_loss: 0.2270 - val_accuracy: 0.9507 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1327 - accuracy: 0.9800 - val_loss: 0.2416 - val_accuracy: 0.9507 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9800 - val_loss: 0.2189 - val_accuracy: 0.9557 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9787 - val_loss: 0.2090 - val_accuracy: 0.9605 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1409 - accuracy: 0.9776 - val_loss: 0.2981 - val_accuracy: 0.9343 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1327 - accuracy: 0.9805 - val_loss: 0.2048 - val_accuracy: 0.9596 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1324 - accuracy: 0.9794 - val_loss: 0.2451 - val_accuracy: 0.9484 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1395 - accuracy: 0.9783 - val_loss: 0.2074 - val_accuracy: 0.9617 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9793 - val_loss: 0.2639 - val_accuracy: 0.9418 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1376 - accuracy: 0.9789 - val_loss: 0.2201 - val_accuracy: 0.9558 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1374 - accuracy: 0.9796 - val_loss: 0.1994 - val_accuracy: 0.9630 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9788 - val_loss: 0.1765 - val_accuracy: 0.9687 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9790 - val_loss: 0.1824 - val_accuracy: 0.9667 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9782 - val_loss: 0.1925 - val_accuracy: 0.9637 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9799 - val_loss: 0.2151 - val_accuracy: 0.9560 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1390 - accuracy: 0.9787 - val_loss: 0.1811 - val_accuracy: 0.9674 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1360 - accuracy: 0.9791 - val_loss: 0.1892 - val_accuracy: 0.9645 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9786 - val_loss: 0.1807 - val_accuracy: 0.9661 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1330 - accuracy: 0.9796 - val_loss: 0.1965 - val_accuracy: 0.9627 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9784 - val_loss: 0.2119 - val_accuracy: 0.9604 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9803 - val_loss: 0.2320 - val_accuracy: 0.9525 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.1870 - val_accuracy: 0.9634 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9787 - val_loss: 0.2263 - val_accuracy: 0.9576 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9794 - val_loss: 0.2407 - val_accuracy: 0.9505 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9794 - val_loss: 0.1989 - val_accuracy: 0.9644 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9783 - val_loss: 0.2073 - val_accuracy: 0.9589 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1351 - accuracy: 0.9803 - val_loss: 0.2003 - val_accuracy: 0.9598 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9785 - val_loss: 0.2095 - val_accuracy: 0.9592 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1414 - accuracy: 0.9776 - val_loss: 0.1903 - val_accuracy: 0.9649 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9790 - val_loss: 0.2043 - val_accuracy: 0.9622 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2226 - val_accuracy: 0.9561 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9778 - val_loss: 0.1923 - val_accuracy: 0.9643 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9792 - val_loss: 0.2100 - val_accuracy: 0.9582 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9792 - val_loss: 0.2170 - val_accuracy: 0.9582 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1392 - accuracy: 0.9784 - val_loss: 0.2202 - val_accuracy: 0.9564 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9790 - val_loss: 0.2047 - val_accuracy: 0.9589 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9790 - val_loss: 0.2026 - val_accuracy: 0.9614 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9789 - val_loss: 0.2044 - val_accuracy: 0.9624 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1357 - accuracy: 0.9789 - val_loss: 0.2143 - val_accuracy: 0.9568 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1373 - accuracy: 0.9783 - val_loss: 0.2096 - val_accuracy: 0.9576 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1348 - accuracy: 0.9791 - val_loss: 0.2134 - val_accuracy: 0.9583 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9785 - val_loss: 0.1945 - val_accuracy: 0.9649 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.1949 - val_accuracy: 0.9647 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1321 - accuracy: 0.9798 - val_loss: 0.1957 - val_accuracy: 0.9613 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9783 - val_loss: 0.2115 - val_accuracy: 0.9591 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1322 - accuracy: 0.9804 - val_loss: 0.2010 - val_accuracy: 0.9607 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9789 - val_loss: 0.1984 - val_accuracy: 0.9661 [0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9782 - val_loss: 0.2356 - val_accuracy: 0.9523 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1366 - accuracy: 0.9793 - val_loss: 0.1937 - val_accuracy: 0.9638 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1366 - accuracy: 0.9790 - val_loss: 0.2022 - val_accuracy: 0.9633 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1322 - accuracy: 0.9805 - val_loss: 0.2189 - val_accuracy: 0.9540 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9790 - val_loss: 0.2127 - val_accuracy: 0.9567 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9781 - val_loss: 0.2017 - val_accuracy: 0.9585 [ 0.000000e+00 0.000000e+00 4.737296e-34 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9779 - val_loss: 0.2069 - val_accuracy: 0.9584 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9784 - val_loss: 0.2122 - val_accuracy: 0.9573 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1373 - accuracy: 0.9785 - val_loss: 0.1985 - val_accuracy: 0.9652 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9790 - val_loss: 0.2066 - val_accuracy: 0.9603 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9783 - val_loss: 0.2213 - val_accuracy: 0.9548 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1380 - accuracy: 0.9788 - val_loss: 0.1882 - val_accuracy: 0.9662 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9783 - val_loss: 0.1963 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9785 - val_loss: 0.2364 - val_accuracy: 0.9493 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9799 - val_loss: 0.1858 - val_accuracy: 0.9647 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2111 - val_accuracy: 0.9564 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1399 - accuracy: 0.9786 - val_loss: 0.2703 - val_accuracy: 0.9396 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9792 - val_loss: 0.2278 - val_accuracy: 0.9511 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9799 - val_loss: 0.2560 - val_accuracy: 0.9455 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1389 - accuracy: 0.9785 - val_loss: 0.1926 - val_accuracy: 0.9633 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9794 - val_loss: 0.2433 - val_accuracy: 0.9491 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9791 - val_loss: 0.1915 - val_accuracy: 0.9641 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1341 - accuracy: 0.9797 - val_loss: 0.2287 - val_accuracy: 0.9523 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9793 - val_loss: 0.2659 - val_accuracy: 0.9428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1353 - accuracy: 0.9796 - val_loss: 0.2035 - val_accuracy: 0.9596 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2084 - val_accuracy: 0.9598 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9790 - val_loss: 0.2164 - val_accuracy: 0.9555 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9789 - val_loss: 0.2448 - val_accuracy: 0.9463 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1361 - accuracy: 0.9790 - val_loss: 0.2010 - val_accuracy: 0.9617 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1385 - accuracy: 0.9783 - val_loss: 0.1971 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2238 - val_accuracy: 0.9536 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9781 - val_loss: 0.2101 - val_accuracy: 0.9587 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1337 - accuracy: 0.9803 - val_loss: 0.2134 - val_accuracy: 0.9564 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1925 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9796 - val_loss: 0.1971 - val_accuracy: 0.9627 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2013 - val_accuracy: 0.9621 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9774 - val_loss: 0.1991 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9781 - val_loss: 0.2090 - val_accuracy: 0.9597 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9789 - val_loss: 0.2100 - val_accuracy: 0.9600 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1359 - accuracy: 0.9791 - val_loss: 0.2031 - val_accuracy: 0.9601 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9800 - val_loss: 0.2124 - val_accuracy: 0.9590 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9791 - val_loss: 0.2415 - val_accuracy: 0.9450 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.1889 - val_accuracy: 0.9651 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9796 - val_loss: 0.2252 - val_accuracy: 0.9554 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9791 - val_loss: 0.2120 - val_accuracy: 0.9566 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9790 - val_loss: 0.1998 - val_accuracy: 0.9606 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1334 - accuracy: 0.9797 - val_loss: 0.1824 - val_accuracy: 0.9658 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9790 - val_loss: 0.2202 - val_accuracy: 0.9551 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9783 - val_loss: 0.1957 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9794 - val_loss: 0.2089 - val_accuracy: 0.9586 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9790 - val_loss: 0.2311 - val_accuracy: 0.9538 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9807 - val_loss: 0.2020 - val_accuracy: 0.9610 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2173 - val_accuracy: 0.9545 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1357 - accuracy: 0.9791 - val_loss: 0.2127 - val_accuracy: 0.9560 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1333 - accuracy: 0.9796 - val_loss: 0.1967 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1332 - accuracy: 0.9793 - val_loss: 0.1962 - val_accuracy: 0.9633 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1415 - accuracy: 0.9773 - val_loss: 0.1961 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9783 - val_loss: 0.1838 - val_accuracy: 0.9659 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1400 - accuracy: 0.9776 - val_loss: 0.2199 - val_accuracy: 0.9568 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1407 - accuracy: 0.9783 - val_loss: 0.2031 - val_accuracy: 0.9595 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9784 - val_loss: 0.2360 - val_accuracy: 0.9506 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9779 - val_loss: 0.1816 - val_accuracy: 0.9672 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9786 - val_loss: 0.2046 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9786 - val_loss: 0.1926 - val_accuracy: 0.9622 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.1992 - val_accuracy: 0.9639 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9781 - val_loss: 0.1974 - val_accuracy: 0.9632 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9780 - val_loss: 0.2283 - val_accuracy: 0.9553 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1419 - accuracy: 0.9774 - val_loss: 0.2697 - val_accuracy: 0.9422 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1430 - accuracy: 0.9775 - val_loss: 0.2648 - val_accuracy: 0.9427 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1391 - accuracy: 0.9779 - val_loss: 0.2051 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9781 - val_loss: 0.2300 - val_accuracy: 0.9549 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9777 - val_loss: 0.2268 - val_accuracy: 0.9543 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9782 - val_loss: 0.1947 - val_accuracy: 0.9632 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1392 - accuracy: 0.9778 - val_loss: 0.2059 - val_accuracy: 0.9593 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.2035 - val_accuracy: 0.9593 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9783 - val_loss: 0.1939 - val_accuracy: 0.9620 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9785 - val_loss: 0.2149 - val_accuracy: 0.9564 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2192 - val_accuracy: 0.9567 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9780 - val_loss: 0.2352 - val_accuracy: 0.9530 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9789 - val_loss: 0.2006 - val_accuracy: 0.9617 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9778 - val_loss: 0.1888 - val_accuracy: 0.9629 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1374 - accuracy: 0.9783 - val_loss: 0.2058 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9781 - val_loss: 0.2516 - val_accuracy: 0.9470 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.2043 - val_accuracy: 0.9585 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.2100 - val_accuracy: 0.9576 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9780 - val_loss: 0.2087 - val_accuracy: 0.9588 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1397 - accuracy: 0.9779 - val_loss: 0.2016 - val_accuracy: 0.9604 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.2500 - val_accuracy: 0.9461 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9782 - val_loss: 0.1963 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9781 - val_loss: 0.2393 - val_accuracy: 0.9513 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1376 - accuracy: 0.9777 - val_loss: 0.1952 - val_accuracy: 0.9630 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2104 - val_accuracy: 0.9592 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.2008 - val_accuracy: 0.9621 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9786 - val_loss: 0.2021 - val_accuracy: 0.9610 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9792 - val_loss: 0.2233 - val_accuracy: 0.9563 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.1952 - val_accuracy: 0.9632 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.3014 - val_accuracy: 0.9312 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1394 - accuracy: 0.9775 - val_loss: 0.2264 - val_accuracy: 0.9525 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9787 - val_loss: 0.1988 - val_accuracy: 0.9605 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9780 - val_loss: 0.2520 - val_accuracy: 0.9437 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1351 - accuracy: 0.9797 - val_loss: 0.2343 - val_accuracy: 0.9508 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9776 - val_loss: 0.2053 - val_accuracy: 0.9591 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9778 - val_loss: 0.2262 - val_accuracy: 0.9521 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9779 - val_loss: 0.2048 - val_accuracy: 0.9606 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2143 - val_accuracy: 0.9568 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1360 - accuracy: 0.9788 - val_loss: 0.2295 - val_accuracy: 0.9545 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1387 - accuracy: 0.9772 - val_loss: 0.1938 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1286 - accuracy: 0.9789 - val_loss: 0.1954 - val_accuracy: 0.9597 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1298 - accuracy: 0.9789 - val_loss: 0.1946 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1250 - accuracy: 0.9794 - val_loss: 0.1806 - val_accuracy: 0.9641 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1278 - accuracy: 0.9781 - val_loss: 0.1864 - val_accuracy: 0.9645 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1237 - accuracy: 0.9800 - val_loss: 0.2007 - val_accuracy: 0.9589 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1241 - accuracy: 0.9789 - val_loss: 0.1949 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1213 - accuracy: 0.9805 - val_loss: 0.1846 - val_accuracy: 0.9647 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1243 - accuracy: 0.9794 - val_loss: 0.2035 - val_accuracy: 0.9602 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1228 - accuracy: 0.9799 - val_loss: 0.1898 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9803 - val_loss: 0.1767 - val_accuracy: 0.9661 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9807 - val_loss: 0.1887 - val_accuracy: 0.9622 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1199 - accuracy: 0.9803 - val_loss: 0.1784 - val_accuracy: 0.9652 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1213 - accuracy: 0.9800 - val_loss: 0.1941 - val_accuracy: 0.9599 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1199 - accuracy: 0.9802 - val_loss: 0.1819 - val_accuracy: 0.9654 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9800 - val_loss: 0.1772 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1221 - accuracy: 0.9797 - val_loss: 0.1939 - val_accuracy: 0.9577 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1191 - accuracy: 0.9805 - val_loss: 0.1704 - val_accuracy: 0.9694 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9799 - val_loss: 0.1813 - val_accuracy: 0.9667 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9801 - val_loss: 0.1777 - val_accuracy: 0.9667 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1174 - accuracy: 0.9815 - val_loss: 0.1890 - val_accuracy: 0.9635 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1188 - accuracy: 0.9804 - val_loss: 0.1913 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1198 - accuracy: 0.9810 - val_loss: 0.1957 - val_accuracy: 0.9597 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9801 - val_loss: 0.1800 - val_accuracy: 0.9660 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9653 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1216 - accuracy: 0.9799 - val_loss: 0.1778 - val_accuracy: 0.9651 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9804 - val_loss: 0.1834 - val_accuracy: 0.9630 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1181 - accuracy: 0.9807 - val_loss: 0.1871 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1196 - accuracy: 0.9804 - val_loss: 0.1760 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1166 - accuracy: 0.9809 - val_loss: 0.1859 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1196 - accuracy: 0.9801 - val_loss: 0.2102 - val_accuracy: 0.9594 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1195 - accuracy: 0.9808 - val_loss: 0.1661 - val_accuracy: 0.9677 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1179 - accuracy: 0.9812 - val_loss: 0.1874 - val_accuracy: 0.9631 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1185 - accuracy: 0.9803 - val_loss: 0.1859 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1168 - accuracy: 0.9811 - val_loss: 0.1919 - val_accuracy: 0.9605 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1169 - accuracy: 0.9813 - val_loss: 0.1749 - val_accuracy: 0.9661 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1213 - accuracy: 0.9798 - val_loss: 0.1855 - val_accuracy: 0.9638 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1192 - accuracy: 0.9805 - val_loss: 0.1871 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1198 - accuracy: 0.9803 - val_loss: 0.1619 - val_accuracy: 0.9685 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1177 - accuracy: 0.9814 - val_loss: 0.1959 - val_accuracy: 0.9585 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1195 - accuracy: 0.9809 - val_loss: 0.2133 - val_accuracy: 0.9559 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9806 - val_loss: 0.1857 - val_accuracy: 0.9640 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9811 - val_loss: 0.1688 - val_accuracy: 0.9674 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1162 - accuracy: 0.9811 - val_loss: 0.1850 - val_accuracy: 0.9629 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9809 - val_loss: 0.1937 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9807 - val_loss: 0.2106 - val_accuracy: 0.9546 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9811 - val_loss: 0.1862 - val_accuracy: 0.9608 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1182 - accuracy: 0.9806 - val_loss: 0.1692 - val_accuracy: 0.9654 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1203 - accuracy: 0.9799 - val_loss: 0.2061 - val_accuracy: 0.9551 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9816 - val_loss: 0.1710 - val_accuracy: 0.9653 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1266 - accuracy: 0.9768 - val_loss: 0.1696 - val_accuracy: 0.9639 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1147 - accuracy: 0.9797 - val_loss: 0.1664 - val_accuracy: 0.9660 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9807 - val_loss: 0.1784 - val_accuracy: 0.9628 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1104 - accuracy: 0.9809 - val_loss: 0.1762 - val_accuracy: 0.9626 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1098 - accuracy: 0.9809 - val_loss: 0.1721 - val_accuracy: 0.9635 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1060 - accuracy: 0.9829 - val_loss: 0.1674 - val_accuracy: 0.9650 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1060 - accuracy: 0.9814 - val_loss: 0.1659 - val_accuracy: 0.9667 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9816 - val_loss: 0.1746 - val_accuracy: 0.9655 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1047 - accuracy: 0.9826 - val_loss: 0.1615 - val_accuracy: 0.9672 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1054 - accuracy: 0.9815 - val_loss: 0.1696 - val_accuracy: 0.9664 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1042 - accuracy: 0.9822 - val_loss: 0.1612 - val_accuracy: 0.9668 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1036 - accuracy: 0.9822 - val_loss: 0.1777 - val_accuracy: 0.9622 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1037 - accuracy: 0.9825 - val_loss: 0.1758 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1050 - accuracy: 0.9817 - val_loss: 0.1718 - val_accuracy: 0.9656 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1013 - accuracy: 0.9828 - val_loss: 0.1727 - val_accuracy: 0.9643 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1038 - accuracy: 0.9812 - val_loss: 0.1646 - val_accuracy: 0.9671 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9828 - val_loss: 0.1663 - val_accuracy: 0.9681 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1019 - accuracy: 0.9826 - val_loss: 0.1713 - val_accuracy: 0.9644 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1011 - accuracy: 0.9827 - val_loss: 0.1655 - val_accuracy: 0.9656 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9819 - val_loss: 0.1674 - val_accuracy: 0.9658 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1011 - accuracy: 0.9829 - val_loss: 0.1750 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1038 - accuracy: 0.9809 - val_loss: 0.1560 - val_accuracy: 0.9692 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1014 - accuracy: 0.9822 - val_loss: 0.1677 - val_accuracy: 0.9645 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1006 - accuracy: 0.9833 - val_loss: 0.1735 - val_accuracy: 0.9636 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1012 - accuracy: 0.9827 - val_loss: 0.1763 - val_accuracy: 0.9625 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1023 - accuracy: 0.9821 - val_loss: 0.1694 - val_accuracy: 0.9654 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1013 - accuracy: 0.9823 - val_loss: 0.1815 - val_accuracy: 0.9593 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1011 - accuracy: 0.9825 - val_loss: 0.1793 - val_accuracy: 0.9610 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1026 - accuracy: 0.9819 - val_loss: 0.1716 - val_accuracy: 0.9653 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1009 - accuracy: 0.9826 - val_loss: 0.1745 - val_accuracy: 0.9647 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1014 - accuracy: 0.9824 - val_loss: 0.1517 - val_accuracy: 0.9701 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0990 - accuracy: 0.9833 - val_loss: 0.1885 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1021 - accuracy: 0.9821 - val_loss: 0.1612 - val_accuracy: 0.9675 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0998 - accuracy: 0.9831 - val_loss: 0.1705 - val_accuracy: 0.9663 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1016 - accuracy: 0.9821 - val_loss: 0.1571 - val_accuracy: 0.9695 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1001 - accuracy: 0.9825 - val_loss: 0.1773 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0999 - accuracy: 0.9832 - val_loss: 0.1671 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1017 - accuracy: 0.9826 - val_loss: 0.1660 - val_accuracy: 0.9667 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1027 - accuracy: 0.9815 - val_loss: 0.1780 - val_accuracy: 0.9630 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1010 - accuracy: 0.9826 - val_loss: 0.1610 - val_accuracy: 0.9681 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 4s 18ms/step - loss: 0.0990 - accuracy: 0.9830 - val_loss: 0.1669 - val_accuracy: 0.9660 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1020 - accuracy: 0.9822 - val_loss: 0.1636 - val_accuracy: 0.9675 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1054 - accuracy: 0.9810 - val_loss: 0.1852 - val_accuracy: 0.9585 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1062 - accuracy: 0.9806 - val_loss: 0.1856 - val_accuracy: 0.9591 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1047 - accuracy: 0.9812 - val_loss: 0.1844 - val_accuracy: 0.9610 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1016 - accuracy: 0.9825 - val_loss: 0.1889 - val_accuracy: 0.9605 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1015 - accuracy: 0.9822 - val_loss: 0.1848 - val_accuracy: 0.9621 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1023 - accuracy: 0.9821 - val_loss: 0.1809 - val_accuracy: 0.9612 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1044 - accuracy: 0.9816 - val_loss: 0.1749 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1024 - accuracy: 0.9825 - val_loss: 0.1667 - val_accuracy: 0.9659 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1413 - accuracy: 0.9699 - val_loss: 0.1588 - val_accuracy: 0.9686 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9757 - val_loss: 0.1632 - val_accuracy: 0.9677 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1192 - accuracy: 0.9758 - val_loss: 0.1623 - val_accuracy: 0.9661 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1145 - accuracy: 0.9766 - val_loss: 0.1604 - val_accuracy: 0.9661 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1154 - accuracy: 0.9768 - val_loss: 0.1646 - val_accuracy: 0.9667 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1132 - accuracy: 0.9772 - val_loss: 0.1601 - val_accuracy: 0.9669 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9772 - val_loss: 0.1616 - val_accuracy: 0.9660 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9771 - val_loss: 0.1635 - val_accuracy: 0.9664 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1094 - accuracy: 0.9788 - val_loss: 0.1724 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1109 - accuracy: 0.9781 - val_loss: 0.1655 - val_accuracy: 0.9669 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1100 - accuracy: 0.9780 - val_loss: 0.1769 - val_accuracy: 0.9638 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1093 - accuracy: 0.9779 - val_loss: 0.1691 - val_accuracy: 0.9658 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1093 - accuracy: 0.9784 - val_loss: 0.1627 - val_accuracy: 0.9678 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1095 - accuracy: 0.9776 - val_loss: 0.1678 - val_accuracy: 0.9669 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1072 - accuracy: 0.9789 - val_loss: 0.1750 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1098 - accuracy: 0.9781 - val_loss: 0.1654 - val_accuracy: 0.9667 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1067 - accuracy: 0.9789 - val_loss: 0.1692 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1085 - accuracy: 0.9787 - val_loss: 0.1792 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9786 - val_loss: 0.1715 - val_accuracy: 0.9650 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1080 - accuracy: 0.9784 - val_loss: 0.1753 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1081 - accuracy: 0.9784 - val_loss: 0.1566 - val_accuracy: 0.9682 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9785 - val_loss: 0.1784 - val_accuracy: 0.9642 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1056 - accuracy: 0.9798 - val_loss: 0.1736 - val_accuracy: 0.9655 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1057 - accuracy: 0.9790 - val_loss: 0.1643 - val_accuracy: 0.9660 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1061 - accuracy: 0.9790 - val_loss: 0.1571 - val_accuracy: 0.9674 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1060 - accuracy: 0.9790 - val_loss: 0.1732 - val_accuracy: 0.9654 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1075 - accuracy: 0.9781 - val_loss: 0.1768 - val_accuracy: 0.9610 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1066 - accuracy: 0.9783 - val_loss: 0.1733 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1051 - accuracy: 0.9794 - val_loss: 0.1571 - val_accuracy: 0.9688 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1063 - accuracy: 0.9794 - val_loss: 0.1804 - val_accuracy: 0.9614 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1083 - accuracy: 0.9781 - val_loss: 0.1781 - val_accuracy: 0.9633 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1066 - accuracy: 0.9783 - val_loss: 0.1613 - val_accuracy: 0.9671 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1055 - accuracy: 0.9794 - val_loss: 0.1717 - val_accuracy: 0.9646 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1059 - accuracy: 0.9786 - val_loss: 0.1841 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1070 - accuracy: 0.9786 - val_loss: 0.1724 - val_accuracy: 0.9625 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1060 - accuracy: 0.9792 - val_loss: 0.1781 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1061 - accuracy: 0.9786 - val_loss: 0.1626 - val_accuracy: 0.9665 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1076 - accuracy: 0.9783 - val_loss: 0.1742 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1054 - accuracy: 0.9786 - val_loss: 0.1680 - val_accuracy: 0.9655 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9789 - val_loss: 0.1626 - val_accuracy: 0.9666 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1045 - accuracy: 0.9796 - val_loss: 0.1650 - val_accuracy: 0.9654 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1066 - accuracy: 0.9791 - val_loss: 0.1661 - val_accuracy: 0.9674 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1052 - accuracy: 0.9790 - val_loss: 0.1657 - val_accuracy: 0.9669 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1049 - accuracy: 0.9792 - val_loss: 0.1741 - val_accuracy: 0.9636 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1040 - accuracy: 0.9789 - val_loss: 0.1776 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1065 - accuracy: 0.9785 - val_loss: 0.1603 - val_accuracy: 0.9669 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1051 - accuracy: 0.9792 - val_loss: 0.1734 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1041 - accuracy: 0.9795 - val_loss: 0.1835 - val_accuracy: 0.9614 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1055 - accuracy: 0.9790 - val_loss: 0.1853 - val_accuracy: 0.9603 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1062 - accuracy: 0.9786 - val_loss: 0.1591 - val_accuracy: 0.9673 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1449 - accuracy: 0.9711 - val_loss: 0.1867 - val_accuracy: 0.9596 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1256 - accuracy: 0.9753 - val_loss: 0.1668 - val_accuracy: 0.9664 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1217 - accuracy: 0.9756 - val_loss: 0.1673 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1177 - accuracy: 0.9764 - val_loss: 0.1787 - val_accuracy: 0.9621 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1178 - accuracy: 0.9761 - val_loss: 0.1725 - val_accuracy: 0.9653 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1175 - accuracy: 0.9761 - val_loss: 0.1777 - val_accuracy: 0.9620 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1186 - accuracy: 0.9754 - val_loss: 0.1688 - val_accuracy: 0.9648 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1152 - accuracy: 0.9768 - val_loss: 0.1877 - val_accuracy: 0.9606 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1149 - accuracy: 0.9767 - val_loss: 0.1836 - val_accuracy: 0.9603 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1161 - accuracy: 0.9759 - val_loss: 0.1715 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9774 - val_loss: 0.1730 - val_accuracy: 0.9642 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1132 - accuracy: 0.9771 - val_loss: 0.1654 - val_accuracy: 0.9647 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1142 - accuracy: 0.9766 - val_loss: 0.1761 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9766 - val_loss: 0.1780 - val_accuracy: 0.9618 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1136 - accuracy: 0.9768 - val_loss: 0.1697 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1142 - accuracy: 0.9762 - val_loss: 0.1804 - val_accuracy: 0.9627 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9769 - val_loss: 0.1777 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1134 - accuracy: 0.9769 - val_loss: 0.1797 - val_accuracy: 0.9617 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1138 - accuracy: 0.9766 - val_loss: 0.1729 - val_accuracy: 0.9641 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1139 - accuracy: 0.9767 - val_loss: 0.1760 - val_accuracy: 0.9619 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9767 - val_loss: 0.1777 - val_accuracy: 0.9622 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1144 - accuracy: 0.9770 - val_loss: 0.1707 - val_accuracy: 0.9629 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1130 - accuracy: 0.9769 - val_loss: 0.1777 - val_accuracy: 0.9626 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1134 - accuracy: 0.9763 - val_loss: 0.1814 - val_accuracy: 0.9608 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1133 - accuracy: 0.9767 - val_loss: 0.1769 - val_accuracy: 0.9611 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1135 - accuracy: 0.9767 - val_loss: 0.1673 - val_accuracy: 0.9656 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1121 - accuracy: 0.9773 - val_loss: 0.1777 - val_accuracy: 0.9615 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1124 - accuracy: 0.9767 - val_loss: 0.1747 - val_accuracy: 0.9647 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1128 - accuracy: 0.9768 - val_loss: 0.1713 - val_accuracy: 0.9643 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1123 - accuracy: 0.9769 - val_loss: 0.1718 - val_accuracy: 0.9633 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9771 - val_loss: 0.1755 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1121 - accuracy: 0.9773 - val_loss: 0.1694 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1118 - accuracy: 0.9768 - val_loss: 0.1649 - val_accuracy: 0.9644 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1132 - accuracy: 0.9764 - val_loss: 0.1632 - val_accuracy: 0.9657 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9771 - val_loss: 0.1830 - val_accuracy: 0.9605 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1118 - accuracy: 0.9770 - val_loss: 0.1626 - val_accuracy: 0.9649 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1124 - accuracy: 0.9769 - val_loss: 0.1736 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9771 - val_loss: 0.1673 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9765 - val_loss: 0.1716 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1126 - accuracy: 0.9765 - val_loss: 0.1687 - val_accuracy: 0.9639 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1116 - accuracy: 0.9769 - val_loss: 0.1677 - val_accuracy: 0.9643 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9765 - val_loss: 0.1698 - val_accuracy: 0.9640 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1774 - val_accuracy: 0.9620 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1129 - accuracy: 0.9765 - val_loss: 0.1596 - val_accuracy: 0.9663 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1111 - accuracy: 0.9765 - val_loss: 0.1720 - val_accuracy: 0.9633 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1657 - val_accuracy: 0.9636 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9769 - val_loss: 0.1732 - val_accuracy: 0.9634 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1113 - accuracy: 0.9769 - val_loss: 0.1714 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1121 - accuracy: 0.9768 - val_loss: 0.1683 - val_accuracy: 0.9639 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1111 - accuracy: 0.9774 - val_loss: 0.1764 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1121 - accuracy: 0.9765 - val_loss: 0.1809 - val_accuracy: 0.9607 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9771 - val_loss: 0.1701 - val_accuracy: 0.9632 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1118 - accuracy: 0.9771 - val_loss: 0.1769 - val_accuracy: 0.9618 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1124 - accuracy: 0.9767 - val_loss: 0.1696 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9772 - val_loss: 0.1681 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1120 - accuracy: 0.9762 - val_loss: 0.1836 - val_accuracy: 0.9602 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1128 - accuracy: 0.9764 - val_loss: 0.1632 - val_accuracy: 0.9661 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1107 - accuracy: 0.9772 - val_loss: 0.1711 - val_accuracy: 0.9633 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1129 - accuracy: 0.9769 - val_loss: 0.1675 - val_accuracy: 0.9648 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9768 - val_loss: 0.1667 - val_accuracy: 0.9653 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1120 - accuracy: 0.9773 - val_loss: 0.1749 - val_accuracy: 0.9634 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1112 - accuracy: 0.9774 - val_loss: 0.1709 - val_accuracy: 0.9653 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1746 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1114 - accuracy: 0.9768 - val_loss: 0.1807 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1104 - accuracy: 0.9776 - val_loss: 0.1674 - val_accuracy: 0.9651 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1115 - accuracy: 0.9769 - val_loss: 0.1738 - val_accuracy: 0.9645 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9768 - val_loss: 0.1731 - val_accuracy: 0.9622 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1130 - accuracy: 0.9767 - val_loss: 0.1732 - val_accuracy: 0.9621 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1126 - accuracy: 0.9769 - val_loss: 0.1679 - val_accuracy: 0.9653 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1120 - accuracy: 0.9770 - val_loss: 0.1798 - val_accuracy: 0.9608 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9764 - val_loss: 0.1662 - val_accuracy: 0.9662 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1106 - accuracy: 0.9771 - val_loss: 0.1749 - val_accuracy: 0.9618 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1128 - accuracy: 0.9767 - val_loss: 0.1659 - val_accuracy: 0.9646 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1103 - accuracy: 0.9773 - val_loss: 0.1717 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1121 - accuracy: 0.9765 - val_loss: 0.1777 - val_accuracy: 0.9635 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1120 - accuracy: 0.9768 - val_loss: 0.1750 - val_accuracy: 0.9636 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1116 - accuracy: 0.9770 - val_loss: 0.1688 - val_accuracy: 0.9629 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1131 - accuracy: 0.9765 - val_loss: 0.1855 - val_accuracy: 0.9606 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1109 - accuracy: 0.9774 - val_loss: 0.1750 - val_accuracy: 0.9639 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9763 - val_loss: 0.1714 - val_accuracy: 0.9624 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1123 - accuracy: 0.9766 - val_loss: 0.1747 - val_accuracy: 0.9623 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1119 - accuracy: 0.9767 - val_loss: 0.1691 - val_accuracy: 0.9635 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1110 - accuracy: 0.9769 - val_loss: 0.1731 - val_accuracy: 0.9617 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1114 - accuracy: 0.9766 - val_loss: 0.1773 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1128 - accuracy: 0.9763 - val_loss: 0.1707 - val_accuracy: 0.9630 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1116 - accuracy: 0.9771 - val_loss: 0.1775 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1127 - accuracy: 0.9764 - val_loss: 0.1705 - val_accuracy: 0.9639 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1112 - accuracy: 0.9770 - val_loss: 0.1646 - val_accuracy: 0.9645 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1125 - accuracy: 0.9767 - val_loss: 0.1631 - val_accuracy: 0.9645 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1125 - accuracy: 0.9763 - val_loss: 0.1672 - val_accuracy: 0.9650 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9759 - val_loss: 0.1744 - val_accuracy: 0.9618 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1122 - accuracy: 0.9764 - val_loss: 0.1654 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1117 - accuracy: 0.9763 - val_loss: 0.1661 - val_accuracy: 0.9650 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1113 - accuracy: 0.9773 - val_loss: 0.1715 - val_accuracy: 0.9644 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1103 - accuracy: 0.9771 - val_loss: 0.1691 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1104 - accuracy: 0.9777 - val_loss: 0.1684 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1115 - accuracy: 0.9769 - val_loss: 0.1698 - val_accuracy: 0.9646 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1115 - accuracy: 0.9767 - val_loss: 0.1677 - val_accuracy: 0.9648 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1116 - accuracy: 0.9769 - val_loss: 0.1673 - val_accuracy: 0.9647 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1108 - accuracy: 0.9775 - val_loss: 0.1662 - val_accuracy: 0.9656 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 5.8590e-04 - accuracy: 0.9999 - val_loss: 0.0975 - val_accuracy: 0.9833 [-0. -0. -0. ... 0.40781373 -0.6702625 0. ] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6419e-04 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9843 [-0. -0. -0. ... 0.41248244 -0.67458117 0. ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8298e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9836 [-0. -0. -0. ... 0.41890672 -0.6830235 -0. ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4828e-05 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.42215222 -0.6881104 -0. ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3218e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.42413443 -0.6933588 0. ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7352e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.43164828 -0.69543904 -0. ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 15ms/step - loss: 2.0002e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9843 [-0. -0. -0. ... 0.4254886 -0.6936554 -0. ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5560e-05 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.42678383 -0.7083477 0. ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1227e-04 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9838 [-0. -0. -0. ... 0.42854083 -0.70800877 -0. ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 8.4910e-04 - accuracy: 0.9998 - val_loss: 0.1064 - val_accuracy: 0.9831 [-0. -0. -0. ... 0.43906522 -0.6968431 -0. ] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 6.6107e-04 - accuracy: 0.9998 - val_loss: 0.1063 - val_accuracy: 0.9819 [-0. -0. -0. ... 0.43379018 -0.6912776 -0. ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4326e-04 - accuracy: 0.9998 - val_loss: 0.1106 - val_accuracy: 0.9829 [-0. -0. -0. ... 0.43622416 -0.7270986 -0. ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0611e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9832 [-0. -0. -0. ... 0.43800637 -0.7380075 -0. ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3358e-04 - accuracy: 0.9999 - val_loss: 0.1059 - val_accuracy: 0.9830 [-0. -0. -0. ... 0.4373247 -0.74203116 0. ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9579e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9834 [-0. -0. -0. ... 0.4375715 -0.7389662 -0. ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5458e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9835 [-0. -0. -0. ... 0.43618035 -0.74026626 -0. ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2224e-05 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9842 [-0. -0. -0. ... 0.43202552 -0.74114835 -0. ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4911e-04 - accuracy: 0.9999 - val_loss: 0.1052 - val_accuracy: 0.9834 [-0. -0. -0. ... 0.43032756 -0.7426719 -0. ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0142e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9835 [-0. -0. -0. ... 0.42278704 -0.7434096 -0. ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7043e-05 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9832 [-0. -0. -0. ... 0.42467904 -0.7440927 -0. ] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3111e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9832 [-0. -0. -0. ... 0.42599857 -0.7450728 0. ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6380e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.4295014 -0.7415794 -0. ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3886e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.42286417 -0.73140395 -0. ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2524e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9836 [-0. -0. -0. ... 0.42159817 -0.73365486 -0. ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5957e-06 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9836 [-0. -0. -0. ... 0.42173636 -0.73345643 0. ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0742e-04 - accuracy: 0.9999 - val_loss: 0.1253 - val_accuracy: 0.9815 [-0. -0. -0. ... 0.4075093 -0.7376263 0. ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1180e-04 - accuracy: 0.9999 - val_loss: 0.1041 - val_accuracy: 0.9847 [-0. -0. -0. ... 0.41434512 -0.73106277 -0. ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2438e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.4216225 -0.73189074 -0. ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2084e-04 - accuracy: 0.9999 - val_loss: 0.1037 - val_accuracy: 0.9836 [-0. -0. -0. ... 0.42373183 -0.74150616 -0. ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9443e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9844 [-0. -0. -0. ... 0.4275105 -0.7408081 -0. ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3234e-04 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 0.9847 [-0. -0. -0. ... 0.4299386 -0.73702914 -0. ] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5078e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9849 [-0. -0. -0. ... 0.4322655 -0.73715705 -0. ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9856e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9844 [-0. -0. -0. ... 0.4370121 -0.73787594 -0. ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7946e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9842 [-0. -0. -0. ... 0.43955818 -0.73869187 0. ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9338e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9841 [-0. -0. -0. ... 0.43910703 -0.7376661 -0. ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0792e-05 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9843 [-0. -0. -0. ... 0.44001165 -0.7384584 -0. ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9759e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.44181287 -0.7369027 -0. ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9020e-06 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.44363716 -0.73844874 -0. ] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8925e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9844 [-0. -0. -0. ... 0.44543317 -0.73909175 -0. ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0013e-06 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.4471137 -0.7392173 0. ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4834e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.44829175 -0.7396168 -0. ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9493e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9841 [-0. -0. -0. ... 0.44931117 -0.74025536 0. ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4696e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.45113707 -0.7405083 -0. ] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7436e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.4510588 -0.7410652 -0. ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2679e-06 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.45323738 -0.7415785 -0. ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3470e-06 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.4553067 -0.74186593 -0. ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0194e-06 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.45586807 -0.7420865 0. ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7708e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.4580327 -0.74233294 0. ] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0250e-06 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46276712 -0.74283844 -0. ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5852e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46342826 -0.74296623 -0. ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1026 - val_accuracy: 0.9824 [-0. -0. -0. ... 0.48178092 -0.6867249 0. ] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8456e-04 - accuracy: 0.9998 - val_loss: 0.0968 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.46431142 -0.68329424 -0. ] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0848e-04 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46596488 -0.686497 -0. ] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6918e-05 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46603048 -0.6869063 0. ] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0014e-04 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46607792 -0.6856161 -0. ] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7721e-05 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 0.9842 [-0. -0. -0. ... 0.46618637 -0.68781096 -0. ] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3912e-05 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9838 [-0. -0. -0. ... 0.46792325 -0.6875334 -0. ] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6974e-05 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9838 [-0. -0. -0. ... 0.46795285 -0.68625325 0. ] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6255e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.45842677 -0.685627 -0. ] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5480e-05 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.46004164 -0.6832966 -0. ] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5547e-05 - accuracy: 1.0000 - val_loss: 0.1013 - val_accuracy: 0.9843 [-0. -0. -0. ... 0.46158594 -0.6847793 -0. ] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2447e-04 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9849 [-0. -0. -0. ... 0.47115117 -0.68501985 0. ] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4952e-05 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9849 [-0. -0. -0. ... 0.47229823 -0.68571186 0. ] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7700e-05 - accuracy: 1.0000 - val_loss: 0.1010 - val_accuracy: 0.9847 [-0. -0. -0. ... 0.47182652 -0.6856034 -0. ] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7152e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9843 [-0. -0. -0. ... 0.47207537 -0.6851744 -0. ] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2995e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9838 [-0. -0. -0. ... 0.47209558 -0.67741156 0. ] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2551e-04 - accuracy: 0.9999 - val_loss: 0.1073 - val_accuracy: 0.9841 [-0. -0. -0. ... 0.47031793 -0.68388474 0. ] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8513e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9835 [-0. -0. -0. ... 0.46896514 -0.6831152 0. ] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6792e-05 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9843 [-0. -0. -0. ... 0.4695946 -0.689099 -0. ] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4817e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.4688582 -0.6888881 0. ] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1332e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46795753 -0.6889045 -0. ] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8736e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.46993044 -0.68950456 -0. ] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8188e-06 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.47209388 -0.6897388 -0. ] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9155e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9839 [-0. -0. -0. ... 0.47312075 -0.689822 -0. ] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6444e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9846 [-0. -0. -0. ... 0.474284 -0.69014466 -0. ] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6727e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9836 [-0. -0. -0. ... 0.47651836 -0.69178396 -0. ] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9058e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9838 [-0. -0. -0. ... 0.47965217 -0.6917678 -0. ] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8798e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9842 [-0. -0. -0. ... 0.47856498 -0.6900147 -0. ] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1881e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9842 [-0. -0. -0. ... 0.48199216 -0.6930432 -0. ] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6779e-06 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9841 [-0. -0. -0. ... 0.48422685 -0.69352424 0. ] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2228e-06 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9840 [-0. -0. -0. ... 0.48736748 -0.69443977 -0. ] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5577e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9834 [-0. -0. -0. ... 0.49136993 -0.6949908 -0. ] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6086e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9835 [-0. -0. -0. ... 0.49325502 -0.6958957 -0. ] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4208e-05 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9837 [-0. -0. -0. ... 0.49402264 -0.6936562 0. ] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1432e-04 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9829 [-0. -0. -0. ... 0.5013987 -0.70756125 -0. ] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0645e-04 - accuracy: 0.9998 - val_loss: 0.1252 - val_accuracy: 0.9826 [-0. -0. -0. ... 0.513968 -0.7227696 -0. ] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6715e-04 - accuracy: 0.9999 - val_loss: 0.1138 - val_accuracy: 0.9847 [-0. -0. -0. ... 0.52013856 -0.71966434 0. ] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5452e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9841 [-0. -0. -0. ... 0.51841974 -0.7110601 0. ] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1851e-05 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9845 [-0. -0. -0. ... 0.51782084 -0.71312135 0. ] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2202e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9845 [-0. -0. -0. ... 0.5179846 -0.7156176 0. ] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0490e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9845 [-0. -0. -0. ... 0.5184238 -0.7164969 -0. ] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8522e-06 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9847 [-0. -0. -0. ... 0.51874816 -0.7172678 -0. ] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3946e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9852 [-0. -0. -0. ... 0.5191075 -0.7183986 -0. ] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8614e-06 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9850 [-0. -0. -0. ... 0.5189788 -0.7187473 0. ] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1649e-06 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9851 [-0. -0. -0. ... 0.5190248 -0.71854764 0. ] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8409e-06 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9849 [-0. -0. -0. ... 0.5190666 -0.7194177 -0. ] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4370e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9847 [-0. -0. -0. ... 0.5193495 -0.72256726 -0. ] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 4s 15ms/step - loss: 4.8004e-06 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9850 [-0. -0. -0. ... 0.51888883 -0.72128445 -0. ] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4695e-06 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9849 [-0. -0. -0. ... 0.5184803 -0.7217318 0. ] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3234e-06 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9850 [-0. -0. -0. ... 0.51923686 -0.72159654 -0. ] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9973 - val_loss: 0.1107 - val_accuracy: 0.9812 [-0. -0. -0. ... 0. -0.7090838 -0. ] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3727e-04 - accuracy: 0.9998 - val_loss: 0.1043 - val_accuracy: 0.9811 [-0. -0. -0. ... 0. -0.70824796 0. ] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7084e-04 - accuracy: 0.9999 - val_loss: 0.1019 - val_accuracy: 0.9824 [-0. -0. -0. ... 0. -0.7100668 -0. ] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2604e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9821 [-0. -0. -0. ... 0. -0.7165425 -0. ] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4502e-04 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9821 [-0. -0. -0. ... 0. -0.7190276 -0. ] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3564e-04 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9820 [-0. -0. -0. ... 0. -0.7206364 -0. ] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4328e-04 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9818 [-0. -0. -0. ... -0. -0.7302709 0. ] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6617e-04 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9816 [-0. -0. -0. ... 0. -0.73292136 -0. ] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7988e-05 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9827 [-0. -0. -0. ... 0. -0.72863805 -0. ] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3119e-04 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9821 [-0. -0. -0. ... -0. -0.72989786 -0. ] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6388e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9826 [-0. -0. -0. ... 0. -0.7320777 -0. ] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3514e-05 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9829 [-0. -0. -0. ... 0. -0.73680156 -0. ] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7401e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9827 [-0. -0. -0. ... 0. -0.7392906 -0. ] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7103e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9825 [-0. -0. -0. ... 0. -0.74020344 -0. ] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0599e-05 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9829 [-0. -0. -0. ... 0. -0.74142325 -0. ] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9157e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9830 [-0. -0. -0. ... 0. -0.74358857 -0. ] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 9.9986e-05 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9836 [-0. -0. -0. ... 0. -0.7496931 -0. ] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4055e-05 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9835 [-0. -0. -0. ... 0. -0.76006985 -0. ] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1105e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9831 [-0. -0. -0. ... 0. -0.75977385 0. ] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4541e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9833 [-0. -0. -0. ... 0. -0.7584823 0. ] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3889e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9837 [-0. -0. -0. ... 0. -0.75596267 -0. ] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 4s 15ms/step - loss: 2.2982e-05 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9835 [-0. -0. -0. ... -0. -0.75933176 -0. ] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1371e-05 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9833 [-0. -0. -0. ... -0. -0.75925076 -0. ] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1428e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9834 [-0. -0. -0. ... 0. -0.7564262 -0. ] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8006e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9832 [-0. -0. -0. ... 0. -0.7622098 -0. ] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 15ms/step - loss: 8.9676e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9819 [-0. -0. -0. ... 0. -0.7654799 0. ] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6773e-04 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9823 [-0. -0. -0. ... 0. -0.76584786 -0. ] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0419e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9820 [-0. -0. -0. ... -0. -0.7734696 0. ] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1962e-05 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9832 [-0. -0. -0. ... 0. -0.76772165 0. ] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1949e-05 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9828 [-0. -0. -0. ... 0. -0.7746529 -0. ] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4181e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9832 [-0. -0. -0. ... 0. -0.7761626 -0. ] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2879e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9833 [-0. -0. -0. ... 0. -0.7850816 -0. ] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0176e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9834 [-0. -0. -0. ... 0. -0.7857657 0. ] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0057e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9835 [-0. -0. -0. ... 0. -0.7858126 0. ] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8259e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9835 [-0. -0. -0. ... 0. -0.7861163 0. ] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7259e-06 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9826 [-0. -0. -0. ... -0. -0.78862035 -0. ] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1734e-06 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9830 [-0. -0. -0. ... 0. -0.79434806 -0. ] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 4s 15ms/step - loss: 5.9578e-06 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9832 [-0. -0. -0. ... 0. -0.79496825 -0. ] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1332e-06 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9829 [-0. -0. -0. ... 0. -0.801339 -0. ] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8694e-06 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9829 [-0. -0. -0. ... 0. -0.7974943 -0. ] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1725e-05 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9826 [-0. -0. -0. ... 0. -0.8124483 0. ] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1808e-05 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9821 [-0. -0. -0. ... -0. -0.81734043 0. ] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5202e-04 - accuracy: 0.9999 - val_loss: 0.1247 - val_accuracy: 0.9819 [-0. -0. -0. ... 0. -0.8370379 -0. ] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3877e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9822 [-0. -0. -0. ... 0. -0.8426857 -0. ] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8964e-04 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9816 [-0. -0. -0. ... 0. -0.8132085 -0. ] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7583e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9828 [-0. -0. -0. ... -0. -0.8088592 0. ] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3995e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9828 [-0. -0. -0. ... 0. -0.81171286 0. ] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8134e-06 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9831 [-0. -0. -0. ... 0. -0.81200993 -0. ] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3990e-05 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9827 [-0. -0. -0. ... 0. -0.81315994 0. ] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0048e-06 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9829 [-0. -0. -0. ... 0. -0.8032662 0. ] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0236 - accuracy: 0.9932 - val_loss: 0.1159 - val_accuracy: 0.9791 [-0. -0. -0. ... 0. -0.80972826 -0. ] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.1121 - val_accuracy: 0.9799 [-0. -0. -0. ... 0. -0.80957514 -0. ] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 0.1128 - val_accuracy: 0.9795 [-0. -0. -0. ... 0. -0.81041396 -0. ] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 4s 15ms/step - loss: 9.4598e-04 - accuracy: 0.9999 - val_loss: 0.1126 - val_accuracy: 0.9798 [-0. -0. -0. ... -0. -0.80594814 -0. ] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 15ms/step - loss: 7.9811e-04 - accuracy: 0.9999 - val_loss: 0.1133 - val_accuracy: 0.9800 [-0. -0. -0. ... -0. -0.81086886 -0. ] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4480e-04 - accuracy: 0.9999 - val_loss: 0.1125 - val_accuracy: 0.9802 [-0. -0. -0. ... -0. -0.81333303 -0. ] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2359e-04 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9802 [-0. -0. -0. ... 0. -0.81643355 -0. ] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7733e-04 - accuracy: 0.9999 - val_loss: 0.1128 - val_accuracy: 0.9803 [-0. -0. -0. ... 0. -0.81359005 -0. ] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6192e-04 - accuracy: 0.9999 - val_loss: 0.1137 - val_accuracy: 0.9807 [-0. -0. -0. ... 0. -0.8156675 -0. ] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8211e-04 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9803 [-0. -0. -0. ... 0. -0.815073 -0. ] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1896e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9808 [-0. -0. -0. ... -0. -0.827741 -0. ] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 4s 15ms/step - loss: 2.8961e-04 - accuracy: 1.0000 - val_loss: 0.1128 - val_accuracy: 0.9808 [-0. -0. -0. ... 0. -0.8233135 -0. ] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2188e-04 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9810 [-0. -0. -0. ... 0. -0.8265783 -0. ] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9849e-04 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9806 [-0. -0. -0. ... -0. -0.83232355 -0. ] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0699e-04 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9810 [-0. -0. -0. ... -0. -0.83675647 -0. ] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2061e-04 - accuracy: 0.9999 - val_loss: 0.1158 - val_accuracy: 0.9810 [-0. -0. -0. ... -0. -0.8519762 -0. ] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7052e-04 - accuracy: 0.9999 - val_loss: 0.1161 - val_accuracy: 0.9811 [-0. -0. -0. ... -0. -0.8522523 -0. ] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8257e-04 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9812 [-0. -0. -0. ... 0. -0.8558983 -0. ] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7170e-04 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9809 [-0. -0. -0. ... 0. -0.8509458 -0. ] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2143e-04 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9814 [-0. -0. -0. ... 0. -0.8583202 -0. ] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7302e-05 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9811 [-0. -0. -0. ... -0. -0.8568227 0. ] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1187e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9809 [-0. -0. -0. ... -0. -0.8577506 -0. ] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6365e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9811 [-0. -0. -0. ... -0. -0.86152947 0. ] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0476e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9811 [-0. -0. -0. ... 0. -0.85924774 -0. ] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 6.6880e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9817 [-0. -0. -0. ... -0. -0.8715754 -0. ] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5983e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9817 [-0. -0. -0. ... 0. -0.8773704 0. ] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0157e-04 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9820 [-0. -0. -0. ... 0. -0.8765876 -0. ] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2829e-05 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9813 [-0. -0. -0. ... 0. -0.88136876 -0. ] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0584e-05 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9815 [-0. -0. -0. ... -0. -0.8819839 -0. ] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7264e-05 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9812 [-0. -0. -0. ... 0. -0.8932061 -0. ] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1811e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9823 [-0. -0. -0. ... -0. -0.8936125 0. ] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7779e-05 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9820 [-0. -0. -0. ... 0. -0.8970623 -0. ] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7727e-05 - accuracy: 1.0000 - val_loss: 0.1220 - val_accuracy: 0.9820 [-0. -0. -0. ... 0. -0.89811736 0. ] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0659e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9819 [-0. -0. -0. ... 0. -0.8914719 -0. ] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8258e-05 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9823 [-0. -0. -0. ... -0. -0.8914511 -0. ] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2085e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9818 [-0. -0. -0. ... -0. -0.89946485 -0. ] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 4s 15ms/step - loss: 2.5564e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9816 [-0. -0. -0. ... -0. -0.8966538 0. ] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 4s 16ms/step - loss: 2.3930e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9815 [-0. -0. -0. ... -0. -0.897169 0. ] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0283e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9822 [-0. -0. -0. ... -0. -0.9037009 -0. ] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5179e-04 - accuracy: 0.9999 - val_loss: 0.1316 - val_accuracy: 0.9812 [-0. -0. -0. ... 0. -0.8919588 0. ] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2473e-05 - accuracy: 1.0000 - val_loss: 0.1308 - val_accuracy: 0.9816 [-0. -0. -0. ... -0. -0.9211406 0. ] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6800e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9827 [-0. -0. -0. ... -0. -0.92473525 -0. ] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5717e-04 - accuracy: 0.9999 - val_loss: 0.1301 - val_accuracy: 0.9815 [-0. -0. -0. ... 0. -0.88578576 -0. ] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0455e-05 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9821 [-0. -0. -0. ... 0. -0.8870065 -0. ] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4638e-05 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9823 [-0. -0. -0. ... -0. -0.88749945 -0. ] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6074e-05 - accuracy: 1.0000 - val_loss: 0.1321 - val_accuracy: 0.9821 [-0. -0. -0. ... 0. -0.88724506 0. ] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0812e-05 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9823 [-0. -0. -0. ... 0. -0.8880428 -0. ] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7899e-05 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9823 [-0. -0. -0. ... -0. -0.8888484 -0. ] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5523e-05 - accuracy: 1.0000 - val_loss: 0.1329 - val_accuracy: 0.9817 [-0. -0. -0. ... 0. -0.8903026 -0. ] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7097e-05 - accuracy: 1.0000 - val_loss: 0.1339 - val_accuracy: 0.9823 [-0. -0. -0. ... -0. -0.89018834 -0. ] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0722 - accuracy: 0.9808 - val_loss: 0.1361 - val_accuracy: 0.9726 0s - loss: 0.0760 - ac [-0. -0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0226 - accuracy: 0.9924 - val_loss: 0.1287 - val_accuracy: 0.9740 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0143 - accuracy: 0.9954 - val_loss: 0.1250 - val_accuracy: 0.9762 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9968 - val_loss: 0.1228 - val_accuracy: 0.9760 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9981 - val_loss: 0.1233 - val_accuracy: 0.9762 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0058 - accuracy: 0.9987 - val_loss: 0.1219 - val_accuracy: 0.9766 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0045 - accuracy: 0.9991 - val_loss: 0.1233 - val_accuracy: 0.9764 [-0. -0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1236 - val_accuracy: 0.9768 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.1241 - val_accuracy: 0.9775 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9998 - val_loss: 0.1240 - val_accuracy: 0.9770 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.1256 - val_accuracy: 0.9765 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.1265 - val_accuracy: 0.9769 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.1271 - val_accuracy: 0.9770 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1278 - val_accuracy: 0.9769 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1279 - val_accuracy: 0.9766 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1282 - val_accuracy: 0.9766 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1289 - val_accuracy: 0.9768 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1314 - val_accuracy: 0.9768 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3636e-04 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9769 [-0. -0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1944e-04 - accuracy: 0.9999 - val_loss: 0.1315 - val_accuracy: 0.9772 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5256e-04 - accuracy: 0.9999 - val_loss: 0.1340 - val_accuracy: 0.9766 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3477e-04 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9774 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4158e-04 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9770 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8427e-04 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9772 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4987e-04 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9773 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1665e-04 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9775 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4172e-04 - accuracy: 1.0000 - val_loss: 0.1395 - val_accuracy: 0.9771 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0189e-04 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9774 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4170e-04 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9776 [-0. -0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1854e-04 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9777 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1784e-04 - accuracy: 0.9999 - val_loss: 0.1474 - val_accuracy: 0.9768 [-0. -0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9013e-04 - accuracy: 1.0000 - val_loss: 0.1463 - val_accuracy: 0.9776 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7281e-04 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9764 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7038e-04 - accuracy: 1.0000 - val_loss: 0.1494 - val_accuracy: 0.9773 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1095e-04 - accuracy: 1.0000 - val_loss: 0.1491 - val_accuracy: 0.9770 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8936e-04 - accuracy: 1.0000 - val_loss: 0.1501 - val_accuracy: 0.9779 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7438e-04 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9762 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5712e-04 - accuracy: 1.0000 - val_loss: 0.1545 - val_accuracy: 0.9775 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8010e-04 - accuracy: 1.0000 - val_loss: 0.1557 - val_accuracy: 0.9773 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3616e-04 - accuracy: 1.0000 - val_loss: 0.1555 - val_accuracy: 0.9774 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7390e-04 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9780 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4767e-04 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9781 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1731e-04 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9776 [-0. -0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8444e-04 - accuracy: 1.0000 - val_loss: 0.1626 - val_accuracy: 0.9776 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4371e-04 - accuracy: 1.0000 - val_loss: 0.1626 - val_accuracy: 0.9775 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1538e-04 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9773 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8988e-05 - accuracy: 1.0000 - val_loss: 0.1639 - val_accuracy: 0.9776 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2483e-04 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9775 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3103e-04 - accuracy: 1.0000 - val_loss: 0.1633 - val_accuracy: 0.9785 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1883e-05 - accuracy: 1.0000 - val_loss: 0.1668 - val_accuracy: 0.9770 [-0. -0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1710 - accuracy: 0.9593 - val_loss: 0.2129 - val_accuracy: 0.9583 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0814 - accuracy: 0.9768 - val_loss: 0.1857 - val_accuracy: 0.9624 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0626 - accuracy: 0.9809 - val_loss: 0.1703 - val_accuracy: 0.9640 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0527 - accuracy: 0.9831 - val_loss: 0.1621 - val_accuracy: 0.9652 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0470 - accuracy: 0.9845 - val_loss: 0.1549 - val_accuracy: 0.9664 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0418 - accuracy: 0.9861 - val_loss: 0.1507 - val_accuracy: 0.9670 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0377 - accuracy: 0.9878 - val_loss: 0.1478 - val_accuracy: 0.9670 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0348 - accuracy: 0.9886 - val_loss: 0.1458 - val_accuracy: 0.9671 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0323 - accuracy: 0.9894 - val_loss: 0.1441 - val_accuracy: 0.9674 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.1428 - val_accuracy: 0.9672 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0286 - accuracy: 0.9907 - val_loss: 0.1413 - val_accuracy: 0.9680 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0263 - accuracy: 0.9915 - val_loss: 0.1403 - val_accuracy: 0.9679 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0253 - accuracy: 0.9916 - val_loss: 0.1398 - val_accuracy: 0.9694 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0235 - accuracy: 0.9924 - val_loss: 0.1396 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0216 - accuracy: 0.9930 - val_loss: 0.1410 - val_accuracy: 0.9692 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0203 - accuracy: 0.9935 - val_loss: 0.1406 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0191 - accuracy: 0.9941 - val_loss: 0.1402 - val_accuracy: 0.9695 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0182 - accuracy: 0.9945 - val_loss: 0.1427 - val_accuracy: 0.9689 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0172 - accuracy: 0.9949 - val_loss: 0.1426 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0164 - accuracy: 0.9950 - val_loss: 0.1434 - val_accuracy: 0.9689 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0159 - accuracy: 0.9953 - val_loss: 0.1441 - val_accuracy: 0.9688 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0144 - accuracy: 0.9958 - val_loss: 0.1455 - val_accuracy: 0.9687 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0142 - accuracy: 0.9958 - val_loss: 0.1461 - val_accuracy: 0.9695 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0134 - accuracy: 0.9963 - val_loss: 0.1464 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 0.1476 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0122 - accuracy: 0.9966 - val_loss: 0.1483 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 0.1503 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0108 - accuracy: 0.9971 - val_loss: 0.1521 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9972 - val_loss: 0.1537 - val_accuracy: 0.9695 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9973 - val_loss: 0.1550 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.1551 - val_accuracy: 0.9695 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0085 - accuracy: 0.9980 - val_loss: 0.1579 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.1582 - val_accuracy: 0.9701 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0077 - accuracy: 0.9984 - val_loss: 0.1613 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0075 - accuracy: 0.9984 - val_loss: 0.1619 - val_accuracy: 0.9699 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0075 - accuracy: 0.9984 - val_loss: 0.1614 - val_accuracy: 0.9695 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9988 - val_loss: 0.1637 - val_accuracy: 0.9699 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0062 - accuracy: 0.9989 - val_loss: 0.1661 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0060 - accuracy: 0.9989 - val_loss: 0.1681 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9987 - val_loss: 0.1696 - val_accuracy: 0.9694 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9990 - val_loss: 0.1702 - val_accuracy: 0.9702 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9993 - val_loss: 0.1731 - val_accuracy: 0.9694 [-0. -0. -0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0052 - accuracy: 0.9990 - val_loss: 0.1740 - val_accuracy: 0.9703 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0049 - accuracy: 0.9993 - val_loss: 0.1768 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9992 - val_loss: 0.1783 - val_accuracy: 0.9700 [-0. -0. -0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1786 - val_accuracy: 0.9695 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0042 - accuracy: 0.9993 - val_loss: 0.1801 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1801 - val_accuracy: 0.9699 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.1824 - val_accuracy: 0.9701 [-0. -0. -0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1831 - val_accuracy: 0.9700 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5240 - accuracy: 0.8715 - val_loss: 0.4106 - val_accuracy: 0.8947 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.3049 - accuracy: 0.9134 - val_loss: 0.3294 - val_accuracy: 0.9167 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2606 - accuracy: 0.9238 - val_loss: 0.2947 - val_accuracy: 0.9232 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2365 - accuracy: 0.9298 - val_loss: 0.2738 - val_accuracy: 0.9271 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2221 - accuracy: 0.9330 - val_loss: 0.2613 - val_accuracy: 0.9303 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 12ms/step - loss: 0.2090 - accuracy: 0.9364 - val_loss: 0.2521 - val_accuracy: 0.9306 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2006 - accuracy: 0.9386 - val_loss: 0.2444 - val_accuracy: 0.9317 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1934 - accuracy: 0.9406 - val_loss: 0.2387 - val_accuracy: 0.9338 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1869 - accuracy: 0.9425 - val_loss: 0.2338 - val_accuracy: 0.9350 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1818 - accuracy: 0.9437 - val_loss: 0.2294 - val_accuracy: 0.9363 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1773 - accuracy: 0.9455 - val_loss: 0.2258 - val_accuracy: 0.9374 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1738 - accuracy: 0.9465 - val_loss: 0.2231 - val_accuracy: 0.9384 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1697 - accuracy: 0.9481 - val_loss: 0.2198 - val_accuracy: 0.9389 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1663 - accuracy: 0.9481 - val_loss: 0.2171 - val_accuracy: 0.9398 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1630 - accuracy: 0.9492 - val_loss: 0.2149 - val_accuracy: 0.9402 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1608 - accuracy: 0.9499 - val_loss: 0.2132 - val_accuracy: 0.9406 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1572 - accuracy: 0.9508 - val_loss: 0.2113 - val_accuracy: 0.9415 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1561 - accuracy: 0.9510 - val_loss: 0.2098 - val_accuracy: 0.9416 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1543 - accuracy: 0.9517 - val_loss: 0.2083 - val_accuracy: 0.9423 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1518 - accuracy: 0.9528 - val_loss: 0.2065 - val_accuracy: 0.9424 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1495 - accuracy: 0.9534 - val_loss: 0.2049 - val_accuracy: 0.9416 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1476 - accuracy: 0.9542 - val_loss: 0.2030 - val_accuracy: 0.9419 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9547 - val_loss: 0.2019 - val_accuracy: 0.9417 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1438 - accuracy: 0.9553 - val_loss: 0.2008 - val_accuracy: 0.9425 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9553 - val_loss: 0.1993 - val_accuracy: 0.9424 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9555 - val_loss: 0.1979 - val_accuracy: 0.9420 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1396 - accuracy: 0.9560 - val_loss: 0.1966 - val_accuracy: 0.9426 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9564 - val_loss: 0.1956 - val_accuracy: 0.9427 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9576 - val_loss: 0.1951 - val_accuracy: 0.9436 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9575 - val_loss: 0.1943 - val_accuracy: 0.9436 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9575 - val_loss: 0.1938 - val_accuracy: 0.9431 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9580 - val_loss: 0.1933 - val_accuracy: 0.9430 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9597 - val_loss: 0.1933 - val_accuracy: 0.9430 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9591 - val_loss: 0.1920 - val_accuracy: 0.9435 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9594 - val_loss: 0.1921 - val_accuracy: 0.9441 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9594 - val_loss: 0.1916 - val_accuracy: 0.9445 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9596 - val_loss: 0.1912 - val_accuracy: 0.9446 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9610 - val_loss: 0.1901 - val_accuracy: 0.9449 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9609 - val_loss: 0.1898 - val_accuracy: 0.9453 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9608 - val_loss: 0.1896 - val_accuracy: 0.9450 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.1889 - val_accuracy: 0.9454 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9616 - val_loss: 0.1884 - val_accuracy: 0.9449 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9619 - val_loss: 0.1878 - val_accuracy: 0.9458 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9619 - val_loss: 0.1875 - val_accuracy: 0.9457 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9618 - val_loss: 0.1873 - val_accuracy: 0.9463 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9624 - val_loss: 0.1868 - val_accuracy: 0.9465 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9622 - val_loss: 0.1867 - val_accuracy: 0.9467 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9624 - val_loss: 0.1859 - val_accuracy: 0.9469 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9630 - val_loss: 0.1862 - val_accuracy: 0.9468 [-0. -0. -0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9630 - val_loss: 0.1857 - val_accuracy: 0.9471 [-0. -0. -0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1209 - accuracy: 0.6780 - val_loss: 0.9906 - val_accuracy: 0.6829 [-0. -0. -0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9224 - accuracy: 0.6975 - val_loss: 0.9064 - val_accuracy: 0.6992 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8830 - accuracy: 0.7041 - val_loss: 0.8826 - val_accuracy: 0.7031 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8559 - accuracy: 0.7089 - val_loss: 0.8602 - val_accuracy: 0.7078 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8387 - accuracy: 0.7099 - val_loss: 0.8435 - val_accuracy: 0.7106 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8271 - accuracy: 0.7118 - val_loss: 0.8340 - val_accuracy: 0.7116 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8176 - accuracy: 0.7127 - val_loss: 0.8268 - val_accuracy: 0.7128 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8101 - accuracy: 0.7145 - val_loss: 0.8203 - val_accuracy: 0.7130 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8043 - accuracy: 0.7151 - val_loss: 0.8146 - val_accuracy: 0.7151 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7983 - accuracy: 0.7162 - val_loss: 0.8095 - val_accuracy: 0.7151 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7931 - accuracy: 0.7176 - val_loss: 0.8041 - val_accuracy: 0.7174 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7877 - accuracy: 0.7173 - val_loss: 0.7984 - val_accuracy: 0.7190 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7819 - accuracy: 0.7201 - val_loss: 0.7937 - val_accuracy: 0.7193 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7776 - accuracy: 0.7206 - val_loss: 0.7892 - val_accuracy: 0.7192 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7747 - accuracy: 0.7212 - val_loss: 0.7851 - val_accuracy: 0.7198 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7700 - accuracy: 0.7228 - val_loss: 0.7817 - val_accuracy: 0.7206 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 4s 15ms/step - loss: 0.7674 - accuracy: 0.7234 - val_loss: 0.7791 - val_accuracy: 0.7208 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7657 - accuracy: 0.7229 - val_loss: 0.7766 - val_accuracy: 0.7220 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7635 - accuracy: 0.7239 - val_loss: 0.7747 - val_accuracy: 0.7220 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7612 - accuracy: 0.7245 - val_loss: 0.7728 - val_accuracy: 0.7232 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7598 - accuracy: 0.7248 - val_loss: 0.7709 - val_accuracy: 0.7229 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7576 - accuracy: 0.7247 - val_loss: 0.7689 - val_accuracy: 0.7231 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7559 - accuracy: 0.7256 - val_loss: 0.7666 - val_accuracy: 0.7235 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7542 - accuracy: 0.7260 - val_loss: 0.7648 - val_accuracy: 0.7240 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7533 - accuracy: 0.7257 - val_loss: 0.7634 - val_accuracy: 0.7234 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7513 - accuracy: 0.7258 - val_loss: 0.7622 - val_accuracy: 0.7244 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7504 - accuracy: 0.7271 - val_loss: 0.7606 - val_accuracy: 0.7235 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 12ms/step - loss: 0.7492 - accuracy: 0.7261 - val_loss: 0.7594 - val_accuracy: 0.7237 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 13ms/step - loss: 0.7485 - accuracy: 0.7258 - val_loss: 0.7589 - val_accuracy: 0.7245 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7468 - accuracy: 0.7267 - val_loss: 0.7579 - val_accuracy: 0.7250 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7461 - accuracy: 0.7270 - val_loss: 0.7571 - val_accuracy: 0.7248 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7454 - accuracy: 0.7265 - val_loss: 0.7565 - val_accuracy: 0.7244 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7449 - accuracy: 0.7268 - val_loss: 0.7556 - val_accuracy: 0.7255 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7437 - accuracy: 0.7268 - val_loss: 0.7548 - val_accuracy: 0.7250 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7437 - accuracy: 0.7272 - val_loss: 0.7541 - val_accuracy: 0.7257 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7428 - accuracy: 0.7278 - val_loss: 0.7534 - val_accuracy: 0.7257 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7425 - accuracy: 0.7269 - val_loss: 0.7530 - val_accuracy: 0.7258 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7419 - accuracy: 0.7275 - val_loss: 0.7525 - val_accuracy: 0.7263 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7415 - accuracy: 0.7276 - val_loss: 0.7516 - val_accuracy: 0.7262 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7404 - accuracy: 0.7277 - val_loss: 0.7514 - val_accuracy: 0.7276 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7407 - accuracy: 0.7281 - val_loss: 0.7504 - val_accuracy: 0.7270 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7401 - accuracy: 0.7281 - val_loss: 0.7503 - val_accuracy: 0.7271 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7394 - accuracy: 0.7279 - val_loss: 0.7503 - val_accuracy: 0.7269 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7395 - accuracy: 0.7276 - val_loss: 0.7498 - val_accuracy: 0.7266 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7391 - accuracy: 0.7286 - val_loss: 0.7493 - val_accuracy: 0.7274 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7394 - accuracy: 0.7282 - val_loss: 0.7493 - val_accuracy: 0.7275 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7386 - accuracy: 0.7282 - val_loss: 0.7491 - val_accuracy: 0.7277 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7383 - accuracy: 0.7279 - val_loss: 0.7486 - val_accuracy: 0.7279 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7383 - accuracy: 0.7283 - val_loss: 0.7489 - val_accuracy: 0.7280 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7372 - accuracy: 0.7283 - val_loss: 0.7481 - val_accuracy: 0.7279 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3443 - accuracy: 0.5385 - val_loss: 1.2891 - val_accuracy: 0.5471 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2449 - accuracy: 0.5578 - val_loss: 1.3165 - val_accuracy: 0.5183 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2270 - accuracy: 0.5602 - val_loss: 1.2269 - val_accuracy: 0.5563 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2182 - accuracy: 0.5625 - val_loss: 1.2220 - val_accuracy: 0.5582 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2173 - accuracy: 0.5634 - val_loss: 1.2234 - val_accuracy: 0.5595 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2137 - accuracy: 0.5656 - val_loss: 1.2132 - val_accuracy: 0.5657 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2116 - accuracy: 0.5647 - val_loss: 1.2134 - val_accuracy: 0.5610 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2093 - accuracy: 0.5680 - val_loss: 1.2129 - val_accuracy: 0.5697 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2071 - accuracy: 0.5697 - val_loss: 1.2138 - val_accuracy: 0.5665 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2054 - accuracy: 0.5696 - val_loss: 1.2083 - val_accuracy: 0.5711 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2033 - accuracy: 0.5705 - val_loss: 1.2078 - val_accuracy: 0.5708 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2030 - accuracy: 0.5706 - val_loss: 1.2089 - val_accuracy: 0.5649 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2025 - accuracy: 0.5706 - val_loss: 1.2084 - val_accuracy: 0.5644 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2018 - accuracy: 0.5702 - val_loss: 1.2027 - val_accuracy: 0.5632 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1992 - accuracy: 0.5719 - val_loss: 1.2023 - val_accuracy: 0.5697 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1977 - accuracy: 0.5719 - val_loss: 1.1996 - val_accuracy: 0.5704 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1919 - accuracy: 0.5748 - val_loss: 1.1910 - val_accuracy: 0.5740 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1896 - accuracy: 0.5744 - val_loss: 1.1866 - val_accuracy: 0.5737 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1873 - accuracy: 0.5754 - val_loss: 1.1873 - val_accuracy: 0.5747 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1872 - accuracy: 0.5752 - val_loss: 1.1874 - val_accuracy: 0.5743 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1848 - accuracy: 0.5766 - val_loss: 1.1888 - val_accuracy: 0.5710 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1879 - accuracy: 0.5744 - val_loss: 1.1842 - val_accuracy: 0.5689 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1847 - accuracy: 0.5758 - val_loss: 1.1857 - val_accuracy: 0.5747 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1857 - accuracy: 0.5756 - val_loss: 1.1830 - val_accuracy: 0.5732 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1835 - accuracy: 0.5763 - val_loss: 1.1837 - val_accuracy: 0.5747 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1836 - accuracy: 0.5763 - val_loss: 1.1833 - val_accuracy: 0.5748 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1836 - accuracy: 0.5765 - val_loss: 1.1824 - val_accuracy: 0.5739 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1832 - accuracy: 0.5777 - val_loss: 1.1828 - val_accuracy: 0.5754 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1821 - accuracy: 0.5770 - val_loss: 1.1826 - val_accuracy: 0.5760 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1811 - accuracy: 0.5772 - val_loss: 1.1828 - val_accuracy: 0.5767 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1799 - accuracy: 0.5788 - val_loss: 1.1826 - val_accuracy: 0.5782 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1795 - accuracy: 0.5786 - val_loss: 1.1835 - val_accuracy: 0.5744 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1806 - accuracy: 0.5782 - val_loss: 1.1809 - val_accuracy: 0.5790 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1793 - accuracy: 0.5783 - val_loss: 1.1797 - val_accuracy: 0.5791 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1790 - accuracy: 0.5788 - val_loss: 1.1781 - val_accuracy: 0.5779 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1782 - accuracy: 0.5791 - val_loss: 1.1774 - val_accuracy: 0.5770 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1773 - accuracy: 0.5786 - val_loss: 1.1783 - val_accuracy: 0.5778 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1774 - accuracy: 0.5791 - val_loss: 1.1782 - val_accuracy: 0.5783 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1763 - accuracy: 0.5789 - val_loss: 1.1782 - val_accuracy: 0.5784 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1774 - accuracy: 0.5794 - val_loss: 1.1780 - val_accuracy: 0.5788 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1766 - accuracy: 0.5802 - val_loss: 1.1758 - val_accuracy: 0.5787 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1763 - accuracy: 0.5810 - val_loss: 1.1770 - val_accuracy: 0.5787 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1746 - accuracy: 0.5804 - val_loss: 1.1800 - val_accuracy: 0.5796 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1761 - accuracy: 0.5802 - val_loss: 1.1792 - val_accuracy: 0.5797 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1763 - accuracy: 0.5800 - val_loss: 1.1767 - val_accuracy: 0.5799 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1752 - accuracy: 0.5798 - val_loss: 1.1739 - val_accuracy: 0.5788 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1752 - accuracy: 0.5797 - val_loss: 1.1756 - val_accuracy: 0.5797 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1745 - accuracy: 0.5811 - val_loss: 1.1772 - val_accuracy: 0.5809 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1744 - accuracy: 0.5804 - val_loss: 1.1778 - val_accuracy: 0.5810 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1742 - accuracy: 0.5806 - val_loss: 1.1754 - val_accuracy: 0.5809 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1754 - accuracy: 0.5799 - val_loss: 1.1742 - val_accuracy: 0.5790 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1748 - accuracy: 0.5797 - val_loss: 1.1758 - val_accuracy: 0.5807 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1740 - accuracy: 0.5810 - val_loss: 1.1755 - val_accuracy: 0.5807 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1743 - accuracy: 0.5810 - val_loss: 1.1750 - val_accuracy: 0.5811 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1739 - accuracy: 0.5807 - val_loss: 1.1748 - val_accuracy: 0.5812 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 12ms/step - loss: 1.1728 - accuracy: 0.5806 - val_loss: 1.1754 - val_accuracy: 0.5816 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1733 - accuracy: 0.5811 - val_loss: 1.1745 - val_accuracy: 0.5814 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1735 - accuracy: 0.5809 - val_loss: 1.1802 - val_accuracy: 0.5766 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1729 - accuracy: 0.5808 - val_loss: 1.1763 - val_accuracy: 0.5816 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1733 - accuracy: 0.5804 - val_loss: 1.1797 - val_accuracy: 0.5777 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1726 - accuracy: 0.5810 - val_loss: 1.1848 - val_accuracy: 0.5760 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1732 - accuracy: 0.5804 - val_loss: 1.1722 - val_accuracy: 0.5801 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1726 - accuracy: 0.5812 - val_loss: 1.1767 - val_accuracy: 0.5816 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1723 - accuracy: 0.5803 - val_loss: 1.1809 - val_accuracy: 0.5761 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1717 - accuracy: 0.5813 - val_loss: 1.1799 - val_accuracy: 0.5764 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1724 - accuracy: 0.5807 - val_loss: 1.1766 - val_accuracy: 0.5818 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1715 - accuracy: 0.5810 - val_loss: 1.1714 - val_accuracy: 0.5806 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1715 - accuracy: 0.5817 - val_loss: 1.1736 - val_accuracy: 0.5814 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1715 - accuracy: 0.5817 - val_loss: 1.1741 - val_accuracy: 0.5824 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1725 - accuracy: 0.5808 - val_loss: 1.1709 - val_accuracy: 0.5755 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1709 - accuracy: 0.5812 - val_loss: 1.1720 - val_accuracy: 0.5816 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1718 - accuracy: 0.5806 - val_loss: 1.1705 - val_accuracy: 0.5796 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1713 - accuracy: 0.5817 - val_loss: 1.1719 - val_accuracy: 0.5807 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1712 - accuracy: 0.5812 - val_loss: 1.1716 - val_accuracy: 0.5807 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1702 - accuracy: 0.5807 - val_loss: 1.1725 - val_accuracy: 0.5809 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1692 - accuracy: 0.5814 - val_loss: 1.1707 - val_accuracy: 0.5813 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1696 - accuracy: 0.5811 - val_loss: 1.1717 - val_accuracy: 0.5820 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1680 - accuracy: 0.5822 - val_loss: 1.1714 - val_accuracy: 0.5821 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1683 - accuracy: 0.5822 - val_loss: 1.1679 - val_accuracy: 0.5806 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1673 - accuracy: 0.5819 - val_loss: 1.1668 - val_accuracy: 0.5804 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1685 - accuracy: 0.5815 - val_loss: 1.1712 - val_accuracy: 0.5827 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1686 - accuracy: 0.5821 - val_loss: 1.1656 - val_accuracy: 0.5811 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1665 - accuracy: 0.5826 - val_loss: 1.1656 - val_accuracy: 0.5769 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1676 - accuracy: 0.5826 - val_loss: 1.1650 - val_accuracy: 0.5812 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1676 - accuracy: 0.5815 - val_loss: 1.1664 - val_accuracy: 0.5823 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1662 - accuracy: 0.5824 - val_loss: 1.1671 - val_accuracy: 0.5759 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1658 - accuracy: 0.5823 - val_loss: 1.1669 - val_accuracy: 0.5826 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1666 - accuracy: 0.5821 - val_loss: 1.1673 - val_accuracy: 0.5827 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1659 - accuracy: 0.5821 - val_loss: 1.1676 - val_accuracy: 0.5825 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1665 - accuracy: 0.5830 - val_loss: 1.1647 - val_accuracy: 0.5809 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1671 - accuracy: 0.5817 - val_loss: 1.1667 - val_accuracy: 0.5826 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1659 - accuracy: 0.5830 - val_loss: 1.1686 - val_accuracy: 0.5829 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1655 - accuracy: 0.5823 - val_loss: 1.1673 - val_accuracy: 0.5829 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1664 - accuracy: 0.5829 - val_loss: 1.1674 - val_accuracy: 0.5829 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1655 - accuracy: 0.5828 - val_loss: 1.1668 - val_accuracy: 0.5829 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1658 - accuracy: 0.5822 - val_loss: 1.1652 - val_accuracy: 0.5823 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1657 - accuracy: 0.5828 - val_loss: 1.1707 - val_accuracy: 0.5805 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1667 - accuracy: 0.5820 - val_loss: 1.1673 - val_accuracy: 0.5829 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1670 - accuracy: 0.5827 - val_loss: 1.1634 - val_accuracy: 0.5812 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1649 - accuracy: 0.5829 - val_loss: 1.1650 - val_accuracy: 0.5820 [-0. -0. -0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 3s 9ms/step - loss: 0.8504 - accuracy: 0.9011 - val_loss: 0.8254 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. 0.16811527 -0.13677959] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8426 - accuracy: 0.9019 - val_loss: 0.8243 - val_accuracy: 0.9053 [ 0. 0. 0. ... -0. 0.17128251 -0.14557184] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8417 - accuracy: 0.9018 - val_loss: 0.8242 - val_accuracy: 0.9058 [ 0. 0. 0. ... -0. 0.17324325 -0.15144806] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8412 - accuracy: 0.9017 - val_loss: 0.8235 - val_accuracy: 0.9056 [ 0. 0. 0. ... 0. 0.17502365 -0.15516572] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8410 - accuracy: 0.9017 - val_loss: 0.8229 - val_accuracy: 0.9056 [ 0. 0. 0. ... 0. 0.17621207 -0.15789835] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8408 - accuracy: 0.9014 - val_loss: 0.8233 - val_accuracy: 0.9058 [ 0. 0. 0. ... 0. 0.1772608 -0.16021816] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9013 - val_loss: 0.8228 - val_accuracy: 0.9055 [ 0. 0. 0. ... 0. 0.17787425 -0.16205059] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8407 - accuracy: 0.9012 - val_loss: 0.8231 - val_accuracy: 0.9053 [ 0. 0. 0. ... 0. 0.17826185 -0.16330725] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9014 - val_loss: 0.8229 - val_accuracy: 0.9058 [ 0. 0. 0. ... 0. 0.1783907 -0.16434571] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9011 - val_loss: 0.8232 - val_accuracy: 0.9057 [ 0. 0. 0. ... 0. 0.17862828 -0.16530217] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8404 - accuracy: 0.9010 - val_loss: 0.8225 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0. 0.17919378 -0.16618681] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8403 - accuracy: 0.9013 - val_loss: 0.8227 - val_accuracy: 0.9057 [ 0. 0. 0. ... 0. 0.1792585 -0.16684856] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0. 0.17921604 -0.1675906 ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8403 - accuracy: 0.9011 - val_loss: 0.8226 - val_accuracy: 0.9056 [ 0. 0. 0. ... -0. 0.17906949 -0.1682019 ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8404 - accuracy: 0.9012 - val_loss: 0.8226 - val_accuracy: 0.9056 [ 0. 0. 0. ... -0. 0.17885174 -0.16876608] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9013 - val_loss: 0.8224 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0. 0.1788688 -0.16930114] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9012 - val_loss: 0.8226 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.17855835 -0.16947967] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9015 - val_loss: 0.8228 - val_accuracy: 0.9066 [ 0. 0. 0. ... 0. 0.17795122 -0.16989419] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8402 - accuracy: 0.9015 - val_loss: 0.8228 - val_accuracy: 0.9065 [ 0. 0. 0. ... 0. 0.17753713 -0.1700926 ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8402 - accuracy: 0.9011 - val_loss: 0.8225 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.17768267 -0.16992763] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9013 - val_loss: 0.8225 - val_accuracy: 0.9063 [ 0. 0. 0. ... 0. 0.17728028 -0.1700343 ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9014 - val_loss: 0.8230 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.17684871 -0.17018503] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8402 - accuracy: 0.9009 - val_loss: 0.8223 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0. 0.17635542 -0.17043464] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9010 - val_loss: 0.8224 - val_accuracy: 0.9063 [ 0. 0. 0. ... 0. 0.17584294 -0.1702512 ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9012 - val_loss: 0.8225 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0.17522226 -0.1700652 ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9010 - val_loss: 0.8233 - val_accuracy: 0.9058 [ 0. 0. 0. ... 0. 0.17510456 -0.17014319] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9012 - val_loss: 0.8227 - val_accuracy: 0.9060 [ 0. 0. 0. ... -0. 0.17467014 -0.16989674] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9010 - val_loss: 0.8226 - val_accuracy: 0.9056 [ 0. 0. 0. ... 0. 0.17442723 -0.16998103] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8225 - val_accuracy: 0.9062 [ 0. 0. 0. ... 0. 0.17421436 -0.16975111] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9011 - val_loss: 0.8230 - val_accuracy: 0.9053 [ 0. 0. 0. ... 0. 0.1738719 -0.16971207] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9011 - val_loss: 0.8224 - val_accuracy: 0.9059 [ 0. 0. 0. ... 0. 0.17350928 -0.16968681] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9055 [ 0. 0. 0. ... 0. 0.17327572 -0.16972728] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9009 - val_loss: 0.8227 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.1732047 -0.16942585] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9014 - val_loss: 0.8225 - val_accuracy: 0.9057 [ 0. 0. 0. ... 0. 0.17304689 -0.16922988] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8401 - accuracy: 0.9011 - val_loss: 0.8223 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.17273594 -0.16896433] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9013 - val_loss: 0.8223 - val_accuracy: 0.9058 [ 0. 0. 0. ... 0. 0.17272128 -0.16926393] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9013 - val_loss: 0.8227 - val_accuracy: 0.9054 [ 0. 0. 0. ... 0. 0.17238018 -0.16912349] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9011 - val_loss: 0.8222 - val_accuracy: 0.9065 [ 0. 0. 0. ... 0. 0.17212783 -0.16881725] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9015 - val_loss: 0.8226 - val_accuracy: 0.9055 [ 0. 0. 0. ... 0. 0.1721536 -0.16896042] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9009 - val_loss: 0.8222 - val_accuracy: 0.9061 [ 0. 0. 0. ... 0. 0.17186978 -0.16895016] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9011 - val_loss: 0.8224 - val_accuracy: 0.9056 [ 0. 0. 0. ... 0. 0.17177871 -0.16888621] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9009 - val_loss: 0.8223 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.17175062 -0.1686623 ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8224 - val_accuracy: 0.9056 [ 0. 0. 0. ... 0. 0.17163137 -0.16859455] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8223 - val_accuracy: 0.9056 [ 0. 0. 0. ... 0. 0.17155059 -0.16872022] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8398 - accuracy: 0.9009 - val_loss: 0.8231 - val_accuracy: 0.9054 [ 0. 0. 0. ... 0. 0.17116362 -0.16858126] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9014 - val_loss: 0.8226 - val_accuracy: 0.9059 [ 0. 0. 0. ... 0. 0.17106862 -0.16847047] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9013 - val_loss: 0.8226 - val_accuracy: 0.9055 [ 0. 0. 0. ... 0. 0.17087427 -0.16864385] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9011 - val_loss: 0.8223 - val_accuracy: 0.9059 [ 0. 0. 0. ... 0. 0.17073846 -0.16894618] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9010 - val_loss: 0.8218 - val_accuracy: 0.9059 [ 0. 0. 0. ... 0. 0.17078204 -0.16859286] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8400 - accuracy: 0.9010 - val_loss: 0.8220 - val_accuracy: 0.9060 [ 0. 0. 0. ... 0. 0.17055723 -0.1686074 ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8637 - accuracy: 0.9010 - val_loss: 0.8411 - val_accuracy: 0.9070 [ 0. 0. 0. ... 0. 0.19908606 -0.15758252] Sparsity at: 0.6458221566523605 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8587 - accuracy: 0.9018 - val_loss: 0.8400 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.198863 -0.1543502] Sparsity at: 0.6458221566523605 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9018 - val_loss: 0.8396 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0. 0.19744878 -0.1529593 ] Sparsity at: 0.6458221566523605 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9020 - val_loss: 0.8395 - val_accuracy: 0.9077 [ 0. 0. 0. ... -0. 0.19603457 -0.15212259] Sparsity at: 0.6458221566523605 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9017 - val_loss: 0.8395 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.19509275 -0.15183629] Sparsity at: 0.6458221566523605 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9017 - val_loss: 0.8392 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.19395871 -0.15147427] Sparsity at: 0.6458221566523605 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9019 - val_loss: 0.8392 - val_accuracy: 0.9075 [ 0. 0. 0. ... 0. 0.19318652 -0.15153106] Sparsity at: 0.6458221566523605 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8391 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.1923742 -0.1517531] Sparsity at: 0.6458221566523605 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9017 - val_loss: 0.8391 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.19181356 -0.15214661] Sparsity at: 0.6458221566523605 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9068 [ 0. 0. 0. ... 0. 0.19142474 -0.15268788] Sparsity at: 0.6458221566523605 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.19076611 -0.15277241] Sparsity at: 0.6458221566523605 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9018 - val_loss: 0.8388 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.19026218 -0.15305339] Sparsity at: 0.6458221566523605 Epoch 63/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8389 - val_accuracy: 0.9078 [ 0. 0. 0. ... -0. 0.19020143 -0.15325448] Sparsity at: 0.6458221566523605 Epoch 64/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9021 - val_loss: 0.8388 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.18973781 -0.15362369] Sparsity at: 0.6458221566523605 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9019 - val_loss: 0.8387 - val_accuracy: 0.9076 [ 0. 0. 0. ... 0. 0.18946482 -0.15363835] Sparsity at: 0.6458221566523605 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9021 - val_loss: 0.8390 - val_accuracy: 0.9068 [ 0. 0. 0. ... 0. 0.18925597 -0.15426892] Sparsity at: 0.6458221566523605 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9018 - val_loss: 0.8389 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.18914074 -0.15444979] Sparsity at: 0.6458221566523605 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8390 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0.18909943 -0.15461834] Sparsity at: 0.6458221566523605 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9015 - val_loss: 0.8390 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.18912435 -0.15472426] Sparsity at: 0.6458221566523605 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8387 - val_accuracy: 0.9080 [ 0. 0. 0. ... -0. 0.18866655 -0.15482514] Sparsity at: 0.6458221566523605 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9016 - val_loss: 0.8390 - val_accuracy: 0.9070 [ 0. 0. 0. ... 0. 0.188647 -0.15503229] Sparsity at: 0.6458221566523605 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9020 - val_loss: 0.8389 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.1883409 -0.15536472] Sparsity at: 0.6458221566523605 Epoch 73/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8389 - val_accuracy: 0.9075 [ 0. 0. 0. ... -0. 0.18809536 -0.15534422] Sparsity at: 0.6458221566523605 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9020 - val_loss: 0.8387 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.18784447 -0.15556799] Sparsity at: 0.6458221566523605 Epoch 75/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8391 - val_accuracy: 0.9077 [ 0. 0. 0. ... -0. 0.1879865 -0.15584293] Sparsity at: 0.6458221566523605 Epoch 76/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8575 - accuracy: 0.9022 - val_loss: 0.8391 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.18773167 -0.15580164] Sparsity at: 0.6458221566523605 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9017 - val_loss: 0.8389 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.18749973 -0.15580127] Sparsity at: 0.6458221566523605 Epoch 78/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8388 - val_accuracy: 0.9076 [ 0. 0. 0. ... 0. 0.1875836 -0.15615831] Sparsity at: 0.6458221566523605 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9022 - val_loss: 0.8390 - val_accuracy: 0.9075 [ 0. 0. 0. ... -0. 0.18765588 -0.15625747] Sparsity at: 0.6458221566523605 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9018 - val_loss: 0.8391 - val_accuracy: 0.9067 [ 0. 0. 0. ... 0. 0.18752357 -0.1563788 ] Sparsity at: 0.6458221566523605 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9019 - val_loss: 0.8388 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.18733993 -0.15630093] Sparsity at: 0.6458221566523605 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9018 - val_loss: 0.8387 - val_accuracy: 0.9077 [ 0. 0. 0. ... 0. 0.18747677 -0.15648025] Sparsity at: 0.6458221566523605 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9020 - val_loss: 0.8388 - val_accuracy: 0.9072 [ 0. 0. 0. ... 0. 0.18736362 -0.15648675] Sparsity at: 0.6458221566523605 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9019 - val_loss: 0.8391 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.18744686 -0.15673442] Sparsity at: 0.6458221566523605 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8386 - val_accuracy: 0.9075 [ 0. 0. 0. ... -0. 0.1875569 -0.15656821] Sparsity at: 0.6458221566523605 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9019 - val_loss: 0.8387 - val_accuracy: 0.9075 [ 0. 0. 0. ... 0. 0.18746947 -0.15665805] Sparsity at: 0.6458221566523605 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9020 - val_loss: 0.8387 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.18707334 -0.15669207] Sparsity at: 0.6458221566523605 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8573 - accuracy: 0.9019 - val_loss: 0.8388 - val_accuracy: 0.9075 [ 0. 0. 0. ... 0. 0.18721037 -0.15701029] Sparsity at: 0.6458221566523605 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9021 - val_loss: 0.8388 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.18700533 -0.15699638] Sparsity at: 0.6458221566523605 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8572 - accuracy: 0.9021 - val_loss: 0.8388 - val_accuracy: 0.9078 [ 0. 0. 0. ... -0. 0.18705624 -0.15692218] Sparsity at: 0.6458221566523605 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.18720259 -0.15708348] Sparsity at: 0.6458221566523605 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9018 - val_loss: 0.8390 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.1871036 -0.15728116] Sparsity at: 0.6458221566523605 Epoch 93/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8573 - accuracy: 0.9020 - val_loss: 0.8390 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.18702503 -0.15715864] Sparsity at: 0.6458221566523605 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9018 - val_loss: 0.8389 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.18698291 -0.15728067] Sparsity at: 0.6458221566523605 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9019 - val_loss: 0.8385 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.18698455 -0.15698901] Sparsity at: 0.6458221566523605 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9016 - val_loss: 0.8390 - val_accuracy: 0.9075 [ 0. 0. 0. ... -0. 0.18697833 -0.15728378] Sparsity at: 0.6458221566523605 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9019 - val_loss: 0.8389 - val_accuracy: 0.9076 [ 0. 0. 0. ... 0. 0.18708514 -0.15736823] Sparsity at: 0.6458221566523605 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9019 - val_loss: 0.8385 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.1871723 -0.1571671] Sparsity at: 0.6458221566523605 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9015 - val_loss: 0.8389 - val_accuracy: 0.9076 [ 0. 0. 0. ... 0. 0.18711779 -0.15732996] Sparsity at: 0.6458221566523605 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8575 - accuracy: 0.9014 - val_loss: 0.8388 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.18694152 -0.15755166] Sparsity at: 0.6458221566523605 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9030 - accuracy: 0.8974 - val_loss: 0.8779 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0.17429511 -0. ] Sparsity at: 0.7593381169527897 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8934 - accuracy: 0.8997 - val_loss: 0.8761 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0.1673576 -0. ] Sparsity at: 0.7593381169527897 Epoch 103/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8922 - accuracy: 0.9000 - val_loss: 0.8749 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0.16486122 -0. ] Sparsity at: 0.7593381169527897 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8916 - accuracy: 0.9001 - val_loss: 0.8744 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0.16406393 -0. ] Sparsity at: 0.7593381169527897 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8912 - accuracy: 0.9002 - val_loss: 0.8741 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.16378316 -0. ] Sparsity at: 0.7593381169527897 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8909 - accuracy: 0.9003 - val_loss: 0.8737 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16357249 -0. ] Sparsity at: 0.7593381169527897 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8907 - accuracy: 0.9003 - val_loss: 0.8731 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.16338661 -0. ] Sparsity at: 0.7593381169527897 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8905 - accuracy: 0.9002 - val_loss: 0.8732 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.1634927 -0. ] Sparsity at: 0.7593381169527897 Epoch 109/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8904 - accuracy: 0.9003 - val_loss: 0.8733 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.16317376 -0. ] Sparsity at: 0.7593381169527897 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8903 - accuracy: 0.9002 - val_loss: 0.8731 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16306138 -0. ] Sparsity at: 0.7593381169527897 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8902 - accuracy: 0.9003 - val_loss: 0.8728 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.1627625 -0. ] Sparsity at: 0.7593381169527897 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8900 - accuracy: 0.9004 - val_loss: 0.8727 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.16274951 -0. ] Sparsity at: 0.7593381169527897 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8901 - accuracy: 0.9000 - val_loss: 0.8726 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.16239916 -0. ] Sparsity at: 0.7593381169527897 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9001 - val_loss: 0.8725 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.16219334 -0. ] Sparsity at: 0.7593381169527897 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9004 - val_loss: 0.8723 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16218741 -0. ] Sparsity at: 0.7593381169527897 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9002 - val_loss: 0.8724 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16198026 -0. ] Sparsity at: 0.7593381169527897 Epoch 117/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9001 - val_loss: 0.8726 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16187611 -0. ] Sparsity at: 0.7593381169527897 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8898 - accuracy: 0.9003 - val_loss: 0.8723 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16165973 -0. ] Sparsity at: 0.7593381169527897 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.9003 - val_loss: 0.8725 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.16152291 -0. ] Sparsity at: 0.7593381169527897 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8899 - accuracy: 0.8999 - val_loss: 0.8724 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16130815 -0. ] Sparsity at: 0.7593381169527897 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9003 - val_loss: 0.8722 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.16128692 -0. ] Sparsity at: 0.7593381169527897 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8898 - accuracy: 0.9000 - val_loss: 0.8724 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0.1612902 -0. ] Sparsity at: 0.7593381169527897 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9001 - val_loss: 0.8722 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0.1610719 -0. ] Sparsity at: 0.7593381169527897 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16088878 -0. ] Sparsity at: 0.7593381169527897 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8722 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.160795 -0. ] Sparsity at: 0.7593381169527897 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.16080746 -0. ] Sparsity at: 0.7593381169527897 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8723 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.1607249 -0. ] Sparsity at: 0.7593381169527897 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8898 - accuracy: 0.9000 - val_loss: 0.8723 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.1606852 -0. ] Sparsity at: 0.7593381169527897 Epoch 129/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.8999 - val_loss: 0.8722 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16052708 -0. ] Sparsity at: 0.7593381169527897 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16039129 -0. ] Sparsity at: 0.7593381169527897 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.8999 - val_loss: 0.8720 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0.1604776 -0. ] Sparsity at: 0.7593381169527897 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8722 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.16041921 -0. ] Sparsity at: 0.7593381169527897 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16027421 -0. ] Sparsity at: 0.7593381169527897 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9002 - val_loss: 0.8722 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.1602242 -0. ] Sparsity at: 0.7593381169527897 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0.16034126 -0. ] Sparsity at: 0.7593381169527897 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9002 - val_loss: 0.8718 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0.16016617 -0. ] Sparsity at: 0.7593381169527897 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8721 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16017987 -0. ] Sparsity at: 0.7593381169527897 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.8999 - val_loss: 0.8722 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0.16022566 -0. ] Sparsity at: 0.7593381169527897 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8720 - val_accuracy: 0.9075 [ 0. 0. 0. ... -0. 0.16022818 -0. ] Sparsity at: 0.7593381169527897 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8722 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16040853 -0. ] Sparsity at: 0.7593381169527897 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9002 - val_loss: 0.8720 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16042857 -0. ] Sparsity at: 0.7593381169527897 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16041853 -0. ] Sparsity at: 0.7593381169527897 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.8999 - val_loss: 0.8720 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16039829 -0. ] Sparsity at: 0.7593381169527897 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9001 - val_loss: 0.8721 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16032799 -0. ] Sparsity at: 0.7593381169527897 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9000 - val_loss: 0.8718 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0.16039006 -0. ] Sparsity at: 0.7593381169527897 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8895 - accuracy: 0.9001 - val_loss: 0.8718 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0.16030921 -0. ] Sparsity at: 0.7593381169527897 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.8998 - val_loss: 0.8718 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0.16018404 -0. ] Sparsity at: 0.7593381169527897 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8897 - accuracy: 0.9002 - val_loss: 0.8719 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16011456 -0. ] Sparsity at: 0.7593381169527897 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8895 - accuracy: 0.9001 - val_loss: 0.8718 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0.16032843 -0. ] Sparsity at: 0.7593381169527897 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8896 - accuracy: 0.9000 - val_loss: 0.8720 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0.16025607 -0. ] Sparsity at: 0.7593381169527897 Epoch 151/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9816 - accuracy: 0.8928 - val_loss: 0.9493 - val_accuracy: 0.9004 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 152/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9653 - accuracy: 0.8969 - val_loss: 0.9464 - val_accuracy: 0.9014 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9634 - accuracy: 0.8972 - val_loss: 0.9454 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9625 - accuracy: 0.8973 - val_loss: 0.9445 - val_accuracy: 0.9015 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9618 - accuracy: 0.8974 - val_loss: 0.9441 - val_accuracy: 0.9015 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.8975 - val_loss: 0.9435 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9609 - accuracy: 0.8975 - val_loss: 0.9432 - val_accuracy: 0.9014 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9606 - accuracy: 0.8978 - val_loss: 0.9430 - val_accuracy: 0.9015 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9604 - accuracy: 0.8978 - val_loss: 0.9429 - val_accuracy: 0.9013 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9601 - accuracy: 0.8981 - val_loss: 0.9428 - val_accuracy: 0.9016 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9600 - accuracy: 0.8979 - val_loss: 0.9425 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9599 - accuracy: 0.8979 - val_loss: 0.9425 - val_accuracy: 0.9016 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9597 - accuracy: 0.8979 - val_loss: 0.9423 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9596 - accuracy: 0.8980 - val_loss: 0.9422 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9595 - accuracy: 0.8979 - val_loss: 0.9422 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9595 - accuracy: 0.8979 - val_loss: 0.9421 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9594 - accuracy: 0.8981 - val_loss: 0.9421 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9594 - accuracy: 0.8979 - val_loss: 0.9421 - val_accuracy: 0.9012 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9594 - accuracy: 0.8980 - val_loss: 0.9421 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9594 - accuracy: 0.8980 - val_loss: 0.9420 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8980 - val_loss: 0.9419 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 172/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8981 - val_loss: 0.9420 - val_accuracy: 0.9015 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9418 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8979 - val_loss: 0.9421 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9418 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9014 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8980 - val_loss: 0.9419 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8978 - val_loss: 0.9419 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9592 - accuracy: 0.8980 - val_loss: 0.9419 - val_accuracy: 0.9015 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8981 - val_loss: 0.9419 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8981 - val_loss: 0.9419 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9418 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 186/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8981 - val_loss: 0.9420 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8980 - val_loss: 0.9418 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8980 - val_loss: 0.9418 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8978 - val_loss: 0.9416 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8981 - val_loss: 0.9417 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8981 - val_loss: 0.9418 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9419 - val_accuracy: 0.9017 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 195/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8980 - val_loss: 0.9417 - val_accuracy: 0.9018 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9589 - accuracy: 0.8978 - val_loss: 0.9418 - val_accuracy: 0.9019 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8978 - val_loss: 0.9417 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9590 - accuracy: 0.8979 - val_loss: 0.9417 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9591 - accuracy: 0.8980 - val_loss: 0.9417 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8447894313304721 Epoch 201/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1061 - accuracy: 0.8662 - val_loss: 1.0505 - val_accuracy: 0.8924 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 202/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0626 - accuracy: 0.8906 - val_loss: 1.0416 - val_accuracy: 0.8962 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0576 - accuracy: 0.8922 - val_loss: 1.0388 - val_accuracy: 0.8972 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0556 - accuracy: 0.8931 - val_loss: 1.0370 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0543 - accuracy: 0.8935 - val_loss: 1.0359 - val_accuracy: 0.8987 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0535 - accuracy: 0.8937 - val_loss: 1.0353 - val_accuracy: 0.8988 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0530 - accuracy: 0.8937 - val_loss: 1.0348 - val_accuracy: 0.8992 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0525 - accuracy: 0.8936 - val_loss: 1.0343 - val_accuracy: 0.8992 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0522 - accuracy: 0.8939 - val_loss: 1.0341 - val_accuracy: 0.8996 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0520 - accuracy: 0.8939 - val_loss: 1.0338 - val_accuracy: 0.8993 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0517 - accuracy: 0.8941 - val_loss: 1.0337 - val_accuracy: 0.8996 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0516 - accuracy: 0.8941 - val_loss: 1.0335 - val_accuracy: 0.8994 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0514 - accuracy: 0.8938 - val_loss: 1.0332 - val_accuracy: 0.8998 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0512 - accuracy: 0.8938 - val_loss: 1.0331 - val_accuracy: 0.8996 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0510 - accuracy: 0.8941 - val_loss: 1.0326 - val_accuracy: 0.8995 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0507 - accuracy: 0.8943 - val_loss: 1.0322 - val_accuracy: 0.8996 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0504 - accuracy: 0.8942 - val_loss: 1.0320 - val_accuracy: 0.9002 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0502 - accuracy: 0.8945 - val_loss: 1.0318 - val_accuracy: 0.9000 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0500 - accuracy: 0.8943 - val_loss: 1.0315 - val_accuracy: 0.9004 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0499 - accuracy: 0.8943 - val_loss: 1.0316 - val_accuracy: 0.8999 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0498 - accuracy: 0.8942 - val_loss: 1.0315 - val_accuracy: 0.8998 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8940 - val_loss: 1.0313 - val_accuracy: 0.8998 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8942 - val_loss: 1.0314 - val_accuracy: 0.8997 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8937 - val_loss: 1.0311 - val_accuracy: 0.9000 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8939 - val_loss: 1.0311 - val_accuracy: 0.8997 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8935 - val_loss: 1.0312 - val_accuracy: 0.8991 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8936 - val_loss: 1.0311 - val_accuracy: 0.8994 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0493 - accuracy: 0.8934 - val_loss: 1.0309 - val_accuracy: 0.8993 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0492 - accuracy: 0.8935 - val_loss: 1.0310 - val_accuracy: 0.8987 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0492 - accuracy: 0.8933 - val_loss: 1.0309 - val_accuracy: 0.8983 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0491 - accuracy: 0.8932 - val_loss: 1.0307 - val_accuracy: 0.8986 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0489 - accuracy: 0.8929 - val_loss: 1.0306 - val_accuracy: 0.8988 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0488 - accuracy: 0.8932 - val_loss: 1.0304 - val_accuracy: 0.8989 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0488 - accuracy: 0.8930 - val_loss: 1.0303 - val_accuracy: 0.8984 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0487 - accuracy: 0.8928 - val_loss: 1.0301 - val_accuracy: 0.8987 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0487 - accuracy: 0.8931 - val_loss: 1.0302 - val_accuracy: 0.8985 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0486 - accuracy: 0.8930 - val_loss: 1.0300 - val_accuracy: 0.8987 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0485 - accuracy: 0.8932 - val_loss: 1.0297 - val_accuracy: 0.8990 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0484 - accuracy: 0.8932 - val_loss: 1.0294 - val_accuracy: 0.8985 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0481 - accuracy: 0.8933 - val_loss: 1.0291 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0477 - accuracy: 0.8932 - val_loss: 1.0287 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0474 - accuracy: 0.8932 - val_loss: 1.0285 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 243/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0472 - accuracy: 0.8934 - val_loss: 1.0284 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0470 - accuracy: 0.8935 - val_loss: 1.0284 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 245/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0470 - accuracy: 0.8934 - val_loss: 1.0282 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0469 - accuracy: 0.8938 - val_loss: 1.0282 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0469 - accuracy: 0.8936 - val_loss: 1.0282 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0469 - accuracy: 0.8938 - val_loss: 1.0282 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 249/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0468 - accuracy: 0.8935 - val_loss: 1.0282 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0468 - accuracy: 0.8936 - val_loss: 1.0281 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 251/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1488 - accuracy: 0.8790 - val_loss: 1.1053 - val_accuracy: 0.8929 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1236 - accuracy: 0.8870 - val_loss: 1.1032 - val_accuracy: 0.8933 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 253/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1225 - accuracy: 0.8874 - val_loss: 1.1027 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1220 - accuracy: 0.8876 - val_loss: 1.1025 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1218 - accuracy: 0.8878 - val_loss: 1.1021 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1216 - accuracy: 0.8877 - val_loss: 1.1020 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1214 - accuracy: 0.8878 - val_loss: 1.1020 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1213 - accuracy: 0.8878 - val_loss: 1.1019 - val_accuracy: 0.8942 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1212 - accuracy: 0.8878 - val_loss: 1.1019 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1212 - accuracy: 0.8878 - val_loss: 1.1018 - val_accuracy: 0.8942 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8878 - val_loss: 1.1018 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8943 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 264/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8942 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 265/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1211 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 267/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1210 - accuracy: 0.8879 - val_loss: 1.1017 - val_accuracy: 0.8937 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8937 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8877 - val_loss: 1.1017 - val_accuracy: 0.8942 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 273/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 276/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8876 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9468884120171673 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8880 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 282/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8880 - val_loss: 1.1017 - val_accuracy: 0.8937 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1015 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8942 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 290/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1210 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8877 - val_loss: 1.1016 - val_accuracy: 0.8941 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1015 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 295/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8879 - val_loss: 1.1016 - val_accuracy: 0.8939 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8880 - val_loss: 1.1016 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8880 - val_loss: 1.1016 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1017 - val_accuracy: 0.8938 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9468884120171673 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1209 - accuracy: 0.8878 - val_loss: 1.1016 - val_accuracy: 0.8940 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9468884120171673 Epoch 301/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3442 - accuracy: 0.8374 - val_loss: 1.2899 - val_accuracy: 0.8602 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 302/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2947 - accuracy: 0.8574 - val_loss: 1.2787 - val_accuracy: 0.8628 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2883 - accuracy: 0.8578 - val_loss: 1.2734 - val_accuracy: 0.8631 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2842 - accuracy: 0.8575 - val_loss: 1.2690 - val_accuracy: 0.8617 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 305/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2810 - accuracy: 0.8578 - val_loss: 1.2664 - val_accuracy: 0.8625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2792 - accuracy: 0.8582 - val_loss: 1.2651 - val_accuracy: 0.8619 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 307/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2783 - accuracy: 0.8580 - val_loss: 1.2645 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2779 - accuracy: 0.8579 - val_loss: 1.2642 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2776 - accuracy: 0.8580 - val_loss: 1.2640 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2774 - accuracy: 0.8578 - val_loss: 1.2639 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 311/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2773 - accuracy: 0.8577 - val_loss: 1.2638 - val_accuracy: 0.8626 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 312/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2772 - accuracy: 0.8578 - val_loss: 1.2638 - val_accuracy: 0.8625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 313/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2771 - accuracy: 0.8576 - val_loss: 1.2637 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2771 - accuracy: 0.8575 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 315/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8575 - val_loss: 1.2637 - val_accuracy: 0.8618 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8575 - val_loss: 1.2637 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 317/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 318/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2770 - accuracy: 0.8575 - val_loss: 1.2636 - val_accuracy: 0.8620 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 320/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 321/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 322/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 323/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8570 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 325/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8620 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8625 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 330/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8621 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8621 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8621 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2635 - val_accuracy: 0.8620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8619 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 341/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8573 - val_loss: 1.2636 - val_accuracy: 0.8624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8621 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8622 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8571 - val_loss: 1.2636 - val_accuracy: 0.8620 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716671137339056 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8570 - val_loss: 1.2636 - val_accuracy: 0.8623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2769 - accuracy: 0.8572 - val_loss: 1.2636 - val_accuracy: 0.8620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716671137339056 Epoch 351/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6439 - accuracy: 0.6389 - val_loss: 1.5786 - val_accuracy: 0.6641 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5816 - accuracy: 0.6601 - val_loss: 1.5614 - val_accuracy: 0.6657 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 353/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5727 - accuracy: 0.6612 - val_loss: 1.5563 - val_accuracy: 0.6646 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5698 - accuracy: 0.6605 - val_loss: 1.5542 - val_accuracy: 0.6637 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5686 - accuracy: 0.6596 - val_loss: 1.5532 - val_accuracy: 0.6616 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 356/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5680 - accuracy: 0.6560 - val_loss: 1.5527 - val_accuracy: 0.6610 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5677 - accuracy: 0.6559 - val_loss: 1.5523 - val_accuracy: 0.6608 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5675 - accuracy: 0.6562 - val_loss: 1.5521 - val_accuracy: 0.6609 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5674 - accuracy: 0.6559 - val_loss: 1.5519 - val_accuracy: 0.6611 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5673 - accuracy: 0.6558 - val_loss: 1.5518 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5672 - accuracy: 0.6556 - val_loss: 1.5517 - val_accuracy: 0.6611 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 362/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5672 - accuracy: 0.6557 - val_loss: 1.5516 - val_accuracy: 0.6615 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5671 - accuracy: 0.6558 - val_loss: 1.5515 - val_accuracy: 0.6611 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5671 - accuracy: 0.6557 - val_loss: 1.5515 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5671 - accuracy: 0.6557 - val_loss: 1.5515 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6558 - val_loss: 1.5514 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6556 - val_loss: 1.5514 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6558 - val_loss: 1.5514 - val_accuracy: 0.6615 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6616 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6617 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6560 - val_loss: 1.5513 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 378/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6559 - val_loss: 1.5513 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6560 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6611 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6560 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5513 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6556 - val_loss: 1.5513 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6615 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 385/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5669 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9845761802575107 Epoch 396/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6557 - val_loss: 1.5512 - val_accuracy: 0.6612 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6558 - val_loss: 1.5512 - val_accuracy: 0.6616 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5668 - accuracy: 0.6556 - val_loss: 1.5512 - val_accuracy: 0.6614 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9845761802575107 Epoch 401/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7968 - accuracy: 0.5550 - val_loss: 1.7333 - val_accuracy: 0.5746 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7445 - accuracy: 0.5697 - val_loss: 1.7187 - val_accuracy: 0.5784 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7377 - accuracy: 0.5708 - val_loss: 1.7153 - val_accuracy: 0.5788 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7359 - accuracy: 0.5706 - val_loss: 1.7141 - val_accuracy: 0.5783 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7353 - accuracy: 0.5706 - val_loss: 1.7134 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7349 - accuracy: 0.5707 - val_loss: 1.7129 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7346 - accuracy: 0.5709 - val_loss: 1.7127 - val_accuracy: 0.5781 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7345 - accuracy: 0.5711 - val_loss: 1.7125 - val_accuracy: 0.5784 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7344 - accuracy: 0.5712 - val_loss: 1.7123 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7343 - accuracy: 0.5714 - val_loss: 1.7121 - val_accuracy: 0.5775 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7342 - accuracy: 0.5714 - val_loss: 1.7120 - val_accuracy: 0.5774 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7341 - accuracy: 0.5714 - val_loss: 1.7120 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7341 - accuracy: 0.5716 - val_loss: 1.7119 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7340 - accuracy: 0.5717 - val_loss: 1.7119 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7340 - accuracy: 0.5717 - val_loss: 1.7118 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7340 - accuracy: 0.5718 - val_loss: 1.7118 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7117 - val_accuracy: 0.5783 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7117 - val_accuracy: 0.5783 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7117 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5717 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5717 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 10ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5777 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5717 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7115 - val_accuracy: 0.5780 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7115 - val_accuracy: 0.5779 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5782 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5777 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5783 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5783 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7339 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5721 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7115 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7115 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5780 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5718 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5719 - val_loss: 1.7116 - val_accuracy: 0.5778 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7338 - accuracy: 0.5720 - val_loss: 1.7116 - val_accuracy: 0.5781 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 1/500 235/235 [==============================] - 4s 9ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.2682 - val_accuracy: 0.9702 [-0. -0. -0. ... 0. 0.588045 -1.0087581] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 8.4622e-04 - accuracy: 0.9997 - val_loss: 0.2677 - val_accuracy: 0.9710 [-0. -0. -0. ... -0. 0.58606076 -1.0111476 ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.2603 - val_accuracy: 0.9721 [-0. -0. -0. ... -0. 0.5863574 -1.004833 ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7883e-04 - accuracy: 0.9999 - val_loss: 0.2586 - val_accuracy: 0.9724 [-0. -0. -0. ... 0. 0.58543134 -1.0041473 ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 7.9474e-05 - accuracy: 1.0000 - val_loss: 0.2578 - val_accuracy: 0.9728 [-0. -0. -0. ... -0. 0.58749086 -1.0070748 ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8397e-05 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9731 [-0. -0. -0. ... -0. 0.5875895 -1.0069286] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3631e-05 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9729 [-0. -0. -0. ... -0. 0.5876826 -1.0070059] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1671e-05 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9731 [-0. -0. -0. ... -0. 0.58773714 -1.0070993 ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0310e-05 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9729 [-0. -0. -0. ... -0. 0.58779305 -1.0072013 ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 9.2633e-06 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9729 [-0. -0. -0. ... -0. 0.5878583 -1.0073192] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 8.4163e-06 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9730 [-0. -0. -0. ... -0. 0.5879386 -1.0074524] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7079e-06 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.58802783 -1.0076001 ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0999e-06 - accuracy: 1.0000 - val_loss: 0.2569 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.58813417 -1.0077622 ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5709e-06 - accuracy: 1.0000 - val_loss: 0.2570 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.5882569 -1.0079422] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 6.0996e-06 - accuracy: 1.0000 - val_loss: 0.2571 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.5883937 -1.0081402] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6795e-06 - accuracy: 1.0000 - val_loss: 0.2572 - val_accuracy: 0.9728 [-0. -0. -0. ... -0. 0.58855295 -1.0083567 ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 5.2962e-06 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.5887338 -1.0085902] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9466e-06 - accuracy: 1.0000 - val_loss: 0.2574 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.58893025 -1.0088445 ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6260e-06 - accuracy: 1.0000 - val_loss: 0.2575 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.5891489 -1.0091203] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3279e-06 - accuracy: 1.0000 - val_loss: 0.2576 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. 0.5893861 -1.0094138] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0523e-06 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.58964604 -1.0097291 ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7957e-06 - accuracy: 1.0000 - val_loss: 0.2578 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. 0.58992475 -1.0100688 ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5538e-06 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.59022635 -1.0104278 ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3285e-06 - accuracy: 1.0000 - val_loss: 0.2581 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.5905556 -1.0108074] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1177e-06 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.5909022 -1.0112115] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9191e-06 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.5912768 -1.0116395] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7320e-06 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.59167427 -1.0120896 ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5565e-06 - accuracy: 1.0000 - val_loss: 0.2588 - val_accuracy: 0.9733 [-0. -0. -0. ... 0. 0.59210324 -1.0125631 ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3911e-06 - accuracy: 1.0000 - val_loss: 0.2590 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.59255624 -1.0130609 ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2350e-06 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.5930469 -1.01359 ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0887e-06 - accuracy: 1.0000 - val_loss: 0.2594 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.5935675 -1.0141453] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9506e-06 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.5941217 -1.014727 ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8209e-06 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9733 [-0. -0. -0. ... 0. 0.5947151 -1.0153388] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6986e-06 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9733 [-0. -0. -0. ... 0. 0.5953617 -1.0159765] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5839e-06 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.59604454 -1.0166512 ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4758e-06 - accuracy: 1.0000 - val_loss: 0.2608 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.5967779 -1.0173626] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3743e-06 - accuracy: 1.0000 - val_loss: 0.2612 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.59756666 -1.0181156 ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2791e-06 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.5984132 -1.0189025] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1894e-06 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.59931725 -1.0197209 ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1054e-06 - accuracy: 1.0000 - val_loss: 0.2624 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.60028 -1.0206004] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0269e-06 - accuracy: 1.0000 - val_loss: 0.2628 - val_accuracy: 0.9733 [-0. -0. -0. ... 0. 0.6013041 -1.0215133] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5300e-07 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.6023893 -1.0224849] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 8.8409e-07 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.6035547 -1.0234933] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 8.1921e-07 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. 0.60478413 -1.0245447 ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5908e-07 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.6060863 -1.0256447] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0278e-07 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.6074693 -1.0267905] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5020e-07 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.60895497 -1.0279917 ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 6.0088e-07 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.6104907 -1.0292248] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5500e-07 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.61209035 -1.0305238 ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1238e-07 - accuracy: 1.0000 - val_loss: 0.2679 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. 0.6137657 -1.0318636] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0218 - accuracy: 0.9934 - val_loss: 0.2384 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. -1.0177894] Sparsity at: 0.6458724517167382 Epoch 52/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.2237 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.0254325] Sparsity at: 0.6458724517167382 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3563e-04 - accuracy: 0.9999 - val_loss: 0.2196 - val_accuracy: 0.9714 [-0. -0. -0. ... 0. -0. -1.0293853] Sparsity at: 0.6458724517167382 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 3.1608e-04 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9714 [-0. -0. -0. ... 0. -0. -1.0345147] Sparsity at: 0.6458724517167382 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4074e-04 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9718 [-0. -0. -0. ... 0. -0. -1.0372287] Sparsity at: 0.6458724517167382 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0348e-04 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9720 [-0. -0. -0. ... 0. -0. -1.0399623] Sparsity at: 0.6458724517167382 Epoch 57/500 235/235 [==============================] - 2s 10ms/step - loss: 1.7805e-04 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9722 [-0. -0. -0. ... 0. -0. -1.0427305] Sparsity at: 0.6458724517167382 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5843e-04 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9721 [-0. -0. -0. ... 0. -0. -1.045603] Sparsity at: 0.6458724517167382 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4256e-04 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9722 [-0. -0. -0. ... 0. -0. -1.0485637] Sparsity at: 0.6458724517167382 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2919e-04 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9725 [-0. -0. -0. ... 0. -0. -1.0516682] Sparsity at: 0.6458724517167382 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1759e-04 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9725 [-0. -0. -0. ... 0. -0. -1.0549709] Sparsity at: 0.6458724517167382 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0744e-04 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9724 [-0. -0. -0. ... 0. -0. -1.0584064] Sparsity at: 0.6458724517167382 Epoch 63/500 235/235 [==============================] - 2s 9ms/step - loss: 9.8410e-05 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.0620241] Sparsity at: 0.6458724517167382 Epoch 64/500 235/235 [==============================] - 2s 9ms/step - loss: 9.0344e-05 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9727 [-0. -0. -0. ... 0. -0. -1.0658306] Sparsity at: 0.6458724517167382 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3001e-05 - accuracy: 1.0000 - val_loss: 0.2190 - val_accuracy: 0.9724 [-0. -0. -0. ... 0. -0. -1.0698181] Sparsity at: 0.6458724517167382 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 7.6394e-05 - accuracy: 1.0000 - val_loss: 0.2193 - val_accuracy: 0.9727 [-0. -0. -0. ... 0. -0. -1.073993] Sparsity at: 0.6458724517167382 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0280e-05 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. -0. -1.0783528] Sparsity at: 0.6458724517167382 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4717e-05 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. -0. -1.0828927] Sparsity at: 0.6458724517167382 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 5.9601e-05 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. -0. -1.0876178] Sparsity at: 0.6458724517167382 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4882e-05 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9729 [-0. -0. -0. ... 0. -0. -1.0925248] Sparsity at: 0.6458724517167382 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0519e-05 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. -0. -1.0975989] Sparsity at: 0.6458724517167382 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6483e-05 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. -0. -1.1028473] Sparsity at: 0.6458724517167382 Epoch 73/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2740e-05 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9727 [-0. -0. -0. ... 0. -0. -1.1082634] Sparsity at: 0.6458724517167382 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9286e-05 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. -0. -1.1138316] Sparsity at: 0.6458724517167382 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6089e-05 - accuracy: 1.0000 - val_loss: 0.2234 - val_accuracy: 0.9729 [-0. -0. -0. ... 0. -0. -1.1195369] Sparsity at: 0.6458724517167382 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3102e-05 - accuracy: 1.0000 - val_loss: 0.2240 - val_accuracy: 0.9728 [-0. -0. -0. ... 0. -0. -1.1253556] Sparsity at: 0.6458724517167382 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0370e-05 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.1313614] Sparsity at: 0.6458724517167382 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7813e-05 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.1376103] Sparsity at: 0.6458724517167382 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5477e-05 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.1439307] Sparsity at: 0.6458724517167382 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3296e-05 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9725 [-0. -0. -0. ... 0. -0. -1.1504414] Sparsity at: 0.6458724517167382 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1289e-05 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9724 [-0. -0. -0. ... 0. -0. -1.1570404] Sparsity at: 0.6458724517167382 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9422e-05 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9724 [-0. -0. -0. ... 0. -0. -1.1638609] Sparsity at: 0.6458724517167382 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7727e-05 - accuracy: 1.0000 - val_loss: 0.2297 - val_accuracy: 0.9725 [-0. -0. -0. ... 0. -0. -1.1708266] Sparsity at: 0.6458724517167382 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6154e-05 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.1779188] Sparsity at: 0.6458724517167382 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4705e-05 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.1851437] Sparsity at: 0.6458724517167382 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3383e-05 - accuracy: 1.0000 - val_loss: 0.2329 - val_accuracy: 0.9726 [-0. -0. -0. ... 0. -0. -1.1925136] Sparsity at: 0.6458724517167382 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2163e-05 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9727 [-0. -0. -0. ... 0. -0. -1.2000003] Sparsity at: 0.6458724517167382 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1048e-05 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9729 [-0. -0. -0. ... 0. -0. -1.207626] Sparsity at: 0.6458724517167382 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0026e-05 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9729 [-0. -0. -0. ... 0. -0. -1.2153797] Sparsity at: 0.6458724517167382 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 9.1006e-06 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. -0. -1.22327] Sparsity at: 0.6458724517167382 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 8.2382e-06 - accuracy: 1.0000 - val_loss: 0.2388 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. -0. -1.2312164] Sparsity at: 0.6458724517167382 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4625e-06 - accuracy: 1.0000 - val_loss: 0.2401 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. -0. -1.239322] Sparsity at: 0.6458724517167382 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7547e-06 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. -0. -1.2475187] Sparsity at: 0.6458724517167382 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1086e-06 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. -0. -1.25583] Sparsity at: 0.6458724517167382 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5207e-06 - accuracy: 1.0000 - val_loss: 0.2443 - val_accuracy: 0.9731 [-0. -0. -0. ... 0. -0. -1.2643262] Sparsity at: 0.6458724517167382 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9827e-06 - accuracy: 1.0000 - val_loss: 0.2458 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. -0. -1.2729176] Sparsity at: 0.6458724517167382 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4983e-06 - accuracy: 1.0000 - val_loss: 0.2472 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. -0. -1.2815895] Sparsity at: 0.6458724517167382 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0591e-06 - accuracy: 1.0000 - val_loss: 0.2487 - val_accuracy: 0.9732 [-0. -0. -0. ... 0. -0. -1.2902172] Sparsity at: 0.6458724517167382 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6569e-06 - accuracy: 1.0000 - val_loss: 0.2502 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. -0. -1.2989717] Sparsity at: 0.6458724517167382 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2964e-06 - accuracy: 1.0000 - val_loss: 0.2517 - val_accuracy: 0.9730 [-0. -0. -0. ... 0. -0. -1.3079267] Sparsity at: 0.6458724517167382 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0437 - accuracy: 0.9874 - val_loss: 0.1909 - val_accuracy: 0.9685 [-0. -0. -0. ... 0. -0. -1.3727199] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.1891 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. -0. -1.3784107] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 0.1869 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. -1.3845935] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9992 - val_loss: 0.1845 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. -0. -1.3902153] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0032 - accuracy: 0.9997 - val_loss: 0.1837 - val_accuracy: 0.9700 [-0. -0. -0. ... 0. -0. -1.3957659] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9999 - val_loss: 0.1831 - val_accuracy: 0.9706 [-0. -0. -0. ... 0. -0. -1.4009901] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 0.9999 - val_loss: 0.1830 - val_accuracy: 0.9709 [-0. -0. -0. ... 0. -0. -1.4061068] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9707 [-0. -0. -0. ... 0. -0. -1.411626] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1837 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.4176506] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.4237087] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1849 - val_accuracy: 0.9715 [-0. -0. -0. ... 0. -0. -1.4301274] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1856 - val_accuracy: 0.9714 [-0. -0. -0. ... 0. -0. -1.4365122] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 8.9607e-04 - accuracy: 1.0000 - val_loss: 0.1864 - val_accuracy: 0.9716 [-0. -0. -0. ... 0. -0. -1.4432533] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 8.0414e-04 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9717 [-0. -0. -0. ... 0. -0. -1.4501265] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 7.2333e-04 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.4571528] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5286e-04 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9711 [-0. -0. -0. ... 0. -0. -1.4645787] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 9ms/step - loss: 5.9129e-04 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9711 [-0. -0. -0. ... 0. -0. -1.4722421] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3535e-04 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.4799819] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 4.8516e-04 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9715 [-0. -0. -0. ... 0. -0. -1.48814] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4021e-04 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.4964588] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0022e-04 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9714 [-0. -0. -0. ... 0. -0. -1.5050299] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6395e-04 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9715 [-0. -0. -0. ... 0. -0. -1.5138777] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3128e-04 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9714 [-0. -0. -0. ... 0. -0. -1.5228974] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0125e-04 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9711 [-0. -0. -0. ... 0. -0. -1.5323297] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7364e-04 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.5417112] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4883e-04 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.5515243] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2657e-04 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.561527] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0609e-04 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.5719991] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8699e-04 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9709 [-0. -0. -0. ... 0. -0. -1.5824245] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7025e-04 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.5932593] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5413e-04 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.6041205] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3987e-04 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.615292] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2686e-04 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.6266656] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1521e-04 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.6383166] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0424e-04 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.650125] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 9.4507e-05 - accuracy: 1.0000 - val_loss: 0.2169 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.6617621] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5613e-05 - accuracy: 1.0000 - val_loss: 0.2187 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.6738933] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 7.7297e-05 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.6862351] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 6.9997e-05 - accuracy: 1.0000 - val_loss: 0.2223 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.6985171] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3077e-05 - accuracy: 1.0000 - val_loss: 0.2242 - val_accuracy: 0.9710 [-0. -0. -0. ... 0. -0. -1.7110292] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6960e-05 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9715 [-0. -0. -0. ... 0. -0. -1.7236226] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1429e-05 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9714 [-0. -0. -0. ... 0. -0. -1.736623] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6352e-05 - accuracy: 1.0000 - val_loss: 0.2300 - val_accuracy: 0.9713 [-0. -0. -0. ... 0. -0. -1.7494847] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1827e-05 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9712 [-0. -0. -0. ... 0. -0. -1.7626666] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7765e-05 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9716 [-0. -0. -0. ... 0. -0. -1.7755849] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3946e-05 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9716 [-0. -0. -0. ... 0. -0. -1.7887508] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0543e-05 - accuracy: 1.0000 - val_loss: 0.2378 - val_accuracy: 0.9716 [-0. -0. -0. ... 0. -0. -1.8019708] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7562e-05 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9716 [-0. -0. -0. ... 0. -0. -1.8155415] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4806e-05 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9718 [-0. -0. -0. ... 0. -0. -1.8289564] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2303e-05 - accuracy: 1.0000 - val_loss: 0.2438 - val_accuracy: 0.9718 [-0. -0. -0. ... 0. -0. -1.8424696] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1061 - accuracy: 0.9722 - val_loss: 0.1920 - val_accuracy: 0.9637 [-0. -0. -0. ... -0. -0. -1.7898865] Sparsity at: 0.8448229613733905 Epoch 152/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0401 - accuracy: 0.9864 - val_loss: 0.1795 - val_accuracy: 0.9664 [-0. -0. -0. ... 0. -0. -1.7822611] Sparsity at: 0.8448229613733905 Epoch 153/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0303 - accuracy: 0.9897 - val_loss: 0.1740 - val_accuracy: 0.9672 [-0. -0. -0. ... 0. -0. -1.781373] Sparsity at: 0.8448229613733905 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0248 - accuracy: 0.9916 - val_loss: 0.1711 - val_accuracy: 0.9678 [-0. -0. -0. ... 0. -0. -1.7810035] Sparsity at: 0.8448229613733905 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0211 - accuracy: 0.9933 - val_loss: 0.1695 - val_accuracy: 0.9683 [-0. -0. -0. ... 0. -0. -1.7809491] Sparsity at: 0.8448229613733905 Epoch 156/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0185 - accuracy: 0.9946 - val_loss: 0.1682 - val_accuracy: 0.9682 [-0. -0. -0. ... 0. -0. -1.7818695] Sparsity at: 0.8448229613733905 Epoch 157/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0164 - accuracy: 0.9953 - val_loss: 0.1675 - val_accuracy: 0.9689 [-0. -0. -0. ... 0. -0. -1.7831535] Sparsity at: 0.8448229613733905 Epoch 158/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0148 - accuracy: 0.9959 - val_loss: 0.1670 - val_accuracy: 0.9682 [-0. -0. -0. ... 0. -0. -1.7854861] Sparsity at: 0.8448229613733905 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.1669 - val_accuracy: 0.9683 [-0. -0. -0. ... 0. -0. -1.7882736] Sparsity at: 0.8448229613733905 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0122 - accuracy: 0.9969 - val_loss: 0.1669 - val_accuracy: 0.9689 [-0. -0. -0. ... 0. -0. -1.7923082] Sparsity at: 0.8448229613733905 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0112 - accuracy: 0.9972 - val_loss: 0.1671 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. -0. -1.7968659] Sparsity at: 0.8448229613733905 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0103 - accuracy: 0.9975 - val_loss: 0.1672 - val_accuracy: 0.9691 [-0. -0. -0. ... 0. -0. -1.8023915] Sparsity at: 0.8448229613733905 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0096 - accuracy: 0.9979 - val_loss: 0.1676 - val_accuracy: 0.9692 [-0. -0. -0. ... 0. -0. -1.8082978] Sparsity at: 0.8448229613733905 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0089 - accuracy: 0.9982 - val_loss: 0.1682 - val_accuracy: 0.9692 [-0. -0. -0. ... 0. -0. -1.8143963] Sparsity at: 0.8448229613733905 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0082 - accuracy: 0.9985 - val_loss: 0.1686 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. -1.821233] Sparsity at: 0.8448229613733905 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0077 - accuracy: 0.9986 - val_loss: 0.1694 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. -0. -1.8284523] Sparsity at: 0.8448229613733905 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0072 - accuracy: 0.9989 - val_loss: 0.1701 - val_accuracy: 0.9697 [-0. -0. -0. ... 0. -0. -1.8361725] Sparsity at: 0.8448229613733905 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0067 - accuracy: 0.9991 - val_loss: 0.1708 - val_accuracy: 0.9697 [-0. -0. -0. ... 0. 0. -1.8441927] Sparsity at: 0.8448229613733905 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0062 - accuracy: 0.9992 - val_loss: 0.1717 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. 0. -1.852512] Sparsity at: 0.8448229613733905 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0058 - accuracy: 0.9994 - val_loss: 0.1727 - val_accuracy: 0.9697 [-0. -0. -0. ... 0. -0. -1.861486] Sparsity at: 0.8448229613733905 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0055 - accuracy: 0.9995 - val_loss: 0.1736 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. -0. -1.870796] Sparsity at: 0.8448229613733905 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0051 - accuracy: 0.9997 - val_loss: 0.1747 - val_accuracy: 0.9700 [-0. -0. -0. ... 0. -0. -1.8802803] Sparsity at: 0.8448229613733905 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0048 - accuracy: 0.9998 - val_loss: 0.1758 - val_accuracy: 0.9701 [-0. -0. -0. ... 0. -0. -1.890179] Sparsity at: 0.8448229613733905 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0045 - accuracy: 0.9998 - val_loss: 0.1768 - val_accuracy: 0.9704 [-0. -0. -0. ... 0. -0. -1.9011841] Sparsity at: 0.8448229613733905 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0042 - accuracy: 0.9998 - val_loss: 0.1783 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. -0. -1.9122967] Sparsity at: 0.8448229613733905 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0039 - accuracy: 0.9998 - val_loss: 0.1793 - val_accuracy: 0.9702 [-0. -0. -0. ... 0. 0. -1.9246377] Sparsity at: 0.8448229613733905 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0037 - accuracy: 0.9999 - val_loss: 0.1807 - val_accuracy: 0.9703 [-0. -0. -0. ... 0. 0. -1.9374578] Sparsity at: 0.8448229613733905 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9999 - val_loss: 0.1819 - val_accuracy: 0.9706 [-0. -0. -0. ... 0. 0. -1.95094] Sparsity at: 0.8448229613733905 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0033 - accuracy: 0.9999 - val_loss: 0.1832 - val_accuracy: 0.9701 [-0. -0. -0. ... 0. -0. -1.964488] Sparsity at: 0.8448229613733905 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0031 - accuracy: 0.9999 - val_loss: 0.1846 - val_accuracy: 0.9700 [-0. -0. -0. ... 0. -0. -1.9783467] Sparsity at: 0.8448229613733905 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9999 - val_loss: 0.1860 - val_accuracy: 0.9701 [-0. -0. -0. ... 0. 0. -1.9927193] Sparsity at: 0.8448229613733905 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9701 [-0. -0. -0. ... 0. 0. -2.0069108] Sparsity at: 0.8448229613733905 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9699 [-0. -0. -0. ... 0. -0. -2.021466] Sparsity at: 0.8448229613733905 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9697 [-0. -0. -0. ... 0. 0. -2.0360525] Sparsity at: 0.8448229613733905 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. 0. -2.0507748] Sparsity at: 0.8448229613733905 Epoch 186/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9699 [-0. -0. -0. ... 0. 0. -2.0660896] Sparsity at: 0.8448229613733905 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9698 [-0. -0. -0. ... 0. 0. -2.0810618] Sparsity at: 0.8448229613733905 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9699 [-0. -0. -0. ... 0. 0. -2.096153] Sparsity at: 0.8448229613733905 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9696 [-0. -0. -0. ... 0. 0. -2.111315] Sparsity at: 0.8448229613733905 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9694 [-0. -0. -0. ... 0. 0. -2.126816] Sparsity at: 0.8448229613733905 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. 0. -2.1418226] Sparsity at: 0.8448229613733905 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9692 [-0. -0. -0. ... 0. 0. -2.1577709] Sparsity at: 0.8448229613733905 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. 0. -2.1732016] Sparsity at: 0.8448229613733905 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9692 [-0. -0. -0. ... 0. -0. -2.1891115] Sparsity at: 0.8448229613733905 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9692 [-0. -0. -0. ... 0. 0. -2.2051053] Sparsity at: 0.8448229613733905 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2100 - val_accuracy: 0.9693 [-0. -0. -0. ... 0. 0. -2.220661] Sparsity at: 0.8448229613733905 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9691 [-0. -0. -0. ... 0. 0. -2.2366815] Sparsity at: 0.8448229613733905 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9691 [-0. -0. -0. ... 0. 0. -2.2523768] Sparsity at: 0.8448229613733905 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 9.9128e-04 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. 0. -2.2688015] Sparsity at: 0.8448229613733905 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3407e-04 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9690 [-0. -0. -0. ... 0. 0. -2.284866] Sparsity at: 0.8448229613733905 Epoch 201/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2044 - accuracy: 0.9470 - val_loss: 0.2320 - val_accuracy: 0.9506 [-0. -0. -0. ... 0. -0. -2.3545854] Sparsity at: 0.9059985246781116 Epoch 202/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1069 - accuracy: 0.9672 - val_loss: 0.2082 - val_accuracy: 0.9559 [-0. -0. -0. ... 0. -0. -2.344783] Sparsity at: 0.9059985246781116 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0897 - accuracy: 0.9713 - val_loss: 0.1966 - val_accuracy: 0.9585 [-0. -0. -0. ... 0. -0. -2.334993] Sparsity at: 0.9059985246781116 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0800 - accuracy: 0.9740 - val_loss: 0.1894 - val_accuracy: 0.9588 [-0. -0. -0. ... 0. -0. -2.3254402] Sparsity at: 0.9059985246781116 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0735 - accuracy: 0.9761 - val_loss: 0.1841 - val_accuracy: 0.9602 [-0. -0. -0. ... 0. -0. -2.3171513] Sparsity at: 0.9059985246781116 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0687 - accuracy: 0.9776 - val_loss: 0.1800 - val_accuracy: 0.9608 [-0. -0. -0. ... 0. -0. -2.3098316] Sparsity at: 0.9059985246781116 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0648 - accuracy: 0.9787 - val_loss: 0.1767 - val_accuracy: 0.9609 [-0. -0. -0. ... 0. -0. -2.3037033] Sparsity at: 0.9059985246781116 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0617 - accuracy: 0.9795 - val_loss: 0.1738 - val_accuracy: 0.9611 [-0. -0. -0. ... 0. -0. -2.2981453] Sparsity at: 0.9059985246781116 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0591 - accuracy: 0.9802 - val_loss: 0.1714 - val_accuracy: 0.9613 [-0. -0. -0. ... 0. -0. -2.2935739] Sparsity at: 0.9059985246781116 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0569 - accuracy: 0.9808 - val_loss: 0.1694 - val_accuracy: 0.9618 [-0. -0. -0. ... 0. -0. -2.2898388] Sparsity at: 0.9059985246781116 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0549 - accuracy: 0.9816 - val_loss: 0.1675 - val_accuracy: 0.9619 [-0. -0. -0. ... 0. -0. -2.2872412] Sparsity at: 0.9059985246781116 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0532 - accuracy: 0.9821 - val_loss: 0.1658 - val_accuracy: 0.9626 [-0. -0. -0. ... 0. -0. -2.2850935] Sparsity at: 0.9059985246781116 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0516 - accuracy: 0.9826 - val_loss: 0.1643 - val_accuracy: 0.9628 [-0. -0. -0. ... 0. -0. -2.283854] Sparsity at: 0.9059985246781116 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0502 - accuracy: 0.9829 - val_loss: 0.1630 - val_accuracy: 0.9631 [-0. -0. -0. ... 0. -0. -2.2835102] Sparsity at: 0.9059985246781116 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0489 - accuracy: 0.9834 - val_loss: 0.1618 - val_accuracy: 0.9631 [-0. -0. -0. ... -0. -0. -2.283599] Sparsity at: 0.9059985246781116 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0477 - accuracy: 0.9837 - val_loss: 0.1607 - val_accuracy: 0.9631 [-0. -0. -0. ... 0. -0. -2.2844095] Sparsity at: 0.9059985246781116 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0467 - accuracy: 0.9841 - val_loss: 0.1597 - val_accuracy: 0.9635 [-0. -0. -0. ... -0. -0. -2.2854853] Sparsity at: 0.9059985246781116 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0457 - accuracy: 0.9844 - val_loss: 0.1589 - val_accuracy: 0.9638 [-0. -0. -0. ... 0. -0. -2.2873783] Sparsity at: 0.9059985246781116 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0448 - accuracy: 0.9847 - val_loss: 0.1581 - val_accuracy: 0.9640 [-0. -0. -0. ... -0. -0. -2.2896485] Sparsity at: 0.9059985246781116 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0439 - accuracy: 0.9851 - val_loss: 0.1574 - val_accuracy: 0.9638 [-0. -0. -0. ... 0. -0. -2.2931924] Sparsity at: 0.9059985246781116 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0431 - accuracy: 0.9853 - val_loss: 0.1567 - val_accuracy: 0.9639 [-0. -0. -0. ... 0. -0. -2.2963223] Sparsity at: 0.9059985246781116 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0424 - accuracy: 0.9858 - val_loss: 0.1562 - val_accuracy: 0.9640 [-0. -0. -0. ... -0. -0. -2.2997863] Sparsity at: 0.9059985246781116 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0416 - accuracy: 0.9860 - val_loss: 0.1557 - val_accuracy: 0.9639 [-0. -0. -0. ... 0. -0. -2.3034658] Sparsity at: 0.9059985246781116 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0410 - accuracy: 0.9862 - val_loss: 0.1552 - val_accuracy: 0.9642 [-0. -0. -0. ... -0. -0. -2.307398] Sparsity at: 0.9059985246781116 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0404 - accuracy: 0.9865 - val_loss: 0.1548 - val_accuracy: 0.9642 [-0. -0. -0. ... -0. -0. -2.3117638] Sparsity at: 0.9059985246781116 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0398 - accuracy: 0.9868 - val_loss: 0.1545 - val_accuracy: 0.9644 [-0. -0. -0. ... -0. -0. -2.316233] Sparsity at: 0.9059985246781116 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0392 - accuracy: 0.9870 - val_loss: 0.1541 - val_accuracy: 0.9645 [-0. -0. -0. ... -0. -0. -2.3206987] Sparsity at: 0.9059985246781116 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0386 - accuracy: 0.9873 - val_loss: 0.1539 - val_accuracy: 0.9647 [-0. -0. -0. ... -0. -0. -2.3256187] Sparsity at: 0.9059985246781116 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0381 - accuracy: 0.9874 - val_loss: 0.1537 - val_accuracy: 0.9649 [-0. -0. -0. ... -0. -0. -2.3306527] Sparsity at: 0.9059985246781116 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0376 - accuracy: 0.9878 - val_loss: 0.1535 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.336068] Sparsity at: 0.9059985246781116 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0372 - accuracy: 0.9880 - val_loss: 0.1534 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.3414526] Sparsity at: 0.9059985246781116 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0367 - accuracy: 0.9881 - val_loss: 0.1533 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.347127] Sparsity at: 0.9059985246781116 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0363 - accuracy: 0.9884 - val_loss: 0.1532 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.353049] Sparsity at: 0.9059985246781116 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0359 - accuracy: 0.9886 - val_loss: 0.1531 - val_accuracy: 0.9652 [-0. -0. -0. ... -0. -0. -2.359361] Sparsity at: 0.9059985246781116 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0355 - accuracy: 0.9888 - val_loss: 0.1531 - val_accuracy: 0.9651 [-0. -0. -0. ... -0. -0. -2.3664706] Sparsity at: 0.9059985246781116 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0351 - accuracy: 0.9890 - val_loss: 0.1531 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.372792] Sparsity at: 0.9059985246781116 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0347 - accuracy: 0.9890 - val_loss: 0.1532 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.3790238] Sparsity at: 0.9059985246781116 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0343 - accuracy: 0.9893 - val_loss: 0.1532 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.3854904] Sparsity at: 0.9059985246781116 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0340 - accuracy: 0.9893 - val_loss: 0.1533 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.3920777] Sparsity at: 0.9059985246781116 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0336 - accuracy: 0.9895 - val_loss: 0.1534 - val_accuracy: 0.9650 [-0. -0. -0. ... -0. -0. -2.3988426] Sparsity at: 0.9059985246781116 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0333 - accuracy: 0.9897 - val_loss: 0.1535 - val_accuracy: 0.9649 [-0. -0. -0. ... -0. -0. -2.4055161] Sparsity at: 0.9059985246781116 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0330 - accuracy: 0.9898 - val_loss: 0.1537 - val_accuracy: 0.9648 [-0. -0. -0. ... -0. -0. -2.412254] Sparsity at: 0.9059985246781116 Epoch 243/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0327 - accuracy: 0.9899 - val_loss: 0.1539 - val_accuracy: 0.9646 [-0. -0. -0. ... -0. -0. -2.4191165] Sparsity at: 0.9059985246781116 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0324 - accuracy: 0.9900 - val_loss: 0.1540 - val_accuracy: 0.9646 [-0. -0. -0. ... -0. -0. -2.4261296] Sparsity at: 0.9059985246781116 Epoch 245/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0321 - accuracy: 0.9900 - val_loss: 0.1542 - val_accuracy: 0.9646 [-0. -0. -0. ... -0. -0. -2.433433] Sparsity at: 0.9059985246781116 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0318 - accuracy: 0.9901 - val_loss: 0.1543 - val_accuracy: 0.9647 [-0. -0. -0. ... -0. -0. -2.44054] Sparsity at: 0.9059985246781116 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0315 - accuracy: 0.9902 - val_loss: 0.1546 - val_accuracy: 0.9649 [-0. -0. -0. ... -0. -0. -2.447858] Sparsity at: 0.9059985246781116 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0313 - accuracy: 0.9903 - val_loss: 0.1548 - val_accuracy: 0.9651 [-0. -0. -0. ... -0. -0. -2.4547138] Sparsity at: 0.9059985246781116 Epoch 249/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0310 - accuracy: 0.9904 - val_loss: 0.1551 - val_accuracy: 0.9653 [-0. -0. -0. ... -0. -0. -2.461772] Sparsity at: 0.9059985246781116 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0307 - accuracy: 0.9906 - val_loss: 0.1553 - val_accuracy: 0.9653 [-0. -0. -0. ... -0. -0. -2.468659] Sparsity at: 0.9059985246781116 Epoch 251/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4910 - accuracy: 0.8537 - val_loss: 0.3351 - val_accuracy: 0.8997 [-0. -0. -0. ... -0. 0. -2.4395401] Sparsity at: 0.9469890021459227 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 0.3028 - accuracy: 0.9039 - val_loss: 0.2901 - val_accuracy: 0.9124 [-0. -0. -0. ... -0. 0. -2.486727] Sparsity at: 0.9469890021459227 Epoch 253/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2664 - accuracy: 0.9149 - val_loss: 0.2685 - val_accuracy: 0.9192 [-0. -0. -0. ... -0. 0. -2.5269258] Sparsity at: 0.9469890021459227 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2465 - accuracy: 0.9213 - val_loss: 0.2554 - val_accuracy: 0.9237 [-0. -0. -0. ... -0. 0. -2.5579612] Sparsity at: 0.9469890021459227 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2340 - accuracy: 0.9255 - val_loss: 0.2465 - val_accuracy: 0.9255 [-0. -0. -0. ... -0. 0. -2.5808158] Sparsity at: 0.9469890021459227 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2251 - accuracy: 0.9285 - val_loss: 0.2399 - val_accuracy: 0.9274 [-0. -0. -0. ... -0. 0. -2.599114] Sparsity at: 0.9469890021459227 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2184 - accuracy: 0.9304 - val_loss: 0.2346 - val_accuracy: 0.9290 [-0. -0. -0. ... -0. 0. -2.6143634] Sparsity at: 0.9469890021459227 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2129 - accuracy: 0.9324 - val_loss: 0.2302 - val_accuracy: 0.9308 [-0. -0. -0. ... -0. 0. -2.627936] Sparsity at: 0.9469890021459227 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2083 - accuracy: 0.9336 - val_loss: 0.2264 - val_accuracy: 0.9319 [-0. -0. -0. ... -0. 0. -2.6397662] Sparsity at: 0.9469890021459227 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2043 - accuracy: 0.9348 - val_loss: 0.2231 - val_accuracy: 0.9324 [-0. -0. -0. ... -0. 0. -2.6511111] Sparsity at: 0.9469890021459227 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2008 - accuracy: 0.9357 - val_loss: 0.2201 - val_accuracy: 0.9326 [-0. -0. -0. ... -0. 0. -2.6615276] Sparsity at: 0.9469890021459227 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1976 - accuracy: 0.9370 - val_loss: 0.2175 - val_accuracy: 0.9332 [-0. -0. -0. ... -0. 0. -2.6712673] Sparsity at: 0.9469890021459227 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1948 - accuracy: 0.9379 - val_loss: 0.2151 - val_accuracy: 0.9340 [-0. -0. -0. ... -0. 0. -2.6802719] Sparsity at: 0.9469890021459227 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1922 - accuracy: 0.9387 - val_loss: 0.2130 - val_accuracy: 0.9350 [-0. -0. -0. ... -0. 0. -2.6885839] Sparsity at: 0.9469890021459227 Epoch 265/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1898 - accuracy: 0.9398 - val_loss: 0.2110 - val_accuracy: 0.9355 [-0. -0. -0. ... -0. 0. -2.696388] Sparsity at: 0.9469890021459227 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1876 - accuracy: 0.9406 - val_loss: 0.2092 - val_accuracy: 0.9365 [-0. -0. -0. ... -0. 0. -2.703596] Sparsity at: 0.9469890021459227 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1856 - accuracy: 0.9411 - val_loss: 0.2076 - val_accuracy: 0.9376 [-0. -0. -0. ... -0. 0. -2.7103546] Sparsity at: 0.9469890021459227 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1838 - accuracy: 0.9419 - val_loss: 0.2061 - val_accuracy: 0.9379 [-0. -0. -0. ... -0. 0. -2.7161982] Sparsity at: 0.9469890021459227 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1821 - accuracy: 0.9427 - val_loss: 0.2048 - val_accuracy: 0.9383 [-0. -0. -0. ... -0. 0. -2.7220504] Sparsity at: 0.9469890021459227 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1805 - accuracy: 0.9430 - val_loss: 0.2036 - val_accuracy: 0.9391 [-0. -0. -0. ... -0. 0. -2.727254] Sparsity at: 0.9469890021459227 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1790 - accuracy: 0.9436 - val_loss: 0.2025 - val_accuracy: 0.9399 [-0. -0. -0. ... -0. 0. -2.732266] Sparsity at: 0.9469890021459227 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1776 - accuracy: 0.9438 - val_loss: 0.2015 - val_accuracy: 0.9401 [-0. -0. -0. ... -0. 0. -2.7367654] Sparsity at: 0.9469890021459227 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1763 - accuracy: 0.9442 - val_loss: 0.2006 - val_accuracy: 0.9403 [-0. -0. -0. ... -0. 0. -2.74053] Sparsity at: 0.9469890021459227 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1751 - accuracy: 0.9445 - val_loss: 0.1998 - val_accuracy: 0.9402 [-0. -0. -0. ... -0. 0. -2.7444315] Sparsity at: 0.9469890021459227 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1740 - accuracy: 0.9445 - val_loss: 0.1990 - val_accuracy: 0.9409 [-0. -0. -0. ... -0. 0. -2.747985] Sparsity at: 0.9469890021459227 Epoch 276/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1729 - accuracy: 0.9449 - val_loss: 0.1983 - val_accuracy: 0.9409 [-0. -0. -0. ... -0. 0. -2.7510498] Sparsity at: 0.9469890021459227 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1718 - accuracy: 0.9453 - val_loss: 0.1976 - val_accuracy: 0.9412 [-0. -0. -0. ... -0. 0. -2.753793] Sparsity at: 0.9469890021459227 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1709 - accuracy: 0.9455 - val_loss: 0.1970 - val_accuracy: 0.9412 [-0. -0. -0. ... -0. 0. -2.7562249] Sparsity at: 0.9469890021459227 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1699 - accuracy: 0.9459 - val_loss: 0.1965 - val_accuracy: 0.9418 [-0. -0. -0. ... -0. 0. -2.758989] Sparsity at: 0.9469890021459227 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1691 - accuracy: 0.9462 - val_loss: 0.1960 - val_accuracy: 0.9419 [-0. -0. -0. ... -0. 0. -2.7609184] Sparsity at: 0.9469890021459227 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1682 - accuracy: 0.9464 - val_loss: 0.1955 - val_accuracy: 0.9421 [-0. -0. -0. ... -0. 0. -2.7632003] Sparsity at: 0.9469890021459227 Epoch 282/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1674 - accuracy: 0.9467 - val_loss: 0.1950 - val_accuracy: 0.9424 [-0. -0. -0. ... -0. 0. -2.7653513] Sparsity at: 0.9469890021459227 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1666 - accuracy: 0.9470 - val_loss: 0.1946 - val_accuracy: 0.9425 [-0. -0. -0. ... -0. 0. -2.7672694] Sparsity at: 0.9469890021459227 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1658 - accuracy: 0.9473 - val_loss: 0.1942 - val_accuracy: 0.9426 [-0. -0. -0. ... -0. 0. -2.7693048] Sparsity at: 0.9469890021459227 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1651 - accuracy: 0.9475 - val_loss: 0.1939 - val_accuracy: 0.9427 [-0. -0. -0. ... -0. 0. -2.7712476] Sparsity at: 0.9469890021459227 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1644 - accuracy: 0.9478 - val_loss: 0.1935 - val_accuracy: 0.9425 [-0. -0. -0. ... -0. 0. -2.7729177] Sparsity at: 0.9469890021459227 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1638 - accuracy: 0.9479 - val_loss: 0.1932 - val_accuracy: 0.9427 [-0. -0. -0. ... -0. 0. -2.7741919] Sparsity at: 0.9469890021459227 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1631 - accuracy: 0.9480 - val_loss: 0.1928 - val_accuracy: 0.9427 [-0. -0. -0. ... -0. 0. -2.7758865] Sparsity at: 0.9469890021459227 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1625 - accuracy: 0.9482 - val_loss: 0.1926 - val_accuracy: 0.9428 [-0. -0. -0. ... -0. 0. -2.7775726] Sparsity at: 0.9469890021459227 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1620 - accuracy: 0.9484 - val_loss: 0.1923 - val_accuracy: 0.9429 [-0. -0. -0. ... -0. 0. -2.7786777] Sparsity at: 0.9469890021459227 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1614 - accuracy: 0.9486 - val_loss: 0.1920 - val_accuracy: 0.9431 [-0. -0. -0. ... -0. 0. -2.780527] Sparsity at: 0.9469890021459227 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1608 - accuracy: 0.9489 - val_loss: 0.1918 - val_accuracy: 0.9429 [-0. -0. -0. ... -0. 0. -2.7816768] Sparsity at: 0.9469890021459227 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1603 - accuracy: 0.9491 - val_loss: 0.1915 - val_accuracy: 0.9428 [-0. -0. -0. ... -0. 0. -2.7832756] Sparsity at: 0.9469890021459227 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1598 - accuracy: 0.9493 - val_loss: 0.1913 - val_accuracy: 0.9428 [-0. -0. -0. ... -0. 0. -2.7852101] Sparsity at: 0.9469890021459227 Epoch 295/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1593 - accuracy: 0.9494 - val_loss: 0.1910 - val_accuracy: 0.9432 [-0. -0. -0. ... -0. 0. -2.7865098] Sparsity at: 0.9469890021459227 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1588 - accuracy: 0.9496 - val_loss: 0.1908 - val_accuracy: 0.9434 [-0. -0. -0. ... -0. 0. -2.7879] Sparsity at: 0.9469890021459227 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1583 - accuracy: 0.9497 - val_loss: 0.1906 - val_accuracy: 0.9436 [-0. -0. -0. ... -0. 0. -2.7897391] Sparsity at: 0.9469890021459227 Epoch 298/500 235/235 [==============================] - 2s 10ms/step - loss: 0.1579 - accuracy: 0.9500 - val_loss: 0.1904 - val_accuracy: 0.9438 [-0. -0. -0. ... -0. 0. -2.7914326] Sparsity at: 0.9469890021459227 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1574 - accuracy: 0.9500 - val_loss: 0.1903 - val_accuracy: 0.9438 [-0. -0. -0. ... 0. 0. -2.7926548] Sparsity at: 0.9469890021459227 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1570 - accuracy: 0.9502 - val_loss: 0.1901 - val_accuracy: 0.9438 [-0. -0. -0. ... -0. 0. -2.794541] Sparsity at: 0.9469890021459227 Epoch 301/500 235/235 [==============================] - 2s 9ms/step - loss: 0.7943 - accuracy: 0.7459 - val_loss: 0.6487 - val_accuracy: 0.8029 [-0. -0. -0. ... 0. 0. -2.6900098] Sparsity at: 0.9718515289699571 Epoch 302/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6332 - accuracy: 0.8027 - val_loss: 0.6057 - val_accuracy: 0.8191 [-0. -0. -0. ... 0. 0. -2.6023936] Sparsity at: 0.9718515289699571 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6028 - accuracy: 0.8134 - val_loss: 0.5846 - val_accuracy: 0.8249 [-0. -0. -0. ... 0. 0. -2.5434372] Sparsity at: 0.9718515289699571 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5856 - accuracy: 0.8182 - val_loss: 0.5715 - val_accuracy: 0.8291 [-0. -0. -0. ... 0. 0. -2.5059433] Sparsity at: 0.9718515289699571 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5739 - accuracy: 0.8221 - val_loss: 0.5624 - val_accuracy: 0.8326 [-0. -0. -0. ... 0. 0. -2.483021] Sparsity at: 0.9718515289699571 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5651 - accuracy: 0.8250 - val_loss: 0.5554 - val_accuracy: 0.8351 [-0. -0. -0. ... 0. 0. -2.468537] Sparsity at: 0.9718515289699571 Epoch 307/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5581 - accuracy: 0.8270 - val_loss: 0.5498 - val_accuracy: 0.8365 [-0. -0. -0. ... 0. 0. -2.4610317] Sparsity at: 0.9718515289699571 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5520 - accuracy: 0.8290 - val_loss: 0.5448 - val_accuracy: 0.8385 [-0. -0. -0. ... 0. 0. -2.4578817] Sparsity at: 0.9718515289699571 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5467 - accuracy: 0.8309 - val_loss: 0.5407 - val_accuracy: 0.8393 [-0. -0. -0. ... 0. 0. -2.4593544] Sparsity at: 0.9718515289699571 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5423 - accuracy: 0.8327 - val_loss: 0.5373 - val_accuracy: 0.8404 [-0. -0. -0. ... 0. 0. -2.4633198] Sparsity at: 0.9718515289699571 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5387 - accuracy: 0.8337 - val_loss: 0.5343 - val_accuracy: 0.8411 [-0. -0. -0. ... 0. 0. -2.467665] Sparsity at: 0.9718515289699571 Epoch 312/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5355 - accuracy: 0.8352 - val_loss: 0.5317 - val_accuracy: 0.8425 [-0. -0. -0. ... 0. 0. -2.4722986] Sparsity at: 0.9718515289699571 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5327 - accuracy: 0.8360 - val_loss: 0.5292 - val_accuracy: 0.8434 [-0. -0. -0. ... 0. 0. -2.477545] Sparsity at: 0.9718515289699571 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5300 - accuracy: 0.8365 - val_loss: 0.5268 - val_accuracy: 0.8438 [-0. -0. -0. ... 0. 0. -2.4834504] Sparsity at: 0.9718515289699571 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5274 - accuracy: 0.8375 - val_loss: 0.5244 - val_accuracy: 0.8449 [-0. -0. -0. ... 0. 0. -2.4892974] Sparsity at: 0.9718515289699571 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5249 - accuracy: 0.8386 - val_loss: 0.5221 - val_accuracy: 0.8455 [-0. -0. -0. ... 0. 0. -2.4956393] Sparsity at: 0.9718515289699571 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5224 - accuracy: 0.8395 - val_loss: 0.5197 - val_accuracy: 0.8466 [-0. -0. -0. ... 0. 0. -2.5028048] Sparsity at: 0.9718515289699571 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5200 - accuracy: 0.8399 - val_loss: 0.5177 - val_accuracy: 0.8471 [-0. -0. -0. ... 0. 0. -2.510462] Sparsity at: 0.9718515289699571 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5179 - accuracy: 0.8403 - val_loss: 0.5159 - val_accuracy: 0.8475 [-0. -0. -0. ... 0. 0. -2.5186467] Sparsity at: 0.9718515289699571 Epoch 320/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5160 - accuracy: 0.8408 - val_loss: 0.5144 - val_accuracy: 0.8485 [-0. -0. -0. ... 0. 0. -2.5276606] Sparsity at: 0.9718515289699571 Epoch 321/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5144 - accuracy: 0.8414 - val_loss: 0.5130 - val_accuracy: 0.8484 [-0. -0. -0. ... 0. 0. -2.5373785] Sparsity at: 0.9718515289699571 Epoch 322/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5129 - accuracy: 0.8416 - val_loss: 0.5117 - val_accuracy: 0.8488 [-0. -0. -0. ... 0. 0. -2.5469127] Sparsity at: 0.9718515289699571 Epoch 323/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5115 - accuracy: 0.8421 - val_loss: 0.5106 - val_accuracy: 0.8495 [-0. -0. -0. ... 0. 0. -2.5566618] Sparsity at: 0.9718515289699571 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5102 - accuracy: 0.8425 - val_loss: 0.5095 - val_accuracy: 0.8502 [-0. -0. -0. ... 0. 0. -2.5659082] Sparsity at: 0.9718515289699571 Epoch 325/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5091 - accuracy: 0.8430 - val_loss: 0.5085 - val_accuracy: 0.8508 [-0. -0. -0. ... 0. 0. -2.575369] Sparsity at: 0.9718515289699571 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5080 - accuracy: 0.8430 - val_loss: 0.5075 - val_accuracy: 0.8512 [-0. -0. -0. ... 0. 0. -2.5848129] Sparsity at: 0.9718515289699571 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5070 - accuracy: 0.8433 - val_loss: 0.5066 - val_accuracy: 0.8515 [-0. -0. -0. ... -0. 0. -2.5937] Sparsity at: 0.9718515289699571 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5061 - accuracy: 0.8435 - val_loss: 0.5058 - val_accuracy: 0.8517 [-0. -0. -0. ... -0. 0. -2.6022801] Sparsity at: 0.9718515289699571 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5051 - accuracy: 0.8436 - val_loss: 0.5050 - val_accuracy: 0.8522 [-0. -0. -0. ... 0. 0. -2.610608] Sparsity at: 0.9718515289699571 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5043 - accuracy: 0.8440 - val_loss: 0.5043 - val_accuracy: 0.8524 [-0. -0. -0. ... -0. 0. -2.6188123] Sparsity at: 0.9718515289699571 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5035 - accuracy: 0.8443 - val_loss: 0.5036 - val_accuracy: 0.8533 [-0. -0. -0. ... -0. 0. -2.6268635] Sparsity at: 0.9718515289699571 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5028 - accuracy: 0.8444 - val_loss: 0.5028 - val_accuracy: 0.8539 [-0. -0. -0. ... -0. 0. -2.6347785] Sparsity at: 0.9718515289699571 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5021 - accuracy: 0.8447 - val_loss: 0.5022 - val_accuracy: 0.8540 [-0. -0. -0. ... -0. 0. -2.6427677] Sparsity at: 0.9718515289699571 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5014 - accuracy: 0.8449 - val_loss: 0.5016 - val_accuracy: 0.8540 [-0. -0. -0. ... -0. 0. -2.6499667] Sparsity at: 0.9718515289699571 Epoch 335/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5007 - accuracy: 0.8453 - val_loss: 0.5010 - val_accuracy: 0.8543 [-0. -0. -0. ... -0. 0. -2.6574116] Sparsity at: 0.9718515289699571 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5001 - accuracy: 0.8454 - val_loss: 0.5004 - val_accuracy: 0.8543 [-0. -0. -0. ... -0. 0. -2.6643114] Sparsity at: 0.9718515289699571 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4995 - accuracy: 0.8456 - val_loss: 0.4999 - val_accuracy: 0.8544 [-0. -0. -0. ... -0. 0. -2.6710615] Sparsity at: 0.9718515289699571 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4989 - accuracy: 0.8457 - val_loss: 0.4993 - val_accuracy: 0.8549 [-0. -0. -0. ... -0. 0. -2.677571] Sparsity at: 0.9718515289699571 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4982 - accuracy: 0.8461 - val_loss: 0.4986 - val_accuracy: 0.8547 [-0. -0. -0. ... -0. 0. -2.684011] Sparsity at: 0.9718515289699571 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4976 - accuracy: 0.8465 - val_loss: 0.4980 - val_accuracy: 0.8547 [-0. -0. -0. ... -0. 0. -2.6896653] Sparsity at: 0.9718515289699571 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4967 - accuracy: 0.8470 - val_loss: 0.4970 - val_accuracy: 0.8548 [-0. -0. -0. ... -0. 0. -2.6944613] Sparsity at: 0.9718515289699571 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4959 - accuracy: 0.8471 - val_loss: 0.4961 - val_accuracy: 0.8547 [-0. -0. -0. ... -0. 0. -2.698111] Sparsity at: 0.9718515289699571 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4952 - accuracy: 0.8473 - val_loss: 0.4956 - val_accuracy: 0.8550 [-0. -0. -0. ... -0. 0. -2.7018592] Sparsity at: 0.9718515289699571 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4946 - accuracy: 0.8476 - val_loss: 0.4951 - val_accuracy: 0.8552 [-0. -0. -0. ... -0. 0. -2.7057436] Sparsity at: 0.9718515289699571 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4941 - accuracy: 0.8476 - val_loss: 0.4947 - val_accuracy: 0.8552 [-0. -0. -0. ... -0. 0. -2.7095397] Sparsity at: 0.9718515289699571 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4937 - accuracy: 0.8477 - val_loss: 0.4943 - val_accuracy: 0.8554 [-0. -0. -0. ... -0. 0. -2.714191] Sparsity at: 0.9718515289699571 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4932 - accuracy: 0.8479 - val_loss: 0.4939 - val_accuracy: 0.8553 [-0. -0. -0. ... -0. 0. -2.7192228] Sparsity at: 0.9718515289699571 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4928 - accuracy: 0.8482 - val_loss: 0.4936 - val_accuracy: 0.8554 [-0. -0. -0. ... -0. 0. -2.7239852] Sparsity at: 0.9718515289699571 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4924 - accuracy: 0.8482 - val_loss: 0.4933 - val_accuracy: 0.8554 [-0. -0. -0. ... -0. 0. -2.728846] Sparsity at: 0.9718515289699571 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4920 - accuracy: 0.8484 - val_loss: 0.4930 - val_accuracy: 0.8557 [-0. -0. -0. ... -0. 0. -2.7337844] Sparsity at: 0.9718515289699571 Epoch 351/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3577 - accuracy: 0.5720 - val_loss: 1.2285 - val_accuracy: 0.5999 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 352/500 235/235 [==============================] - 2s 10ms/step - loss: 1.2413 - accuracy: 0.5983 - val_loss: 1.2112 - val_accuracy: 0.6024 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2272 - accuracy: 0.5972 - val_loss: 1.2045 - val_accuracy: 0.6030 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2200 - accuracy: 0.5986 - val_loss: 1.2002 - val_accuracy: 0.6044 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2152 - accuracy: 0.6003 - val_loss: 1.1967 - val_accuracy: 0.6056 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2114 - accuracy: 0.6006 - val_loss: 1.1937 - val_accuracy: 0.6060 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2081 - accuracy: 0.6015 - val_loss: 1.1908 - val_accuracy: 0.6072 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2048 - accuracy: 0.6021 - val_loss: 1.1874 - val_accuracy: 0.6082 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2002 - accuracy: 0.6031 - val_loss: 1.1826 - val_accuracy: 0.6094 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.6043 - val_loss: 1.1799 - val_accuracy: 0.6110 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1932 - accuracy: 0.6038 - val_loss: 1.1782 - val_accuracy: 0.6107 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1914 - accuracy: 0.6042 - val_loss: 1.1766 - val_accuracy: 0.6107 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1899 - accuracy: 0.6041 - val_loss: 1.1752 - val_accuracy: 0.6110 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1886 - accuracy: 0.6043 - val_loss: 1.1741 - val_accuracy: 0.6112 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1874 - accuracy: 0.6044 - val_loss: 1.1730 - val_accuracy: 0.6112 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1863 - accuracy: 0.6044 - val_loss: 1.1721 - val_accuracy: 0.6116 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1853 - accuracy: 0.6046 - val_loss: 1.1713 - val_accuracy: 0.6119 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1845 - accuracy: 0.6051 - val_loss: 1.1705 - val_accuracy: 0.6121 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1836 - accuracy: 0.6051 - val_loss: 1.1699 - val_accuracy: 0.6121 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1829 - accuracy: 0.6048 - val_loss: 1.1693 - val_accuracy: 0.6128 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1823 - accuracy: 0.6055 - val_loss: 1.1688 - val_accuracy: 0.6129 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1817 - accuracy: 0.6055 - val_loss: 1.1684 - val_accuracy: 0.6130 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1811 - accuracy: 0.6060 - val_loss: 1.1679 - val_accuracy: 0.6130 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1806 - accuracy: 0.6062 - val_loss: 1.1676 - val_accuracy: 0.6131 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1801 - accuracy: 0.6061 - val_loss: 1.1672 - val_accuracy: 0.6132 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1797 - accuracy: 0.6060 - val_loss: 1.1669 - val_accuracy: 0.6138 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1793 - accuracy: 0.6060 - val_loss: 1.1666 - val_accuracy: 0.6137 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1789 - accuracy: 0.6065 - val_loss: 1.1663 - val_accuracy: 0.6142 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1786 - accuracy: 0.6066 - val_loss: 1.1660 - val_accuracy: 0.6145 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1782 - accuracy: 0.6068 - val_loss: 1.1657 - val_accuracy: 0.6145 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1779 - accuracy: 0.6069 - val_loss: 1.1655 - val_accuracy: 0.6150 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1776 - accuracy: 0.6073 - val_loss: 1.1653 - val_accuracy: 0.6149 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1773 - accuracy: 0.6069 - val_loss: 1.1650 - val_accuracy: 0.6148 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1770 - accuracy: 0.6075 - val_loss: 1.1648 - val_accuracy: 0.6149 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1767 - accuracy: 0.6073 - val_loss: 1.1646 - val_accuracy: 0.6150 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1764 - accuracy: 0.6072 - val_loss: 1.1643 - val_accuracy: 0.6151 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1762 - accuracy: 0.6079 - val_loss: 1.1641 - val_accuracy: 0.6148 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1759 - accuracy: 0.6079 - val_loss: 1.1639 - val_accuracy: 0.6152 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1756 - accuracy: 0.6077 - val_loss: 1.1636 - val_accuracy: 0.6151 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1753 - accuracy: 0.6078 - val_loss: 1.1634 - val_accuracy: 0.6152 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1751 - accuracy: 0.6083 - val_loss: 1.1632 - val_accuracy: 0.6153 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1748 - accuracy: 0.6079 - val_loss: 1.1630 - val_accuracy: 0.6153 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1746 - accuracy: 0.6085 - val_loss: 1.1629 - val_accuracy: 0.6154 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1743 - accuracy: 0.6082 - val_loss: 1.1627 - val_accuracy: 0.6154 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1741 - accuracy: 0.6085 - val_loss: 1.1626 - val_accuracy: 0.6154 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1740 - accuracy: 0.6082 - val_loss: 1.1624 - val_accuracy: 0.6153 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1738 - accuracy: 0.6084 - val_loss: 1.1623 - val_accuracy: 0.6154 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1736 - accuracy: 0.6085 - val_loss: 1.1622 - val_accuracy: 0.6154 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1734 - accuracy: 0.6087 - val_loss: 1.1621 - val_accuracy: 0.6152 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 400/500 235/235 [==============================] - 2s 10ms/step - loss: 1.1732 - accuracy: 0.6088 - val_loss: 1.1620 - val_accuracy: 0.6154 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 401/500 235/235 [==============================] - 2s 10ms/step - loss: 1.7721 - accuracy: 0.3932 - val_loss: 1.6076 - val_accuracy: 0.4268 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6161 - accuracy: 0.4288 - val_loss: 1.5961 - val_accuracy: 0.4307 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6074 - accuracy: 0.4329 - val_loss: 1.5890 - val_accuracy: 0.4325 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6017 - accuracy: 0.4351 - val_loss: 1.5839 - val_accuracy: 0.4321 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5974 - accuracy: 0.4370 - val_loss: 1.5797 - val_accuracy: 0.4335 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5939 - accuracy: 0.4392 - val_loss: 1.5763 - val_accuracy: 0.4545 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5908 - accuracy: 0.4448 - val_loss: 1.5732 - val_accuracy: 0.4555 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5880 - accuracy: 0.4505 - val_loss: 1.5706 - val_accuracy: 0.4564 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5856 - accuracy: 0.4554 - val_loss: 1.5684 - val_accuracy: 0.4571 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5836 - accuracy: 0.4580 - val_loss: 1.5666 - val_accuracy: 0.4575 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5819 - accuracy: 0.4604 - val_loss: 1.5652 - val_accuracy: 0.4577 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5806 - accuracy: 0.4616 - val_loss: 1.5641 - val_accuracy: 0.4581 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5795 - accuracy: 0.4615 - val_loss: 1.5630 - val_accuracy: 0.4584 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5784 - accuracy: 0.4616 - val_loss: 1.5622 - val_accuracy: 0.4591 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5775 - accuracy: 0.4615 - val_loss: 1.5614 - val_accuracy: 0.4590 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5768 - accuracy: 0.4610 - val_loss: 1.5607 - val_accuracy: 0.4599 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5760 - accuracy: 0.4609 - val_loss: 1.5601 - val_accuracy: 0.4610 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5754 - accuracy: 0.4605 - val_loss: 1.5596 - val_accuracy: 0.4607 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5748 - accuracy: 0.4603 - val_loss: 1.5589 - val_accuracy: 0.4619 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5743 - accuracy: 0.4600 - val_loss: 1.5585 - val_accuracy: 0.4599 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5738 - accuracy: 0.4598 - val_loss: 1.5582 - val_accuracy: 0.4595 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5734 - accuracy: 0.4596 - val_loss: 1.5578 - val_accuracy: 0.4600 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5730 - accuracy: 0.4594 - val_loss: 1.5575 - val_accuracy: 0.4601 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5726 - accuracy: 0.4587 - val_loss: 1.5571 - val_accuracy: 0.4602 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5722 - accuracy: 0.4584 - val_loss: 1.5569 - val_accuracy: 0.4605 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5719 - accuracy: 0.4580 - val_loss: 1.5565 - val_accuracy: 0.4607 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5716 - accuracy: 0.4577 - val_loss: 1.5563 - val_accuracy: 0.4609 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5713 - accuracy: 0.4575 - val_loss: 1.5561 - val_accuracy: 0.4609 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5710 - accuracy: 0.4575 - val_loss: 1.5559 - val_accuracy: 0.4611 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5708 - accuracy: 0.4577 - val_loss: 1.5557 - val_accuracy: 0.4608 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5706 - accuracy: 0.4576 - val_loss: 1.5555 - val_accuracy: 0.4611 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5703 - accuracy: 0.4575 - val_loss: 1.5553 - val_accuracy: 0.4613 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5701 - accuracy: 0.4575 - val_loss: 1.5551 - val_accuracy: 0.4618 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5699 - accuracy: 0.4574 - val_loss: 1.5549 - val_accuracy: 0.4616 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5697 - accuracy: 0.4573 - val_loss: 1.5548 - val_accuracy: 0.4611 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5695 - accuracy: 0.4571 - val_loss: 1.5547 - val_accuracy: 0.4612 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5693 - accuracy: 0.4568 - val_loss: 1.5545 - val_accuracy: 0.4613 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5691 - accuracy: 0.4566 - val_loss: 1.5544 - val_accuracy: 0.4614 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5690 - accuracy: 0.4567 - val_loss: 1.5541 - val_accuracy: 0.4617 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5688 - accuracy: 0.4562 - val_loss: 1.5540 - val_accuracy: 0.4616 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5686 - accuracy: 0.4563 - val_loss: 1.5539 - val_accuracy: 0.4615 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5685 - accuracy: 0.4564 - val_loss: 1.5538 - val_accuracy: 0.4615 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5683 - accuracy: 0.4563 - val_loss: 1.5537 - val_accuracy: 0.4617 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5681 - accuracy: 0.4560 - val_loss: 1.5534 - val_accuracy: 0.4618 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5680 - accuracy: 0.4560 - val_loss: 1.5533 - val_accuracy: 0.4619 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5678 - accuracy: 0.4558 - val_loss: 1.5532 - val_accuracy: 0.4621 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5677 - accuracy: 0.4557 - val_loss: 1.5531 - val_accuracy: 0.4620 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5676 - accuracy: 0.4555 - val_loss: 1.5530 - val_accuracy: 0.4624 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5675 - accuracy: 0.4556 - val_loss: 1.5528 - val_accuracy: 0.4624 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5673 - accuracy: 0.4556 - val_loss: 1.5527 - val_accuracy: 0.4625 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5672 - accuracy: 0.4555 - val_loss: 1.5527 - val_accuracy: 0.4623 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5671 - accuracy: 0.4552 - val_loss: 1.5525 - val_accuracy: 0.4622 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5670 - accuracy: 0.4553 - val_loss: 1.5524 - val_accuracy: 0.4622 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5669 - accuracy: 0.4554 - val_loss: 1.5523 - val_accuracy: 0.4622 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5668 - accuracy: 0.4553 - val_loss: 1.5522 - val_accuracy: 0.4623 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5667 - accuracy: 0.4551 - val_loss: 1.5521 - val_accuracy: 0.4627 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5666 - accuracy: 0.4551 - val_loss: 1.5520 - val_accuracy: 0.4627 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5665 - accuracy: 0.4549 - val_loss: 1.5520 - val_accuracy: 0.4635 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5664 - accuracy: 0.4549 - val_loss: 1.5519 - val_accuracy: 0.4637 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 4s 17ms/step - loss: 1.5663 - accuracy: 0.4547 - val_loss: 1.5517 - val_accuracy: 0.4634 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5662 - accuracy: 0.4548 - val_loss: 1.5516 - val_accuracy: 0.4638 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5661 - accuracy: 0.4547 - val_loss: 1.5516 - val_accuracy: 0.4643 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5660 - accuracy: 0.4547 - val_loss: 1.5515 - val_accuracy: 0.4643 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5659 - accuracy: 0.4546 - val_loss: 1.5513 - val_accuracy: 0.4643 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5658 - accuracy: 0.4545 - val_loss: 1.5512 - val_accuracy: 0.4649 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5657 - accuracy: 0.4545 - val_loss: 1.5512 - val_accuracy: 0.4647 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5656 - accuracy: 0.4543 - val_loss: 1.5511 - val_accuracy: 0.4645 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5656 - accuracy: 0.4543 - val_loss: 1.5510 - val_accuracy: 0.4642 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5655 - accuracy: 0.4542 - val_loss: 1.5509 - val_accuracy: 0.4643 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5654 - accuracy: 0.4541 - val_loss: 1.5508 - val_accuracy: 0.4645 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5653 - accuracy: 0.4539 - val_loss: 1.5507 - val_accuracy: 0.4647 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5652 - accuracy: 0.4541 - val_loss: 1.5508 - val_accuracy: 0.4647 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5652 - accuracy: 0.4538 - val_loss: 1.5506 - val_accuracy: 0.4647 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5651 - accuracy: 0.4538 - val_loss: 1.5505 - val_accuracy: 0.4648 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5650 - accuracy: 0.4539 - val_loss: 1.5505 - val_accuracy: 0.4649 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5650 - accuracy: 0.4539 - val_loss: 1.5505 - val_accuracy: 0.4646 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5649 - accuracy: 0.4536 - val_loss: 1.5504 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 11ms/step - loss: 1.5648 - accuracy: 0.4537 - val_loss: 1.5504 - val_accuracy: 0.4652 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5647 - accuracy: 0.4537 - val_loss: 1.5503 - val_accuracy: 0.4652 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5647 - accuracy: 0.4538 - val_loss: 1.5502 - val_accuracy: 0.4652 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5646 - accuracy: 0.4536 - val_loss: 1.5501 - val_accuracy: 0.4653 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5645 - accuracy: 0.4534 - val_loss: 1.5500 - val_accuracy: 0.4657 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 11ms/step - loss: 1.5645 - accuracy: 0.4535 - val_loss: 1.5500 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5644 - accuracy: 0.4535 - val_loss: 1.5499 - val_accuracy: 0.4652 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5644 - accuracy: 0.4533 - val_loss: 1.5499 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5643 - accuracy: 0.4533 - val_loss: 1.5499 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5642 - accuracy: 0.4533 - val_loss: 1.5498 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5641 - accuracy: 0.4532 - val_loss: 1.5498 - val_accuracy: 0.4653 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5641 - accuracy: 0.4533 - val_loss: 1.5497 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5641 - accuracy: 0.4534 - val_loss: 1.5496 - val_accuracy: 0.4653 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5640 - accuracy: 0.4532 - val_loss: 1.5497 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5639 - accuracy: 0.4534 - val_loss: 1.5495 - val_accuracy: 0.4653 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5639 - accuracy: 0.4533 - val_loss: 1.5495 - val_accuracy: 0.4653 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5638 - accuracy: 0.4534 - val_loss: 1.5495 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5637 - accuracy: 0.4532 - val_loss: 1.5494 - val_accuracy: 0.4656 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 3s 11ms/step - loss: 1.5637 - accuracy: 0.4533 - val_loss: 1.5494 - val_accuracy: 0.4655 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5636 - accuracy: 0.4532 - val_loss: 1.5493 - val_accuracy: 0.4654 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5636 - accuracy: 0.4534 - val_loss: 1.5493 - val_accuracy: 0.4653 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5636 - accuracy: 0.4534 - val_loss: 1.5493 - val_accuracy: 0.4652 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5635 - accuracy: 0.4534 - val_loss: 1.5492 - val_accuracy: 0.4651 [-0. -0. -0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 1/200 235/235 [==============================] - 5s 16ms/step - loss: 2.1563 - accuracy: 0.9278 - val_loss: 1.5487 - val_accuracy: 0.7609 Epoch 2/200 235/235 [==============================] - 4s 15ms/step - loss: 0.4329 - accuracy: 0.9586 - val_loss: 0.4927 - val_accuracy: 0.9365 Epoch 3/200 235/235 [==============================] - 4s 15ms/step - loss: 0.3091 - accuracy: 0.9628 - val_loss: 0.3538 - val_accuracy: 0.9429 Epoch 4/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2810 - accuracy: 0.9647 - val_loss: 0.3075 - val_accuracy: 0.9512 Epoch 5/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2640 - accuracy: 0.9672 - val_loss: 0.2891 - val_accuracy: 0.9564 Epoch 6/200 235/235 [==============================] - 4s 16ms/step - loss: 0.2530 - accuracy: 0.9683 - val_loss: 0.3675 - val_accuracy: 0.9282- loss: 0.2 Epoch 7/200 235/235 [==============================] - 3s 15ms/step - loss: 0.2438 - accuracy: 0.9685 - val_loss: 0.3105 - val_accuracy: 0.9434 Epoch 8/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2342 - accuracy: 0.9695 - val_loss: 0.2781 - val_accuracy: 0.9518 Epoch 9/200 235/235 [==============================] - 4s 16ms/step - loss: 0.2314 - accuracy: 0.9697 - val_loss: 0.2811 - val_accuracy: 0.9516 Epoch 10/200 235/235 [==============================] - 4s 16ms/step - loss: 0.2200 - accuracy: 0.9702 - val_loss: 0.2590 - val_accuracy: 0.9568 Epoch 11/200 235/235 [==============================] - 4s 17ms/step - loss: 0.2146 - accuracy: 0.9713 - val_loss: 0.2549 - val_accuracy: 0.9554 Epoch 12/200 235/235 [==============================] - 4s 18ms/step - loss: 0.2103 - accuracy: 0.9712 - val_loss: 0.2408 - val_accuracy: 0.9619 Epoch 13/200 235/235 [==============================] - 4s 17ms/step - loss: 0.2058 - accuracy: 0.9715 - val_loss: 0.2518 - val_accuracy: 0.9568 Epoch 14/200 235/235 [==============================] - 4s 19ms/step - loss: 0.2006 - accuracy: 0.9728 - val_loss: 0.2371 - val_accuracy: 0.9607 Epoch 15/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1993 - accuracy: 0.9726 - val_loss: 0.2331 - val_accuracy: 0.9623 Epoch 16/200 235/235 [==============================] - 4s 16ms/step - loss: 0.2019 - accuracy: 0.9709 - val_loss: 0.2733 - val_accuracy: 0.9453 Epoch 17/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1963 - accuracy: 0.9721 - val_loss: 0.2418 - val_accuracy: 0.9584 Epoch 18/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1880 - accuracy: 0.9740 - val_loss: 0.2775 - val_accuracy: 0.9437 Epoch 19/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1914 - accuracy: 0.9728 - val_loss: 0.2290 - val_accuracy: 0.9606 Epoch 20/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1885 - accuracy: 0.9734 - val_loss: 0.2374 - val_accuracy: 0.9568 Epoch 21/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1857 - accuracy: 0.9728 - val_loss: 0.2321 - val_accuracy: 0.9580 Epoch 22/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1855 - accuracy: 0.9732 - val_loss: 0.2163 - val_accuracy: 0.9651 Epoch 23/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1825 - accuracy: 0.9736 - val_loss: 0.2319 - val_accuracy: 0.9600 Epoch 24/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1786 - accuracy: 0.9739 - val_loss: 0.2428 - val_accuracy: 0.9542 Epoch 25/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1804 - accuracy: 0.9736 - val_loss: 0.2278 - val_accuracy: 0.9598 Epoch 26/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1770 - accuracy: 0.9733 - val_loss: 0.2115 - val_accuracy: 0.9639 Epoch 27/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1736 - accuracy: 0.9747 - val_loss: 0.2142 - val_accuracy: 0.9624 Epoch 28/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1702 - accuracy: 0.9748 - val_loss: 0.1980 - val_accuracy: 0.9678 Epoch 29/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1720 - accuracy: 0.9740 - val_loss: 0.2170 - val_accuracy: 0.9591 Epoch 30/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1715 - accuracy: 0.9741 - val_loss: 0.2142 - val_accuracy: 0.9614 Epoch 31/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1695 - accuracy: 0.9752 - val_loss: 0.2019 - val_accuracy: 0.9662 Epoch 32/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1690 - accuracy: 0.9749 - val_loss: 0.2369 - val_accuracy: 0.9528 Epoch 33/200 235/235 [==============================] - 5s 22ms/step - loss: 0.1675 - accuracy: 0.9750 - val_loss: 0.2308 - val_accuracy: 0.9551 Epoch 34/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1663 - accuracy: 0.9750 - val_loss: 0.1973 - val_accuracy: 0.9653 Epoch 35/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1641 - accuracy: 0.9756 - val_loss: 0.2213 - val_accuracy: 0.9591 Epoch 36/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1673 - accuracy: 0.9751 - val_loss: 0.2254 - val_accuracy: 0.9565 Epoch 37/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1642 - accuracy: 0.9754 - val_loss: 0.2350 - val_accuracy: 0.9533 Epoch 38/200 235/235 [==============================] - 3s 12ms/step - loss: 0.1634 - accuracy: 0.9757 - val_loss: 0.2080 - val_accuracy: 0.9637 Epoch 39/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1659 - accuracy: 0.9750 - val_loss: 0.2270 - val_accuracy: 0.9589 Epoch 40/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1649 - accuracy: 0.9749 - val_loss: 0.2151 - val_accuracy: 0.9628 Epoch 41/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1609 - accuracy: 0.9759 - val_loss: 0.2335 - val_accuracy: 0.9563 Epoch 42/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1632 - accuracy: 0.9752 - val_loss: 0.2220 - val_accuracy: 0.9582 Epoch 43/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1631 - accuracy: 0.9755 - val_loss: 0.2194 - val_accuracy: 0.9640 Epoch 44/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9752 - val_loss: 0.2003 - val_accuracy: 0.9657 Epoch 45/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1605 - accuracy: 0.9758 - val_loss: 0.2410 - val_accuracy: 0.9534 Epoch 46/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1623 - accuracy: 0.9754 - val_loss: 0.2012 - val_accuracy: 0.9660 Epoch 47/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1600 - accuracy: 0.9763 - val_loss: 0.2303 - val_accuracy: 0.9549 Epoch 48/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1569 - accuracy: 0.9774 - val_loss: 0.2149 - val_accuracy: 0.9629 Epoch 49/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1590 - accuracy: 0.9762 - val_loss: 0.2601 - val_accuracy: 0.9483 Epoch 50/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1613 - accuracy: 0.9761 - val_loss: 0.2183 - val_accuracy: 0.9584 Epoch 51/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1563 - accuracy: 0.9771 - val_loss: 0.2606 - val_accuracy: 0.9436 Epoch 52/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1585 - accuracy: 0.9760 - val_loss: 0.2201 - val_accuracy: 0.9570 Epoch 53/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1625 - accuracy: 0.9756 - val_loss: 0.2679 - val_accuracy: 0.9459 Epoch 54/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1584 - accuracy: 0.9765 - val_loss: 0.2247 - val_accuracy: 0.9588 Epoch 55/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1611 - accuracy: 0.9761 - val_loss: 0.2229 - val_accuracy: 0.9591 Epoch 56/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1568 - accuracy: 0.9769 - val_loss: 0.2196 - val_accuracy: 0.9608 Epoch 57/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1591 - accuracy: 0.9763 - val_loss: 0.2238 - val_accuracy: 0.9583 Epoch 58/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1576 - accuracy: 0.9768 - val_loss: 0.2122 - val_accuracy: 0.9595 Epoch 59/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1566 - accuracy: 0.9763 - val_loss: 0.2427 - val_accuracy: 0.9515 Epoch 60/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1599 - accuracy: 0.9761 - val_loss: 0.2005 - val_accuracy: 0.9634 Epoch 61/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1560 - accuracy: 0.9764 - val_loss: 0.1895 - val_accuracy: 0.9668 Epoch 62/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1552 - accuracy: 0.9769 - val_loss: 0.2209 - val_accuracy: 0.9567 Epoch 63/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1578 - accuracy: 0.9764 - val_loss: 0.2241 - val_accuracy: 0.9580 Epoch 64/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1550 - accuracy: 0.9768 - val_loss: 0.2036 - val_accuracy: 0.9636 Epoch 65/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1573 - accuracy: 0.9761 - val_loss: 0.2113 - val_accuracy: 0.9605 Epoch 66/200 235/235 [==============================] - 4s 17ms/step - loss: 0.1573 - accuracy: 0.9768 - val_loss: 0.2026 - val_accuracy: 0.9647 Epoch 67/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1558 - accuracy: 0.9768 - val_loss: 0.2148 - val_accuracy: 0.9592 Epoch 68/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1535 - accuracy: 0.9780 - val_loss: 0.2248 - val_accuracy: 0.9557 Epoch 69/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1528 - accuracy: 0.9778 - val_loss: 0.2044 - val_accuracy: 0.9640 Epoch 70/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1529 - accuracy: 0.9772 - val_loss: 0.2182 - val_accuracy: 0.9576 Epoch 71/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1526 - accuracy: 0.9772 - val_loss: 0.1934 - val_accuracy: 0.9653 Epoch 72/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1525 - accuracy: 0.9770 - val_loss: 0.2119 - val_accuracy: 0.9581 Epoch 73/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1567 - accuracy: 0.9762 - val_loss: 0.2232 - val_accuracy: 0.9585 Epoch 74/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1537 - accuracy: 0.9779 - val_loss: 0.2287 - val_accuracy: 0.9562 Epoch 75/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1545 - accuracy: 0.9771 - val_loss: 0.2028 - val_accuracy: 0.9621 Epoch 76/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1546 - accuracy: 0.9769 - val_loss: 0.1987 - val_accuracy: 0.9645 Epoch 77/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1516 - accuracy: 0.9779 - val_loss: 0.2166 - val_accuracy: 0.9571 Epoch 78/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1536 - accuracy: 0.9774 - val_loss: 0.2286 - val_accuracy: 0.9528 Epoch 79/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1499 - accuracy: 0.9778 - val_loss: 0.2278 - val_accuracy: 0.9555 Epoch 80/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1559 - accuracy: 0.9760 - val_loss: 0.2686 - val_accuracy: 0.9455 Epoch 81/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1535 - accuracy: 0.9768 - val_loss: 0.2449 - val_accuracy: 0.9506 Epoch 82/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1518 - accuracy: 0.9775 - val_loss: 0.2028 - val_accuracy: 0.9631 Epoch 83/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1528 - accuracy: 0.9772 - val_loss: 0.2105 - val_accuracy: 0.9589 Epoch 84/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1531 - accuracy: 0.9769 - val_loss: 0.2109 - val_accuracy: 0.9613 Epoch 85/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1503 - accuracy: 0.9773 - val_loss: 0.1985 - val_accuracy: 0.9638 Epoch 86/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1508 - accuracy: 0.9776 - val_loss: 0.2155 - val_accuracy: 0.9585 Epoch 87/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1529 - accuracy: 0.9765 - val_loss: 0.2003 - val_accuracy: 0.9646 Epoch 88/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1498 - accuracy: 0.9777 - val_loss: 0.2252 - val_accuracy: 0.9548 Epoch 89/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1521 - accuracy: 0.9771 - val_loss: 0.2159 - val_accuracy: 0.9585 Epoch 90/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1523 - accuracy: 0.9765 - val_loss: 0.2400 - val_accuracy: 0.9491 Epoch 91/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1505 - accuracy: 0.9778 - val_loss: 0.2337 - val_accuracy: 0.9546 Epoch 92/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1510 - accuracy: 0.9773 - val_loss: 0.1999 - val_accuracy: 0.9621 Epoch 93/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1492 - accuracy: 0.9773 - val_loss: 0.2199 - val_accuracy: 0.9588 Epoch 94/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1534 - accuracy: 0.9768 - val_loss: 0.2206 - val_accuracy: 0.9588 Epoch 95/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1509 - accuracy: 0.9771 - val_loss: 0.2080 - val_accuracy: 0.9590 Epoch 96/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1478 - accuracy: 0.9779 - val_loss: 0.2137 - val_accuracy: 0.9602 Epoch 97/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1502 - accuracy: 0.9766 - val_loss: 0.2033 - val_accuracy: 0.9625 Epoch 98/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1510 - accuracy: 0.9773 - val_loss: 0.2154 - val_accuracy: 0.9585 Epoch 99/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1487 - accuracy: 0.9777 - val_loss: 0.2229 - val_accuracy: 0.9546 Epoch 100/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1495 - accuracy: 0.9773 - val_loss: 0.2044 - val_accuracy: 0.9608 Epoch 101/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1514 - accuracy: 0.9773 - val_loss: 0.2297 - val_accuracy: 0.9527 Epoch 102/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1540 - accuracy: 0.9770 - val_loss: 0.1960 - val_accuracy: 0.9642 Epoch 103/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1515 - accuracy: 0.9769 - val_loss: 0.2105 - val_accuracy: 0.9600 Epoch 104/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1482 - accuracy: 0.9775 - val_loss: 0.1968 - val_accuracy: 0.9647 Epoch 105/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1508 - accuracy: 0.9769 - val_loss: 0.2294 - val_accuracy: 0.9564 Epoch 106/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1474 - accuracy: 0.9777 - val_loss: 0.2026 - val_accuracy: 0.9621 Epoch 107/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9778 - val_loss: 0.2028 - val_accuracy: 0.9614 Epoch 108/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1490 - accuracy: 0.9771 - val_loss: 0.2248 - val_accuracy: 0.9555 Epoch 109/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1502 - accuracy: 0.9768 - val_loss: 0.2030 - val_accuracy: 0.9640 Epoch 110/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1458 - accuracy: 0.9781 - val_loss: 0.2152 - val_accuracy: 0.9562 Epoch 111/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1472 - accuracy: 0.9779 - val_loss: 0.2072 - val_accuracy: 0.9607 Epoch 112/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1538 - accuracy: 0.9758 - val_loss: 0.2200 - val_accuracy: 0.9563 Epoch 113/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1467 - accuracy: 0.9786 - val_loss: 0.1828 - val_accuracy: 0.9676 Epoch 114/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1483 - accuracy: 0.9774 - val_loss: 0.1973 - val_accuracy: 0.9617 Epoch 115/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1508 - accuracy: 0.9766 - val_loss: 0.1906 - val_accuracy: 0.9655 Epoch 116/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9777 - val_loss: 0.1844 - val_accuracy: 0.9685 Epoch 117/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1491 - accuracy: 0.9770 - val_loss: 0.2164 - val_accuracy: 0.9576 Epoch 118/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1463 - accuracy: 0.9783 - val_loss: 0.2037 - val_accuracy: 0.9609 Epoch 119/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1514 - accuracy: 0.9766 - val_loss: 0.2229 - val_accuracy: 0.9558 Epoch 120/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1450 - accuracy: 0.9785 - val_loss: 0.2491 - val_accuracy: 0.9474 Epoch 121/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1490 - accuracy: 0.9768 - val_loss: 0.2173 - val_accuracy: 0.9577 Epoch 122/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1488 - accuracy: 0.9774 - val_loss: 0.2717 - val_accuracy: 0.9434 Epoch 123/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1476 - accuracy: 0.9774 - val_loss: 0.1853 - val_accuracy: 0.9661 Epoch 124/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1504 - accuracy: 0.9764 - val_loss: 0.2088 - val_accuracy: 0.9593 Epoch 125/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1461 - accuracy: 0.9778 - val_loss: 0.1827 - val_accuracy: 0.9686 Epoch 126/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1464 - accuracy: 0.9775 - val_loss: 0.2138 - val_accuracy: 0.9591 Epoch 127/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1451 - accuracy: 0.9776 - val_loss: 0.1953 - val_accuracy: 0.9636 Epoch 128/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1498 - accuracy: 0.9770 - val_loss: 0.2299 - val_accuracy: 0.9550 Epoch 129/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1503 - accuracy: 0.9767 - val_loss: 0.2189 - val_accuracy: 0.9567 Epoch 130/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1459 - accuracy: 0.9782 - val_loss: 0.2325 - val_accuracy: 0.9503 Epoch 131/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1428 - accuracy: 0.9785 - val_loss: 0.2311 - val_accuracy: 0.9528 Epoch 132/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1475 - accuracy: 0.9776 - val_loss: 0.1975 - val_accuracy: 0.9629 Epoch 133/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1465 - accuracy: 0.9780 - val_loss: 0.2030 - val_accuracy: 0.9609 Epoch 134/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1454 - accuracy: 0.9780 - val_loss: 0.1966 - val_accuracy: 0.9633 Epoch 135/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1455 - accuracy: 0.9785 - val_loss: 0.2031 - val_accuracy: 0.9619 Epoch 136/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1456 - accuracy: 0.9776 - val_loss: 0.2054 - val_accuracy: 0.9633 Epoch 137/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1462 - accuracy: 0.9781 - val_loss: 0.2271 - val_accuracy: 0.9547 Epoch 138/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1463 - accuracy: 0.9781 - val_loss: 0.2152 - val_accuracy: 0.9590 Epoch 139/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1443 - accuracy: 0.9785 - val_loss: 0.2238 - val_accuracy: 0.9544 Epoch 140/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9788 - val_loss: 0.2090 - val_accuracy: 0.9604 Epoch 141/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1491 - accuracy: 0.9768 - val_loss: 0.2307 - val_accuracy: 0.9530 Epoch 142/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1438 - accuracy: 0.9790 - val_loss: 0.2075 - val_accuracy: 0.9602 Epoch 143/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1439 - accuracy: 0.9781 - val_loss: 0.2400 - val_accuracy: 0.9511 Epoch 144/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1470 - accuracy: 0.9787 - val_loss: 0.1945 - val_accuracy: 0.9650 Epoch 145/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1413 - accuracy: 0.9792 - val_loss: 0.2201 - val_accuracy: 0.9544 Epoch 146/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1456 - accuracy: 0.9779 - val_loss: 0.2350 - val_accuracy: 0.9518 Epoch 147/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9776 - val_loss: 0.2078 - val_accuracy: 0.9599 Epoch 148/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1427 - accuracy: 0.9790 - val_loss: 0.1993 - val_accuracy: 0.9621 Epoch 149/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1466 - accuracy: 0.9779 - val_loss: 0.2055 - val_accuracy: 0.9611 Epoch 150/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1406 - accuracy: 0.9789 - val_loss: 0.2068 - val_accuracy: 0.9591 Epoch 151/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1466 - accuracy: 0.9764 - val_loss: 0.2143 - val_accuracy: 0.9590 Epoch 152/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1438 - accuracy: 0.9783 - val_loss: 0.1907 - val_accuracy: 0.9648 Epoch 153/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1444 - accuracy: 0.9782 - val_loss: 0.2316 - val_accuracy: 0.9544 Epoch 154/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1420 - accuracy: 0.9786 - val_loss: 0.1986 - val_accuracy: 0.9614 Epoch 155/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1430 - accuracy: 0.9785 - val_loss: 0.1909 - val_accuracy: 0.9662 Epoch 156/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1465 - accuracy: 0.9772 - val_loss: 0.2500 - val_accuracy: 0.9467 Epoch 157/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1416 - accuracy: 0.9782 - val_loss: 0.1955 - val_accuracy: 0.9638 Epoch 158/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1462 - accuracy: 0.9775 - val_loss: 0.2217 - val_accuracy: 0.9563 Epoch 159/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1418 - accuracy: 0.9788 - val_loss: 0.1908 - val_accuracy: 0.9654 Epoch 160/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1378 - accuracy: 0.9799 - val_loss: 0.2019 - val_accuracy: 0.9593 Epoch 161/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1464 - accuracy: 0.9776 - val_loss: 0.2128 - val_accuracy: 0.9586 Epoch 162/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1442 - accuracy: 0.9775 - val_loss: 0.2253 - val_accuracy: 0.9538 Epoch 163/200 235/235 [==============================] - 4s 16ms/step - loss: 0.1457 - accuracy: 0.9781 - val_loss: 0.2320 - val_accuracy: 0.9513 Epoch 164/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1430 - accuracy: 0.9783 - val_loss: 0.1913 - val_accuracy: 0.9646 Epoch 165/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9789 - val_loss: 0.1951 - val_accuracy: 0.9643 Epoch 166/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1440 - accuracy: 0.9783 - val_loss: 0.1924 - val_accuracy: 0.9632 Epoch 167/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1387 - accuracy: 0.9797 - val_loss: 0.2044 - val_accuracy: 0.9596 Epoch 168/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1393 - accuracy: 0.9791 - val_loss: 0.1966 - val_accuracy: 0.9627 Epoch 169/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1458 - accuracy: 0.9773 - val_loss: 0.1845 - val_accuracy: 0.9668 Epoch 170/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1391 - accuracy: 0.9789 - val_loss: 0.1934 - val_accuracy: 0.9645 Epoch 171/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1413 - accuracy: 0.9782 - val_loss: 0.2239 - val_accuracy: 0.9538 Epoch 172/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1429 - accuracy: 0.9779 - val_loss: 0.2463 - val_accuracy: 0.9495 Epoch 173/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1482 - accuracy: 0.9773 - val_loss: 0.2001 - val_accuracy: 0.9615 Epoch 174/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1394 - accuracy: 0.9793 - val_loss: 0.2169 - val_accuracy: 0.9569 Epoch 175/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9784 - val_loss: 0.1939 - val_accuracy: 0.9651 Epoch 176/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1417 - accuracy: 0.9789 - val_loss: 0.2107 - val_accuracy: 0.9580 Epoch 177/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1388 - accuracy: 0.9794 - val_loss: 0.2081 - val_accuracy: 0.9594 Epoch 178/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1379 - accuracy: 0.9796 - val_loss: 0.2102 - val_accuracy: 0.9585 Epoch 179/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9784 - val_loss: 0.2194 - val_accuracy: 0.9573 Epoch 180/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1412 - accuracy: 0.9790 - val_loss: 0.2069 - val_accuracy: 0.9586 Epoch 181/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1423 - accuracy: 0.9785 - val_loss: 0.2188 - val_accuracy: 0.9559 Epoch 182/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1426 - accuracy: 0.9787 - val_loss: 0.1921 - val_accuracy: 0.9652 Epoch 183/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1451 - accuracy: 0.9771 - val_loss: 0.1865 - val_accuracy: 0.9648 Epoch 184/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1410 - accuracy: 0.9786 - val_loss: 0.1943 - val_accuracy: 0.9638 Epoch 185/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9775 - val_loss: 0.2207 - val_accuracy: 0.9550 Epoch 186/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1399 - accuracy: 0.9789 - val_loss: 0.2148 - val_accuracy: 0.9576 Epoch 187/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1436 - accuracy: 0.9781 - val_loss: 0.2055 - val_accuracy: 0.9592 Epoch 188/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1379 - accuracy: 0.9790 - val_loss: 0.2217 - val_accuracy: 0.9554 Epoch 189/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1392 - accuracy: 0.9787 - val_loss: 0.2174 - val_accuracy: 0.9542 Epoch 190/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9786 - val_loss: 0.2188 - val_accuracy: 0.9563 Epoch 191/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1413 - accuracy: 0.9790 - val_loss: 0.1833 - val_accuracy: 0.9650 Epoch 192/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1422 - accuracy: 0.9783 - val_loss: 0.2730 - val_accuracy: 0.9364 Epoch 193/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1431 - accuracy: 0.9776 - val_loss: 0.1882 - val_accuracy: 0.9661 Epoch 194/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1436 - accuracy: 0.9790 - val_loss: 0.2011 - val_accuracy: 0.9600 Epoch 195/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1366 - accuracy: 0.9808 - val_loss: 0.1986 - val_accuracy: 0.9623 Epoch 196/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1402 - accuracy: 0.9794 - val_loss: 0.2006 - val_accuracy: 0.9609 Epoch 197/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1420 - accuracy: 0.9781 - val_loss: 0.1868 - val_accuracy: 0.9666 Epoch 198/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1408 - accuracy: 0.9789 - val_loss: 0.1953 - val_accuracy: 0.9645 Epoch 199/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1428 - accuracy: 0.9775 - val_loss: 0.1832 - val_accuracy: 0.9667 Epoch 200/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1368 - accuracy: 0.9798 - val_loss: 0.1996 - val_accuracy: 0.9619 Epoch 1/200 235/235 [==============================] - 4s 15ms/step - loss: 0.2417 - accuracy: 0.9285 - val_loss: 0.2194 - val_accuracy: 0.9534 Epoch 2/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0870 - accuracy: 0.9752 - val_loss: 0.1039 - val_accuracy: 0.9655 Epoch 3/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0505 - accuracy: 0.9862 - val_loss: 0.0899 - val_accuracy: 0.9719 Epoch 4/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0314 - accuracy: 0.9920 - val_loss: 0.0835 - val_accuracy: 0.9739 Epoch 5/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9951 - val_loss: 0.0901 - val_accuracy: 0.9731 Epoch 6/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0144 - accuracy: 0.9963 - val_loss: 0.0916 - val_accuracy: 0.9724 Epoch 7/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0132 - accuracy: 0.9969 - val_loss: 0.0872 - val_accuracy: 0.9739 Epoch 8/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.0873 - val_accuracy: 0.9762 Epoch 9/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 0.0869 - val_accuracy: 0.9765 Epoch 10/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0094 - accuracy: 0.9974 - val_loss: 0.0957 - val_accuracy: 0.9776 Epoch 11/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0074 - accuracy: 0.9980 - val_loss: 0.1009 - val_accuracy: 0.9750 Epoch 12/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0081 - accuracy: 0.9974 - val_loss: 0.0934 - val_accuracy: 0.9763 Epoch 13/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0083 - accuracy: 0.9973 - val_loss: 0.0946 - val_accuracy: 0.9787 Epoch 14/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0909 - val_accuracy: 0.9786 Epoch 15/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.0923 - val_accuracy: 0.9780 Epoch 16/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9978 - val_loss: 0.0932 - val_accuracy: 0.9777 Epoch 17/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.0963 - val_accuracy: 0.9780 Epoch 18/200 235/235 [==============================] - 4s 16ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 0.1022 - val_accuracy: 0.9765 Epoch 19/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0071 - accuracy: 0.9980 - val_loss: 0.0913 - val_accuracy: 0.9789 Epoch 20/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.1048 - val_accuracy: 0.9758 Epoch 21/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9983 - val_loss: 0.0845 - val_accuracy: 0.9808 Epoch 22/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 0.1005 - val_accuracy: 0.9783 Epoch 23/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.0781 - val_accuracy: 0.9814 Epoch 24/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0840 - val_accuracy: 0.9803 Epoch 25/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 0.0849 - val_accuracy: 0.9818 Epoch 26/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1142 - val_accuracy: 0.9751 Epoch 27/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.1148 - val_accuracy: 0.9734 Epoch 28/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0127 - accuracy: 0.9953 - val_loss: 0.1084 - val_accuracy: 0.9765 Epoch 29/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9979 - val_loss: 0.0880 - val_accuracy: 0.9806 Epoch 30/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0809 - val_accuracy: 0.9825 Epoch 31/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 0.0876 - val_accuracy: 0.9829 Epoch 32/200 235/235 [==============================] - 3s 15ms/step - loss: 9.3206e-04 - accuracy: 0.9998 - val_loss: 0.0826 - val_accuracy: 0.9834 Epoch 33/200 235/235 [==============================] - 3s 15ms/step - loss: 3.5433e-04 - accuracy: 0.9999 - val_loss: 0.0838 - val_accuracy: 0.9832 Epoch 34/200 235/235 [==============================] - 3s 15ms/step - loss: 3.1421e-04 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9837 Epoch 35/200 235/235 [==============================] - 3s 15ms/step - loss: 1.1686e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9840 Epoch 36/200 235/235 [==============================] - 3s 15ms/step - loss: 8.4656e-05 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 0.9843 Epoch 37/200 235/235 [==============================] - 3s 15ms/step - loss: 6.8309e-05 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9842 Epoch 38/200 235/235 [==============================] - 3s 15ms/step - loss: 6.3141e-05 - accuracy: 1.0000 - val_loss: 0.0783 - val_accuracy: 0.9846 Epoch 39/200 235/235 [==============================] - 3s 14ms/step - loss: 9.6400e-05 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9839 Epoch 40/200 235/235 [==============================] - 3s 13ms/step - loss: 6.9066e-04 - accuracy: 0.9998 - val_loss: 0.0954 - val_accuracy: 0.9823 Epoch 41/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0327 - accuracy: 0.9897 - val_loss: 0.1552 - val_accuracy: 0.9666 Epoch 42/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0150 - accuracy: 0.9949 - val_loss: 0.0836 - val_accuracy: 0.9812 Epoch 43/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.0782 - val_accuracy: 0.9821 Epoch 44/200 235/235 [==============================] - 3s 12ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0755 - val_accuracy: 0.9837 Epoch 45/200 235/235 [==============================] - 3s 15ms/step - loss: 4.7189e-04 - accuracy: 1.0000 - val_loss: 0.0732 - val_accuracy: 0.9837 Epoch 46/200 235/235 [==============================] - 3s 15ms/step - loss: 2.5655e-04 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9840 Epoch 47/200 235/235 [==============================] - 3s 14ms/step - loss: 2.4572e-04 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9839 Epoch 48/200 235/235 [==============================] - 3s 14ms/step - loss: 1.9183e-04 - accuracy: 1.0000 - val_loss: 0.0719 - val_accuracy: 0.9845 Epoch 49/200 235/235 [==============================] - 3s 14ms/step - loss: 1.2318e-04 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9843 Epoch 50/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0600e-04 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9844 Epoch 51/200 235/235 [==============================] - 3s 15ms/step - loss: 8.4569e-05 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9846 Epoch 52/200 235/235 [==============================] - 3s 15ms/step - loss: 8.0213e-05 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9848 Epoch 53/200 235/235 [==============================] - 3s 14ms/step - loss: 6.7946e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9848 Epoch 54/200 235/235 [==============================] - 3s 15ms/step - loss: 5.7373e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9845 Epoch 55/200 235/235 [==============================] - 3s 15ms/step - loss: 5.4949e-05 - accuracy: 1.0000 - val_loss: 0.0743 - val_accuracy: 0.9846 Epoch 56/200 235/235 [==============================] - 3s 15ms/step - loss: 5.1014e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9838 Epoch 57/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 0.1949 - val_accuracy: 0.9594 Epoch 58/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0177 - accuracy: 0.9944 - val_loss: 0.0776 - val_accuracy: 0.9818 Epoch 59/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.0703 - val_accuracy: 0.9822 Epoch 60/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0691 - val_accuracy: 0.9841 Epoch 61/200 235/235 [==============================] - 3s 14ms/step - loss: 3.9402e-04 - accuracy: 1.0000 - val_loss: 0.0687 - val_accuracy: 0.9843 Epoch 62/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3301e-04 - accuracy: 1.0000 - val_loss: 0.0683 - val_accuracy: 0.9841 Epoch 63/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5572e-04 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 0.9839 Epoch 64/200 235/235 [==============================] - 3s 14ms/step - loss: 1.2315e-04 - accuracy: 1.0000 - val_loss: 0.0696 - val_accuracy: 0.9842 Epoch 65/200 235/235 [==============================] - 3s 14ms/step - loss: 1.5173e-04 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9840 Epoch 66/200 235/235 [==============================] - 3s 14ms/step - loss: 7.6259e-04 - accuracy: 0.9998 - val_loss: 0.0924 - val_accuracy: 0.9793 Epoch 67/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.1030 - val_accuracy: 0.9786 Epoch 68/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9977 - val_loss: 0.0930 - val_accuracy: 0.9801 Epoch 69/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9985 - val_loss: 0.0801 - val_accuracy: 0.9822 Epoch 70/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0832 - val_accuracy: 0.9819 Epoch 71/200 235/235 [==============================] - 3s 14ms/step - loss: 7.3600e-04 - accuracy: 0.9998 - val_loss: 0.0723 - val_accuracy: 0.9850 Epoch 72/200 235/235 [==============================] - 3s 14ms/step - loss: 4.1038e-04 - accuracy: 0.9999 - val_loss: 0.0739 - val_accuracy: 0.9849 Epoch 73/200 235/235 [==============================] - 3s 14ms/step - loss: 1.2531e-04 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9851 Epoch 74/200 235/235 [==============================] - 3s 14ms/step - loss: 1.1531e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9853 Epoch 75/200 235/235 [==============================] - 3s 14ms/step - loss: 7.8246e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9848 Epoch 76/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0678e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9842 Epoch 77/200 235/235 [==============================] - 4s 15ms/step - loss: 1.5456e-04 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9848 Epoch 78/200 235/235 [==============================] - 3s 15ms/step - loss: 1.6944e-04 - accuracy: 0.9999 - val_loss: 0.0795 - val_accuracy: 0.9846 Epoch 79/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1358 - val_accuracy: 0.9715 Epoch 80/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0108 - accuracy: 0.9965 - val_loss: 0.0961 - val_accuracy: 0.9821 Epoch 81/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9987 - val_loss: 0.0927 - val_accuracy: 0.9824 Epoch 82/200 235/235 [==============================] - 3s 14ms/step - loss: 9.8481e-04 - accuracy: 0.9997 - val_loss: 0.0861 - val_accuracy: 0.9831 Epoch 83/200 235/235 [==============================] - 3s 14ms/step - loss: 5.7218e-04 - accuracy: 0.9998 - val_loss: 0.0806 - val_accuracy: 0.9851 Epoch 84/200 235/235 [==============================] - 3s 14ms/step - loss: 3.7279e-04 - accuracy: 1.0000 - val_loss: 0.0806 - val_accuracy: 0.9838 Epoch 85/200 235/235 [==============================] - 3s 14ms/step - loss: 6.1142e-04 - accuracy: 0.9998 - val_loss: 0.0798 - val_accuracy: 0.9845 Epoch 86/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0894 - val_accuracy: 0.9833 Epoch 87/200 235/235 [==============================] - 3s 15ms/step - loss: 9.4859e-04 - accuracy: 0.9998 - val_loss: 0.0846 - val_accuracy: 0.9845 Epoch 88/200 235/235 [==============================] - 3s 14ms/step - loss: 3.0982e-04 - accuracy: 0.9999 - val_loss: 0.0834 - val_accuracy: 0.9849 Epoch 89/200 235/235 [==============================] - 3s 15ms/step - loss: 2.8228e-04 - accuracy: 0.9999 - val_loss: 0.0851 - val_accuracy: 0.9844 Epoch 90/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3257e-04 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9840 Epoch 91/200 235/235 [==============================] - 3s 15ms/step - loss: 1.7209e-04 - accuracy: 0.9999 - val_loss: 0.0867 - val_accuracy: 0.9845 Epoch 92/200 235/235 [==============================] - 3s 15ms/step - loss: 1.8568e-04 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9834 Epoch 93/200 235/235 [==============================] - 4s 15ms/step - loss: 3.7085e-04 - accuracy: 0.9999 - val_loss: 0.0895 - val_accuracy: 0.9831 Epoch 94/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.1246 - val_accuracy: 0.9768 Epoch 95/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.1030 - val_accuracy: 0.9826 Epoch 96/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.0932 - val_accuracy: 0.9835 Epoch 97/200 235/235 [==============================] - 3s 15ms/step - loss: 8.5810e-04 - accuracy: 0.9998 - val_loss: 0.0910 - val_accuracy: 0.9842 Epoch 98/200 235/235 [==============================] - 3s 15ms/step - loss: 3.7517e-04 - accuracy: 0.9999 - val_loss: 0.0883 - val_accuracy: 0.9853 Epoch 99/200 235/235 [==============================] - 3s 14ms/step - loss: 1.8331e-04 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9853 Epoch 100/200 235/235 [==============================] - 3s 15ms/step - loss: 7.3496e-05 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9852 Epoch 101/200 235/235 [==============================] - 3s 14ms/step - loss: 8.6417e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9855 Epoch 102/200 235/235 [==============================] - 3s 14ms/step - loss: 5.0333e-05 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 0.9858 Epoch 103/200 235/235 [==============================] - 3s 14ms/step - loss: 3.7029e-05 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9857 Epoch 104/200 235/235 [==============================] - 3s 14ms/step - loss: 1.2491e-04 - accuracy: 0.9999 - val_loss: 0.0928 - val_accuracy: 0.9836 Epoch 105/200 235/235 [==============================] - 3s 14ms/step - loss: 1.8845e-04 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9846 Epoch 106/200 235/235 [==============================] - 3s 15ms/step - loss: 6.5816e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9844 Epoch 107/200 235/235 [==============================] - 3s 15ms/step - loss: 3.5948e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9852 Epoch 108/200 235/235 [==============================] - 3s 14ms/step - loss: 2.7824e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9850 Epoch 109/200 235/235 [==============================] - 3s 14ms/step - loss: 3.5503e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9842 Epoch 110/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0105 - accuracy: 0.9966 - val_loss: 0.1224 - val_accuracy: 0.9768 Epoch 111/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.0979 - val_accuracy: 0.9801 Epoch 112/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0892 - val_accuracy: 0.9821 Epoch 113/200 235/235 [==============================] - 3s 14ms/step - loss: 6.4947e-04 - accuracy: 0.9999 - val_loss: 0.0912 - val_accuracy: 0.9832 Epoch 114/200 235/235 [==============================] - 3s 15ms/step - loss: 1.7188e-04 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9832 Epoch 115/200 235/235 [==============================] - 4s 15ms/step - loss: 2.6432e-04 - accuracy: 0.9999 - val_loss: 0.0851 - val_accuracy: 0.9844 Epoch 116/200 235/235 [==============================] - 3s 13ms/step - loss: 2.2968e-04 - accuracy: 0.9999 - val_loss: 0.0854 - val_accuracy: 0.9848 Epoch 117/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0776e-04 - accuracy: 1.0000 - val_loss: 0.0869 - val_accuracy: 0.9843 Epoch 118/200 235/235 [==============================] - 3s 14ms/step - loss: 7.5090e-05 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9847 Epoch 119/200 235/235 [==============================] - 3s 14ms/step - loss: 4.4949e-05 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 0.9846 Epoch 120/200 235/235 [==============================] - 3s 14ms/step - loss: 4.9686e-05 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9844 Epoch 121/200 235/235 [==============================] - 3s 14ms/step - loss: 3.3715e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9848 Epoch 122/200 235/235 [==============================] - 3s 14ms/step - loss: 3.2463e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9847 Epoch 123/200 235/235 [==============================] - 3s 14ms/step - loss: 2.5402e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9842 Epoch 124/200 235/235 [==============================] - 3s 15ms/step - loss: 5.1521e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9845 Epoch 125/200 235/235 [==============================] - 3s 14ms/step - loss: 3.8432e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9845 Epoch 126/200 235/235 [==============================] - 3s 12ms/step - loss: 2.5508e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9845 Epoch 127/200 235/235 [==============================] - 3s 14ms/step - loss: 1.6994e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9847 Epoch 128/200 235/235 [==============================] - 3s 14ms/step - loss: 1.4846e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9845 Epoch 129/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0070 - accuracy: 0.9980 - val_loss: 0.1596 - val_accuracy: 0.9736 Epoch 130/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.1036 - val_accuracy: 0.9821 Epoch 131/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.0873 - val_accuracy: 0.9845 Epoch 132/200 235/235 [==============================] - 3s 15ms/step - loss: 6.7479e-04 - accuracy: 0.9998 - val_loss: 0.0856 - val_accuracy: 0.9852 Epoch 133/200 235/235 [==============================] - 3s 14ms/step - loss: 2.2862e-04 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9851 Epoch 134/200 235/235 [==============================] - 3s 14ms/step - loss: 1.6797e-04 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9851 Epoch 135/200 235/235 [==============================] - 3s 14ms/step - loss: 2.5734e-04 - accuracy: 0.9999 - val_loss: 0.0877 - val_accuracy: 0.9841 Epoch 136/200 235/235 [==============================] - 3s 14ms/step - loss: 1.1071e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9849 Epoch 137/200 235/235 [==============================] - 3s 14ms/step - loss: 4.3735e-04 - accuracy: 0.9999 - val_loss: 0.0898 - val_accuracy: 0.9843 Epoch 138/200 235/235 [==============================] - 3s 14ms/step - loss: 1.7115e-04 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9846 Epoch 139/200 235/235 [==============================] - 3s 14ms/step - loss: 9.0449e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9849 Epoch 140/200 235/235 [==============================] - 3s 15ms/step - loss: 1.1265e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9847 Epoch 141/200 235/235 [==============================] - 3s 14ms/step - loss: 1.2293e-04 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9842 Epoch 142/200 235/235 [==============================] - 3s 14ms/step - loss: 6.1251e-05 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9841 Epoch 143/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1227 - val_accuracy: 0.9799 Epoch 144/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.1130 - val_accuracy: 0.9791 Epoch 145/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1031 - val_accuracy: 0.9816 Epoch 146/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1005 - val_accuracy: 0.9824 Epoch 147/200 235/235 [==============================] - 3s 15ms/step - loss: 3.6012e-04 - accuracy: 0.9999 - val_loss: 0.1015 - val_accuracy: 0.9823 Epoch 148/200 235/235 [==============================] - 3s 15ms/step - loss: 2.0742e-04 - accuracy: 0.9999 - val_loss: 0.1003 - val_accuracy: 0.9829 Epoch 149/200 235/235 [==============================] - 3s 15ms/step - loss: 9.0095e-05 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9828 Epoch 150/200 235/235 [==============================] - 3s 14ms/step - loss: 2.3657e-04 - accuracy: 0.9999 - val_loss: 0.0995 - val_accuracy: 0.9839 Epoch 151/200 235/235 [==============================] - 3s 14ms/step - loss: 5.0496e-04 - accuracy: 0.9999 - val_loss: 0.1076 - val_accuracy: 0.9829 Epoch 152/200 235/235 [==============================] - 3s 14ms/step - loss: 7.2158e-04 - accuracy: 0.9998 - val_loss: 0.1133 - val_accuracy: 0.9812 Epoch 153/200 235/235 [==============================] - 3s 15ms/step - loss: 3.7902e-04 - accuracy: 0.9999 - val_loss: 0.1091 - val_accuracy: 0.9809 Epoch 154/200 235/235 [==============================] - 3s 14ms/step - loss: 4.6656e-04 - accuracy: 0.9999 - val_loss: 0.1188 - val_accuracy: 0.9810 Epoch 155/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9996 - val_loss: 0.1129 - val_accuracy: 0.9820 Epoch 156/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1256 - val_accuracy: 0.9786 Epoch 157/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 0.1161 - val_accuracy: 0.9802 Epoch 158/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1126 - val_accuracy: 0.9806 Epoch 159/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1133 - val_accuracy: 0.9811 Epoch 160/200 235/235 [==============================] - 3s 14ms/step - loss: 2.5914e-04 - accuracy: 0.9999 - val_loss: 0.1073 - val_accuracy: 0.9822 Epoch 161/200 235/235 [==============================] - 3s 13ms/step - loss: 6.9392e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9818 Epoch 162/200 235/235 [==============================] - 3s 15ms/step - loss: 3.7683e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9821 Epoch 163/200 235/235 [==============================] - 3s 15ms/step - loss: 2.9555e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9821 Epoch 164/200 235/235 [==============================] - 3s 14ms/step - loss: 2.2291e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9822 Epoch 165/200 235/235 [==============================] - 3s 15ms/step - loss: 2.5781e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9821 Epoch 166/200 235/235 [==============================] - 3s 15ms/step - loss: 1.7213e-05 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9823 Epoch 167/200 235/235 [==============================] - 3s 15ms/step - loss: 1.8023e-05 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9828 Epoch 168/200 235/235 [==============================] - 3s 15ms/step - loss: 1.1348e-05 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9826 Epoch 169/200 235/235 [==============================] - 3s 15ms/step - loss: 1.1937e-05 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9826 Epoch 170/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0013e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9826 Epoch 171/200 235/235 [==============================] - 3s 15ms/step - loss: 1.1729e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9829 Epoch 172/200 235/235 [==============================] - 3s 14ms/step - loss: 9.6510e-06 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9828 Epoch 173/200 235/235 [==============================] - 3s 14ms/step - loss: 8.3136e-06 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9830 Epoch 174/200 235/235 [==============================] - 3s 14ms/step - loss: 6.7108e-06 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9830 Epoch 175/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1845 - val_accuracy: 0.9705 Epoch 176/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0091 - accuracy: 0.9972 - val_loss: 0.1317 - val_accuracy: 0.9796 Epoch 177/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1098 - val_accuracy: 0.9813 Epoch 178/200 235/235 [==============================] - 3s 15ms/step - loss: 2.0586e-04 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9820 Epoch 179/200 235/235 [==============================] - 3s 15ms/step - loss: 9.7181e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9812 Epoch 180/200 235/235 [==============================] - 4s 15ms/step - loss: 1.0607e-04 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9819 Epoch 181/200 235/235 [==============================] - 4s 15ms/step - loss: 1.4671e-04 - accuracy: 0.9999 - val_loss: 0.1053 - val_accuracy: 0.9820 Epoch 182/200 235/235 [==============================] - 4s 15ms/step - loss: 4.1251e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9818 Epoch 183/200 235/235 [==============================] - 4s 16ms/step - loss: 3.0490e-05 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9823 Epoch 184/200 235/235 [==============================] - 4s 15ms/step - loss: 2.5516e-05 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9826 Epoch 185/200 235/235 [==============================] - 4s 15ms/step - loss: 3.0899e-05 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9827 Epoch 186/200 235/235 [==============================] - 4s 15ms/step - loss: 2.9537e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9827 Epoch 187/200 235/235 [==============================] - 4s 15ms/step - loss: 3.7856e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9825 Epoch 188/200 235/235 [==============================] - 4s 15ms/step - loss: 3.6901e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9830 Epoch 189/200 235/235 [==============================] - 4s 15ms/step - loss: 1.4884e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9831 Epoch 190/200 235/235 [==============================] - 4s 15ms/step - loss: 1.3319e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9832 Epoch 191/200 235/235 [==============================] - 4s 15ms/step - loss: 1.1079e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9831 Epoch 192/200 235/235 [==============================] - 4s 15ms/step - loss: 9.4640e-06 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9831 Epoch 193/200 235/235 [==============================] - 4s 15ms/step - loss: 1.0928e-05 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9834 Epoch 194/200 235/235 [==============================] - 4s 16ms/step - loss: 2.9295e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9828 Epoch 195/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0070 - accuracy: 0.9980 - val_loss: 0.1321 - val_accuracy: 0.9791 Epoch 196/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0050 - accuracy: 0.9985 - val_loss: 0.1003 - val_accuracy: 0.9829 Epoch 197/200 235/235 [==============================] - 4s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1016 - val_accuracy: 0.9826 Epoch 198/200 235/235 [==============================] - 4s 15ms/step - loss: 2.4540e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9829 Epoch 199/200 235/235 [==============================] - 4s 15ms/step - loss: 1.2677e-04 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9837 Epoch 200/200 235/235 [==============================] - 4s 15ms/step - loss: 7.2674e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9838 Epoch 1/200 235/235 [==============================] - 3s 10ms/step - loss: 1.5548 - accuracy: 0.8564 - val_loss: 0.9197 - val_accuracy: 0.9028 Epoch 2/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8712 - accuracy: 0.8970 - val_loss: 0.8255 - val_accuracy: 0.9004 Epoch 3/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8326 - accuracy: 0.8972 - val_loss: 0.8123 - val_accuracy: 0.9002 Epoch 4/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8235 - accuracy: 0.8970 - val_loss: 0.8064 - val_accuracy: 0.8986 Epoch 5/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8187 - accuracy: 0.8976 - val_loss: 0.8014 - val_accuracy: 0.8995 Epoch 6/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8149 - accuracy: 0.8977 - val_loss: 0.7993 - val_accuracy: 0.8989 Epoch 7/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8130 - accuracy: 0.8982 - val_loss: 0.7971 - val_accuracy: 0.8983 Epoch 8/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8114 - accuracy: 0.8980 - val_loss: 0.7959 - val_accuracy: 0.8990 Epoch 9/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8099 - accuracy: 0.8982 - val_loss: 0.7963 - val_accuracy: 0.8990 Epoch 10/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8094 - accuracy: 0.8983 - val_loss: 0.7947 - val_accuracy: 0.8990 Epoch 11/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8084 - accuracy: 0.8985 - val_loss: 0.7948 - val_accuracy: 0.8990 Epoch 12/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8082 - accuracy: 0.8987 - val_loss: 0.7926 - val_accuracy: 0.9002 Epoch 13/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8072 - accuracy: 0.8987 - val_loss: 0.7932 - val_accuracy: 0.8995 Epoch 14/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8071 - accuracy: 0.8990 - val_loss: 0.7912 - val_accuracy: 0.9003 Epoch 15/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8065 - accuracy: 0.8991 - val_loss: 0.7900 - val_accuracy: 0.9016 Epoch 16/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8063 - accuracy: 0.8990 - val_loss: 0.7898 - val_accuracy: 0.9015 Epoch 17/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8060 - accuracy: 0.8992 - val_loss: 0.7907 - val_accuracy: 0.9013 Epoch 18/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8059 - accuracy: 0.8993 - val_loss: 0.7903 - val_accuracy: 0.9016 Epoch 19/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8996 - val_loss: 0.7902 - val_accuracy: 0.9022 Epoch 20/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.8999 - val_loss: 0.7904 - val_accuracy: 0.9016 Epoch 21/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8053 - accuracy: 0.9000 - val_loss: 0.7890 - val_accuracy: 0.9018 Epoch 22/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8055 - accuracy: 0.8998 - val_loss: 0.7896 - val_accuracy: 0.9028 Epoch 23/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8052 - accuracy: 0.8999 - val_loss: 0.7894 - val_accuracy: 0.9023 Epoch 24/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8053 - accuracy: 0.9000 - val_loss: 0.7895 - val_accuracy: 0.9023 Epoch 25/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8051 - accuracy: 0.9000 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 26/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9003 - val_loss: 0.7884 - val_accuracy: 0.9031 Epoch 27/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8045 - accuracy: 0.9004 - val_loss: 0.7892 - val_accuracy: 0.9020 Epoch 28/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8049 - accuracy: 0.9004 - val_loss: 0.7889 - val_accuracy: 0.9030 Epoch 29/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9005 - val_loss: 0.7887 - val_accuracy: 0.9032 Epoch 30/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8047 - accuracy: 0.9005 - val_loss: 0.7894 - val_accuracy: 0.9024 Epoch 31/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8048 - accuracy: 0.9005 - val_loss: 0.7888 - val_accuracy: 0.9034 Epoch 32/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8046 - accuracy: 0.9004 - val_loss: 0.7886 - val_accuracy: 0.9028 Epoch 33/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9003 - val_loss: 0.7894 - val_accuracy: 0.9022 Epoch 34/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9027 Epoch 35/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9010 - val_loss: 0.7871 - val_accuracy: 0.9037 Epoch 36/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9025 Epoch 37/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7882 - val_accuracy: 0.9041 Epoch 38/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8043 - accuracy: 0.9008 - val_loss: 0.7887 - val_accuracy: 0.9031 Epoch 39/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9039 Epoch 40/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9036 Epoch 41/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9033 Epoch 42/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9029 Epoch 43/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9009 - val_loss: 0.7889 - val_accuracy: 0.9029 Epoch 44/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9032 Epoch 45/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8041 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9038 Epoch 46/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9030 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7873 - val_accuracy: 0.9040 Epoch 48/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9037 Epoch 49/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9033 Epoch 50/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9033 Epoch 51/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7886 - val_accuracy: 0.9030 Epoch 52/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9028 Epoch 53/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8040 - accuracy: 0.9007 - val_loss: 0.7877 - val_accuracy: 0.9041 Epoch 54/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7880 - val_accuracy: 0.9034 Epoch 55/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9036 Epoch 56/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9043 Epoch 57/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7883 - val_accuracy: 0.9039 Epoch 58/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7878 - val_accuracy: 0.9034 Epoch 59/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9034 Epoch 60/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9034 Epoch 61/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9036 Epoch 62/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9036 Epoch 63/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9033 Epoch 64/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7876 - val_accuracy: 0.9035 Epoch 65/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9032 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9015 - val_loss: 0.7872 - val_accuracy: 0.9037 Epoch 67/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9042 Epoch 68/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9042 Epoch 69/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7881 - val_accuracy: 0.9036 Epoch 70/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9032 Epoch 71/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9035 Epoch 72/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9024 Epoch 73/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9037 Epoch 74/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7875 - val_accuracy: 0.9038 Epoch 75/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9036 Epoch 76/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7892 - val_accuracy: 0.9023 Epoch 77/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9041 Epoch 78/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9033 Epoch 79/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9036 Epoch 80/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9035 Epoch 81/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9026 Epoch 82/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7881 - val_accuracy: 0.9036 Epoch 83/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9030 Epoch 84/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9032 Epoch 85/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7887 - val_accuracy: 0.9030 Epoch 86/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9044 Epoch 87/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9031 Epoch 88/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7888 - val_accuracy: 0.9033 Epoch 89/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9029 Epoch 90/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7875 - val_accuracy: 0.9032 Epoch 91/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9035 Epoch 92/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9036 Epoch 93/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7875 - val_accuracy: 0.9036 Epoch 94/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 95/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9032 Epoch 96/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 97/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9041 Epoch 98/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9040 Epoch 99/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9026 Epoch 100/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9040 Epoch 101/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7883 - val_accuracy: 0.9040 Epoch 102/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7871 - val_accuracy: 0.9038 Epoch 103/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9030 Epoch 104/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9039 Epoch 105/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9040 Epoch 106/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9028 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9041 Epoch 108/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9030 Epoch 109/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7868 - val_accuracy: 0.9037 Epoch 110/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9039 Epoch 111/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9039 Epoch 112/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9036 Epoch 113/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9037 Epoch 114/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7893 - val_accuracy: 0.9023 Epoch 115/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7883 - val_accuracy: 0.9028 Epoch 116/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9033 Epoch 117/200 235/235 [==============================] - 2s 10ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9031 Epoch 118/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9034 Epoch 119/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9033 Epoch 120/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9045 Epoch 121/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9039 Epoch 122/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9028 Epoch 123/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9039 Epoch 124/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9036 Epoch 125/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7875 - val_accuracy: 0.9038 Epoch 126/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9037 Epoch 127/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9040 Epoch 128/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7885 - val_accuracy: 0.9029 Epoch 129/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9045 Epoch 130/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7876 - val_accuracy: 0.9031 Epoch 131/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9029 Epoch 132/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9031 Epoch 133/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7880 - val_accuracy: 0.9036 Epoch 134/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7869 - val_accuracy: 0.9043 Epoch 135/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8029 - accuracy: 0.9015 - val_loss: 0.7869 - val_accuracy: 0.9045 Epoch 136/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7884 - val_accuracy: 0.9033 Epoch 137/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9039 Epoch 138/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9042 Epoch 139/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9031 Epoch 140/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9041 Epoch 141/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9025 Epoch 142/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7890 - val_accuracy: 0.9023 Epoch 143/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9033 Epoch 144/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9039 Epoch 145/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 146/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9038 Epoch 147/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7889 - val_accuracy: 0.9037 Epoch 148/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9040 Epoch 149/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9045 Epoch 150/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9039 Epoch 151/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9033 Epoch 152/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7888 - val_accuracy: 0.9038 Epoch 153/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9040 Epoch 154/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9036 Epoch 155/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7867 - val_accuracy: 0.9039 Epoch 156/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9042 Epoch 157/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 158/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9037 Epoch 159/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9041 Epoch 160/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9037 Epoch 161/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9042 Epoch 162/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9039 Epoch 163/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7888 - val_accuracy: 0.9036 Epoch 164/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9035 Epoch 165/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7874 - val_accuracy: 0.9037 Epoch 166/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9039 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9030 Epoch 168/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9042 Epoch 169/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9039 Epoch 170/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9037 Epoch 171/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9028 Epoch 172/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9042 Epoch 173/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9035 Epoch 174/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9033 Epoch 175/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9032 Epoch 176/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7881 - val_accuracy: 0.9026 Epoch 177/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7887 - val_accuracy: 0.9030 Epoch 178/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7884 - val_accuracy: 0.9032 Epoch 179/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9037 Epoch 180/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9042 Epoch 181/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8029 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9032 Epoch 182/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9031 Epoch 183/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9008 - val_loss: 0.7890 - val_accuracy: 0.9034 Epoch 184/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7885 - val_accuracy: 0.9034 Epoch 185/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7883 - val_accuracy: 0.9029 Epoch 186/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9032 Epoch 187/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7881 - val_accuracy: 0.9039 Epoch 188/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9037 Epoch 189/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9035 Epoch 190/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9049 Epoch 191/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9029 Epoch 192/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9036 Epoch 193/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9034 Epoch 194/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9039 Epoch 195/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9034 Epoch 196/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9035 Epoch 197/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9032 Epoch 198/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7880 - val_accuracy: 0.9033 Epoch 199/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9018 - val_loss: 0.7872 - val_accuracy: 0.9033 Epoch 200/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9036 Epoch 1/200 235/235 [==============================] - 3s 9ms/step - loss: 0.4636 - accuracy: 0.8702 - val_loss: 0.2482 - val_accuracy: 0.9267 Epoch 2/200 235/235 [==============================] - 2s 9ms/step - loss: 0.2260 - accuracy: 0.9349 - val_loss: 0.1879 - val_accuracy: 0.9466 Epoch 3/200 235/235 [==============================] - 2s 9ms/step - loss: 0.1718 - accuracy: 0.9494 - val_loss: 0.1556 - val_accuracy: 0.9549 Epoch 4/200 235/235 [==============================] - 2s 9ms/step - loss: 0.1382 - accuracy: 0.9593 - val_loss: 0.1365 - val_accuracy: 0.9591 Epoch 5/200 235/235 [==============================] - 2s 9ms/step - loss: 0.1141 - accuracy: 0.9670 - val_loss: 0.1245 - val_accuracy: 0.9632 Epoch 6/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0958 - accuracy: 0.9725 - val_loss: 0.1159 - val_accuracy: 0.9642 Epoch 7/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0813 - accuracy: 0.9760 - val_loss: 0.1095 - val_accuracy: 0.9663 Epoch 8/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0695 - accuracy: 0.9799 - val_loss: 0.1057 - val_accuracy: 0.9680 Epoch 9/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0598 - accuracy: 0.9828 - val_loss: 0.1035 - val_accuracy: 0.9694 Epoch 10/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0517 - accuracy: 0.9853 - val_loss: 0.1009 - val_accuracy: 0.9704 Epoch 11/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0451 - accuracy: 0.9875 - val_loss: 0.1000 - val_accuracy: 0.9702 Epoch 12/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0389 - accuracy: 0.9895 - val_loss: 0.1002 - val_accuracy: 0.9702 Epoch 13/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0337 - accuracy: 0.9911 - val_loss: 0.1004 - val_accuracy: 0.9708 Epoch 14/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0291 - accuracy: 0.9926 - val_loss: 0.1032 - val_accuracy: 0.9711 Epoch 15/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0251 - accuracy: 0.9941 - val_loss: 0.1059 - val_accuracy: 0.9705 Epoch 16/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0213 - accuracy: 0.9955 - val_loss: 0.1078 - val_accuracy: 0.9707 Epoch 17/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0184 - accuracy: 0.9963 - val_loss: 0.1124 - val_accuracy: 0.9704 Epoch 18/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0158 - accuracy: 0.9972 - val_loss: 0.1128 - val_accuracy: 0.9711 Epoch 19/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0137 - accuracy: 0.9978 - val_loss: 0.1135 - val_accuracy: 0.9714 Epoch 20/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0118 - accuracy: 0.9982 - val_loss: 0.1175 - val_accuracy: 0.9712 Epoch 21/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0107 - accuracy: 0.9985 - val_loss: 0.1152 - val_accuracy: 0.9715 Epoch 22/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0101 - accuracy: 0.9982 - val_loss: 0.1166 - val_accuracy: 0.9721 Epoch 23/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9980 - val_loss: 0.1213 - val_accuracy: 0.9706 Epoch 24/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 0.1252 - val_accuracy: 0.9722 Epoch 25/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0107 - accuracy: 0.9970 - val_loss: 0.1230 - val_accuracy: 0.9721 Epoch 26/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 0.1223 - val_accuracy: 0.9732 Epoch 27/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0067 - accuracy: 0.9984 - val_loss: 0.1164 - val_accuracy: 0.9743 Epoch 28/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0060 - accuracy: 0.9987 - val_loss: 0.1174 - val_accuracy: 0.9751 Epoch 29/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.1249 - val_accuracy: 0.9736 Epoch 30/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.1256 - val_accuracy: 0.9740 Epoch 31/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9993 - val_loss: 0.1313 - val_accuracy: 0.9718 Epoch 32/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.1371 - val_accuracy: 0.9721 Epoch 33/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0019 - accuracy: 0.9999 - val_loss: 0.1249 - val_accuracy: 0.9742 Epoch 34/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.1356 - val_accuracy: 0.9737 Epoch 35/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.1321 - val_accuracy: 0.9745 Epoch 36/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 0.9999 - val_loss: 0.1400 - val_accuracy: 0.9733 Epoch 37/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.1554 - val_accuracy: 0.9717 Epoch 38/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0130 - accuracy: 0.9956 - val_loss: 0.1454 - val_accuracy: 0.9717 Epoch 39/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 0.1359 - val_accuracy: 0.9743 Epoch 40/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.1424 - val_accuracy: 0.9746 Epoch 41/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1444 - val_accuracy: 0.9739 Epoch 42/200 235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.1372 - val_accuracy: 0.9760 Epoch 43/200 235/235 [==============================] - 2s 9ms/step - loss: 6.9030e-04 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9760 Epoch 44/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2515e-04 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9755 Epoch 45/200 235/235 [==============================] - 2s 9ms/step - loss: 3.9104e-04 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9763 Epoch 46/200 235/235 [==============================] - 2s 9ms/step - loss: 3.0156e-04 - accuracy: 1.0000 - val_loss: 0.1396 - val_accuracy: 0.9762 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 2.5831e-04 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9764 Epoch 48/200 235/235 [==============================] - 2s 10ms/step - loss: 2.2528e-04 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9765 Epoch 49/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0069e-04 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9765 Epoch 50/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8075e-04 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9765 Epoch 51/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6289e-04 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9766 Epoch 52/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4745e-04 - accuracy: 1.0000 - val_loss: 0.1442 - val_accuracy: 0.9765 Epoch 53/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3378e-04 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9765 Epoch 54/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2113e-04 - accuracy: 1.0000 - val_loss: 0.1462 - val_accuracy: 0.9766 Epoch 55/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0982e-04 - accuracy: 1.0000 - val_loss: 0.1473 - val_accuracy: 0.9768 Epoch 56/200 235/235 [==============================] - 2s 9ms/step - loss: 9.9565e-05 - accuracy: 1.0000 - val_loss: 0.1484 - val_accuracy: 0.9769 Epoch 57/200 235/235 [==============================] - 2s 8ms/step - loss: 8.9980e-05 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 0.9770 Epoch 58/200 235/235 [==============================] - 2s 9ms/step - loss: 8.1538e-05 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9770 Epoch 59/200 235/235 [==============================] - 2s 9ms/step - loss: 7.3624e-05 - accuracy: 1.0000 - val_loss: 0.1521 - val_accuracy: 0.9768 Epoch 60/200 235/235 [==============================] - 2s 9ms/step - loss: 6.6442e-05 - accuracy: 1.0000 - val_loss: 0.1533 - val_accuracy: 0.9769 Epoch 61/200 235/235 [==============================] - 2s 9ms/step - loss: 5.9909e-05 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9769 Epoch 62/200 235/235 [==============================] - 2s 9ms/step - loss: 5.3916e-05 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9766 Epoch 63/200 235/235 [==============================] - 2s 9ms/step - loss: 4.8425e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9765 Epoch 64/200 235/235 [==============================] - 2s 9ms/step - loss: 4.3482e-05 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9763 Epoch 65/200 235/235 [==============================] - 2s 9ms/step - loss: 3.8939e-05 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9763 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 3.4831e-05 - accuracy: 1.0000 - val_loss: 0.1617 - val_accuracy: 0.9762 Epoch 67/200 235/235 [==============================] - 2s 9ms/step - loss: 3.1149e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9761 Epoch 68/200 235/235 [==============================] - 2s 9ms/step - loss: 2.7816e-05 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 0.9760 Epoch 69/200 235/235 [==============================] - 2s 9ms/step - loss: 2.4778e-05 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9759 Epoch 70/200 235/235 [==============================] - 2s 9ms/step - loss: 2.2055e-05 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9759 Epoch 71/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9603e-05 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9759 Epoch 72/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7442e-05 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9758 Epoch 73/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5484e-05 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9762 Epoch 74/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3738e-05 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9761 Epoch 75/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2169e-05 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9762 Epoch 76/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0803e-05 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9760 Epoch 77/200 235/235 [==============================] - 2s 9ms/step - loss: 9.5778e-06 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9762 Epoch 78/200 235/235 [==============================] - 2s 9ms/step - loss: 8.4757e-06 - accuracy: 1.0000 - val_loss: 0.1800 - val_accuracy: 0.9763 Epoch 79/200 235/235 [==============================] - 2s 9ms/step - loss: 7.4902e-06 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9763 Epoch 80/200 235/235 [==============================] - 2s 9ms/step - loss: 6.6270e-06 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9763 Epoch 81/200 235/235 [==============================] - 2s 9ms/step - loss: 5.8574e-06 - accuracy: 1.0000 - val_loss: 0.1847 - val_accuracy: 0.9763 Epoch 82/200 235/235 [==============================] - 2s 9ms/step - loss: 5.1781e-06 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9762 Epoch 83/200 235/235 [==============================] - 2s 9ms/step - loss: 4.5803e-06 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9762 Epoch 84/200 235/235 [==============================] - 2s 9ms/step - loss: 4.0495e-06 - accuracy: 1.0000 - val_loss: 0.1895 - val_accuracy: 0.9762 Epoch 85/200 235/235 [==============================] - 2s 9ms/step - loss: 3.5778e-06 - accuracy: 1.0000 - val_loss: 0.1911 - val_accuracy: 0.9762 Epoch 86/200 235/235 [==============================] - 2s 9ms/step - loss: 3.1672e-06 - accuracy: 1.0000 - val_loss: 0.1928 - val_accuracy: 0.9762 Epoch 87/200 235/235 [==============================] - 2s 9ms/step - loss: 2.7985e-06 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9761 Epoch 88/200 235/235 [==============================] - 2s 9ms/step - loss: 2.4729e-06 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9762 Epoch 89/200 235/235 [==============================] - 2s 9ms/step - loss: 2.1841e-06 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9763 Epoch 90/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9319e-06 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9763 Epoch 91/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7084e-06 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9763 Epoch 92/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5128e-06 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9764 Epoch 93/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3339e-06 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9764 Epoch 94/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1823e-06 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9763 Epoch 95/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0465e-06 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9765 Epoch 96/200 235/235 [==============================] - 2s 9ms/step - loss: 9.2750e-07 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9764 Epoch 97/200 235/235 [==============================] - 2s 9ms/step - loss: 8.2416e-07 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9764 Epoch 98/200 235/235 [==============================] - 2s 9ms/step - loss: 7.2945e-07 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9764 Epoch 99/200 235/235 [==============================] - 2s 9ms/step - loss: 6.4697e-07 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9762 Epoch 100/200 235/235 [==============================] - 2s 9ms/step - loss: 5.7459e-07 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9763 Epoch 101/200 235/235 [==============================] - 2s 9ms/step - loss: 5.1078e-07 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9762 Epoch 102/200 235/235 [==============================] - 2s 9ms/step - loss: 4.5550e-07 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9760 Epoch 103/200 235/235 [==============================] - 2s 9ms/step - loss: 4.0522e-07 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9760 Epoch 104/200 235/235 [==============================] - 2s 9ms/step - loss: 3.6144e-07 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9759 Epoch 105/200 235/235 [==============================] - 2s 9ms/step - loss: 3.2283e-07 - accuracy: 1.0000 - val_loss: 0.2217 - val_accuracy: 0.9759 Epoch 106/200 235/235 [==============================] - 2s 9ms/step - loss: 2.8862e-07 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9759 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 2.5798e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9759 Epoch 108/200 235/235 [==============================] - 2s 9ms/step - loss: 2.3138e-07 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9760 Epoch 109/200 235/235 [==============================] - 2s 9ms/step - loss: 2.0806e-07 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9759 Epoch 110/200 235/235 [==============================] - 2s 9ms/step - loss: 1.8673e-07 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9758 Epoch 111/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6829e-07 - accuracy: 1.0000 - val_loss: 0.2297 - val_accuracy: 0.9758 Epoch 112/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5177e-07 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9758 Epoch 113/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3728e-07 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9758 Epoch 114/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2430e-07 - accuracy: 1.0000 - val_loss: 0.2333 - val_accuracy: 0.9759 Epoch 115/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1303e-07 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9759 Epoch 116/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0274e-07 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9758 Epoch 117/200 235/235 [==============================] - 2s 9ms/step - loss: 9.3704e-08 - accuracy: 1.0000 - val_loss: 0.2367 - val_accuracy: 0.9758 Epoch 118/200 235/235 [==============================] - 2s 9ms/step - loss: 8.5614e-08 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9758 Epoch 119/200 235/235 [==============================] - 2s 9ms/step - loss: 7.8291e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9758 Epoch 120/200 235/235 [==============================] - 2s 9ms/step - loss: 7.1754e-08 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9757 Epoch 121/200 235/235 [==============================] - 2s 9ms/step - loss: 6.6157e-08 - accuracy: 1.0000 - val_loss: 0.2406 - val_accuracy: 0.9758 Epoch 122/200 235/235 [==============================] - 2s 9ms/step - loss: 6.1007e-08 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9759 Epoch 123/200 235/235 [==============================] - 2s 9ms/step - loss: 5.6366e-08 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9759 Epoch 124/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2265e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9759 Epoch 125/200 235/235 [==============================] - 2s 9ms/step - loss: 4.8502e-08 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9761 Epoch 126/200 235/235 [==============================] - 2s 9ms/step - loss: 4.5258e-08 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9760 Epoch 127/200 235/235 [==============================] - 2s 9ms/step - loss: 4.2105e-08 - accuracy: 1.0000 - val_loss: 0.2457 - val_accuracy: 0.9760 Epoch 128/200 235/235 [==============================] - 2s 9ms/step - loss: 3.9486e-08 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9759 Epoch 129/200 235/235 [==============================] - 2s 9ms/step - loss: 3.6925e-08 - accuracy: 1.0000 - val_loss: 0.2470 - val_accuracy: 0.9759 Epoch 130/200 235/235 [==============================] - 2s 9ms/step - loss: 3.4688e-08 - accuracy: 1.0000 - val_loss: 0.2477 - val_accuracy: 0.9760 Epoch 131/200 235/235 [==============================] - 2s 9ms/step - loss: 3.2661e-08 - accuracy: 1.0000 - val_loss: 0.2482 - val_accuracy: 0.9759 Epoch 132/200 235/235 [==============================] - 2s 9ms/step - loss: 3.0784e-08 - accuracy: 1.0000 - val_loss: 0.2489 - val_accuracy: 0.9759 Epoch 133/200 235/235 [==============================] - 2s 9ms/step - loss: 2.8998e-08 - accuracy: 1.0000 - val_loss: 0.2495 - val_accuracy: 0.9761 Epoch 134/200 235/235 [==============================] - 2s 9ms/step - loss: 2.7517e-08 - accuracy: 1.0000 - val_loss: 0.2500 - val_accuracy: 0.9761 Epoch 135/200 235/235 [==============================] - 2s 9ms/step - loss: 2.6041e-08 - accuracy: 1.0000 - val_loss: 0.2505 - val_accuracy: 0.9760 Epoch 136/200 235/235 [==============================] - 2s 9ms/step - loss: 2.4718e-08 - accuracy: 1.0000 - val_loss: 0.2511 - val_accuracy: 0.9758 Epoch 137/200 235/235 [==============================] - 2s 9ms/step - loss: 2.3550e-08 - accuracy: 1.0000 - val_loss: 0.2516 - val_accuracy: 0.9758 Epoch 138/200 235/235 [==============================] - 2s 9ms/step - loss: 2.2391e-08 - accuracy: 1.0000 - val_loss: 0.2521 - val_accuracy: 0.9758 Epoch 139/200 235/235 [==============================] - 2s 9ms/step - loss: 2.1414e-08 - accuracy: 1.0000 - val_loss: 0.2527 - val_accuracy: 0.9757 Epoch 140/200 235/235 [==============================] - 2s 9ms/step - loss: 2.0478e-08 - accuracy: 1.0000 - val_loss: 0.2531 - val_accuracy: 0.9758 Epoch 141/200 235/235 [==============================] - 2s 9ms/step - loss: 1.9616e-08 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9758 Epoch 142/200 235/235 [==============================] - 2s 9ms/step - loss: 1.8748e-08 - accuracy: 1.0000 - val_loss: 0.2539 - val_accuracy: 0.9759 Epoch 143/200 235/235 [==============================] - 2s 9ms/step - loss: 1.8088e-08 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9759 Epoch 144/200 235/235 [==============================] - 2s 9ms/step - loss: 1.7323e-08 - accuracy: 1.0000 - val_loss: 0.2547 - val_accuracy: 0.9759 Epoch 145/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6610e-08 - accuracy: 1.0000 - val_loss: 0.2551 - val_accuracy: 0.9759 Epoch 146/200 235/235 [==============================] - 2s 9ms/step - loss: 1.6050e-08 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.9759 Epoch 147/200 235/235 [==============================] - 2s 9ms/step - loss: 1.5507e-08 - accuracy: 1.0000 - val_loss: 0.2558 - val_accuracy: 0.9759 Epoch 148/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4987e-08 - accuracy: 1.0000 - val_loss: 0.2563 - val_accuracy: 0.9758 Epoch 149/200 235/235 [==============================] - 2s 9ms/step - loss: 1.4462e-08 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9758 Epoch 150/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3989e-08 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9758 Epoch 151/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3522e-08 - accuracy: 1.0000 - val_loss: 0.2571 - val_accuracy: 0.9757 Epoch 152/200 235/235 [==============================] - 2s 9ms/step - loss: 1.3117e-08 - accuracy: 1.0000 - val_loss: 0.2574 - val_accuracy: 0.9758 Epoch 153/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2749e-08 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9759 Epoch 154/200 235/235 [==============================] - 2s 9ms/step - loss: 1.2378e-08 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9759 Epoch 155/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1998e-08 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9760 Epoch 156/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1702e-08 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9760 Epoch 157/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1406e-08 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9760 Epoch 158/200 235/235 [==============================] - 2s 9ms/step - loss: 1.1116e-08 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9760 Epoch 159/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0777e-08 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9761 Epoch 160/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0562e-08 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9761 Epoch 161/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0278e-08 - accuracy: 1.0000 - val_loss: 0.2600 - val_accuracy: 0.9760 Epoch 162/200 235/235 [==============================] - 2s 9ms/step - loss: 1.0008e-08 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9759 Epoch 163/200 235/235 [==============================] - 2s 9ms/step - loss: 9.7652e-09 - accuracy: 1.0000 - val_loss: 0.2604 - val_accuracy: 0.9759 Epoch 164/200 235/235 [==============================] - 2s 9ms/step - loss: 9.5765e-09 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9759 Epoch 165/200 235/235 [==============================] - 2s 9ms/step - loss: 9.3182e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9759 Epoch 166/200 235/235 [==============================] - 2s 9ms/step - loss: 9.1016e-09 - accuracy: 1.0000 - val_loss: 0.2610 - val_accuracy: 0.9759 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 8.8712e-09 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9759 Epoch 168/200 235/235 [==============================] - 2s 8ms/step - loss: 8.7142e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9760 Epoch 169/200 235/235 [==============================] - 2s 9ms/step - loss: 8.5394e-09 - accuracy: 1.0000 - val_loss: 0.2616 - val_accuracy: 0.9760 Epoch 170/200 235/235 [==============================] - 2s 9ms/step - loss: 8.3407e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9760 Epoch 171/200 235/235 [==============================] - 2s 9ms/step - loss: 8.1658e-09 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9760 Epoch 172/200 235/235 [==============================] - 2s 9ms/step - loss: 7.9592e-09 - accuracy: 1.0000 - val_loss: 0.2622 - val_accuracy: 0.9759 Epoch 173/200 235/235 [==============================] - 2s 9ms/step - loss: 7.8539e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9759 Epoch 174/200 235/235 [==============================] - 2s 9ms/step - loss: 7.6791e-09 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9760 Epoch 175/200 235/235 [==============================] - 2s 9ms/step - loss: 7.4983e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9760 Epoch 176/200 235/235 [==============================] - 2s 9ms/step - loss: 7.4347e-09 - accuracy: 1.0000 - val_loss: 0.2629 - val_accuracy: 0.9760 Epoch 177/200 235/235 [==============================] - 2s 9ms/step - loss: 7.2340e-09 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9760 Epoch 178/200 235/235 [==============================] - 2s 8ms/step - loss: 7.1069e-09 - accuracy: 1.0000 - val_loss: 0.2632 - val_accuracy: 0.9760 Epoch 179/200 235/235 [==============================] - 2s 9ms/step - loss: 6.9936e-09 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9760 Epoch 180/200 235/235 [==============================] - 2s 9ms/step - loss: 6.8525e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9760 Epoch 181/200 235/235 [==============================] - 2s 9ms/step - loss: 6.7512e-09 - accuracy: 1.0000 - val_loss: 0.2636 - val_accuracy: 0.9759 Epoch 182/200 235/235 [==============================] - 2s 9ms/step - loss: 6.6419e-09 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9759 Epoch 183/200 235/235 [==============================] - 2s 9ms/step - loss: 6.5466e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9759 Epoch 184/200 235/235 [==============================] - 2s 10ms/step - loss: 6.4274e-09 - accuracy: 1.0000 - val_loss: 0.2640 - val_accuracy: 0.9759 Epoch 185/200 235/235 [==============================] - 2s 9ms/step - loss: 6.3340e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9759 Epoch 186/200 235/235 [==============================] - 2s 9ms/step - loss: 6.2366e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9759 Epoch 187/200 235/235 [==============================] - 2s 9ms/step - loss: 6.1095e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9759 Epoch 188/200 235/235 [==============================] - 2s 9ms/step - loss: 5.9764e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9759 Epoch 189/200 235/235 [==============================] - 2s 9ms/step - loss: 5.9227e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9759 Epoch 190/200 235/235 [==============================] - 2s 9ms/step - loss: 5.7697e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9759 Epoch 191/200 235/235 [==============================] - 2s 9ms/step - loss: 5.7141e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9759 Epoch 192/200 235/235 [==============================] - 2s 9ms/step - loss: 5.6307e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9759 Epoch 193/200 235/235 [==============================] - 2s 9ms/step - loss: 5.5452e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9759 Epoch 194/200 235/235 [==============================] - 2s 9ms/step - loss: 5.4022e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9759 Epoch 195/200 235/235 [==============================] - 2s 9ms/step - loss: 5.3684e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9759 Epoch 196/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2730e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9760 Epoch 197/200 235/235 [==============================] - 2s 9ms/step - loss: 5.2253e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9760 Epoch 198/200 235/235 [==============================] - 2s 9ms/step - loss: 5.1598e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9760 Epoch 199/200 235/235 [==============================] - 2s 9ms/step - loss: 5.0684e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9760 Epoch 200/200 235/235 [==============================] - 2s 9ms/step - loss: 4.9571e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9760 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03726593032479286 Thresholhold 0.020722776651382446 Using suggest threshold. Applying new mask Percentage zeros 0.2775 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 1. 1. 1.] ... [1. 0. 0. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.0608268603682518 Thresholhold -0.09495096653699875 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.11545035243034363 Thresholhold 0.16133734583854675 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 5/235 [..............................] - ETA: 3s - loss: 7.6018 - accuracy: 0.4187 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0138s vs `on_train_batch_begin` time: 11.1363s). Check your callbacks. 235/235 [==============================] - 71s 14ms/step - loss: 2.2008 - accuracy: 0.9227 - val_loss: 1.6339 - val_accuracy: 0.9025 [-3.4934537e-08 -1.1419273e-07 2.6109504e-07 ... -0.0000000e+00 -1.3341206e-01 0.0000000e+00] Sparsity at: 0.24706235912847482 Epoch 2/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4417 - accuracy: 0.9603 - val_loss: 0.5500 - val_accuracy: 0.9432 [-3.9394123e-13 -1.3250265e-13 1.2746615e-12 ... -0.0000000e+00 -1.0383350e-01 0.0000000e+00] Sparsity at: 0.24706235912847482 Epoch 3/500 235/235 [==============================] - 4s 16ms/step - loss: 0.3054 - accuracy: 0.9657 - val_loss: 0.3533 - val_accuracy: 0.9449 [ 6.0108646e-19 -7.2390489e-19 3.5585930e-18 ... -0.0000000e+00 -9.5932662e-02 0.0000000e+00] Sparsity at: 0.24706235912847482 Epoch 4/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2715 - accuracy: 0.9678 - val_loss: 0.3059 - val_accuracy: 0.9507 [ 8.5233336e-24 1.0058221e-24 -2.3831149e-23 ... -0.0000000e+00 -8.7623201e-02 0.0000000e+00] Sparsity at: 0.24706235912847482 Epoch 5/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2559 - accuracy: 0.9681 - val_loss: 0.2840 - val_accuracy: 0.9584 [-2.1679155e-29 5.2759388e-29 -5.7082409e-29 ... 0.0000000e+00 -8.0072187e-02 0.0000000e+00] Sparsity at: 0.24706235912847482 Epoch 6/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2433 - accuracy: 0.9699 - val_loss: 0.2883 - val_accuracy: 0.9538 [ 1.3988824e-34 4.8135736e-34 7.1390720e-34 ... -0.0000000e+00 -7.5122505e-02 0.0000000e+00] Sparsity at: 0.24706235912847482 Epoch 7/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2339 - accuracy: 0.9707 - val_loss: 0.3604 - val_accuracy: 0.9263 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.9691218e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 8/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2222 - accuracy: 0.9725 - val_loss: 0.2502 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.4845793e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 9/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2128 - accuracy: 0.9730 - val_loss: 0.2431 - val_accuracy: 0.9612 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.8598582e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 10/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2090 - accuracy: 0.9730 - val_loss: 0.2701 - val_accuracy: 0.9544 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.5561662e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 11/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2048 - accuracy: 0.9736 - val_loss: 0.2654 - val_accuracy: 0.9530 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.6279004e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 12/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2000 - accuracy: 0.9739 - val_loss: 0.2505 - val_accuracy: 0.9553 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.0788168e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 13/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1936 - accuracy: 0.9743 - val_loss: 0.2363 - val_accuracy: 0.9585 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.6563486e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 14/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1924 - accuracy: 0.9735 - val_loss: 0.2413 - val_accuracy: 0.9554 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.0205315e-02 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 15/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1859 - accuracy: 0.9748 - val_loss: 0.2137 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.1632353e-03 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 16/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1850 - accuracy: 0.9747 - val_loss: 0.2520 - val_accuracy: 0.9524 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.3819027e-03 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 17/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1819 - accuracy: 0.9759 - val_loss: 0.2593 - val_accuracy: 0.9491: 0.1829 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -9.3547907e-03 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 18/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1795 - accuracy: 0.9753 - val_loss: 0.2383 - val_accuracy: 0.9568 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.9037499e-03 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 19/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1777 - accuracy: 0.9752 - val_loss: 0.2311 - val_accuracy: 0.9565 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 9.1937027e-04 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 20/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1754 - accuracy: 0.9749 - val_loss: 0.2151 - val_accuracy: 0.9621 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.0056236e-03 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 21/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1711 - accuracy: 0.9760 - val_loss: 0.2075 - val_accuracy: 0.9646 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.4182271e-03 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 22/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1726 - accuracy: 0.9749 - val_loss: 0.2215 - val_accuracy: 0.9625 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.3562939e-04 0.0000000e+00] Sparsity at: 0.24706611570247933 Epoch 23/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1692 - accuracy: 0.9758 - val_loss: 0.2202 - val_accuracy: 0.9609 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 5.3756922e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 24/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1713 - accuracy: 0.9754 - val_loss: 0.2036 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.0112582e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 25/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1671 - accuracy: 0.9765 - val_loss: 0.2502 - val_accuracy: 0.9491 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.1671721e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 26/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1648 - accuracy: 0.9768 - val_loss: 0.2102 - val_accuracy: 0.9620 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.7522077e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 27/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1686 - accuracy: 0.9757 - val_loss: 0.2282 - val_accuracy: 0.9597 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.8454020e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 28/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1633 - accuracy: 0.9765 - val_loss: 0.2141 - val_accuracy: 0.9589 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.1984252e-03 -0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 29/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1639 - accuracy: 0.9761 - val_loss: 0.2382 - val_accuracy: 0.9549 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.7747652e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 30/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1632 - accuracy: 0.9766 - val_loss: 0.2238 - val_accuracy: 0.9585 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -8.5075060e-04 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 31/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1615 - accuracy: 0.9763 - val_loss: 0.1989 - val_accuracy: 0.9641 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.2501156e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 32/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1638 - accuracy: 0.9757 - val_loss: 0.2184 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.9345551e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 33/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1607 - accuracy: 0.9771 - val_loss: 0.1967 - val_accuracy: 0.9655 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.5447971e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 34/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1593 - accuracy: 0.9769 - val_loss: 0.2130 - val_accuracy: 0.9623 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.3834254e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 35/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1606 - accuracy: 0.9765 - val_loss: 0.2156 - val_accuracy: 0.9602 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.0499612e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 36/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1577 - accuracy: 0.9763 - val_loss: 0.2184 - val_accuracy: 0.9574 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.2691919e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 37/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1587 - accuracy: 0.9762 - val_loss: 0.1998 - val_accuracy: 0.9646 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.4836230e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 38/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1593 - accuracy: 0.9768 - val_loss: 0.2038 - val_accuracy: 0.9632 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.5866872e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 39/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1545 - accuracy: 0.9771 - val_loss: 0.2162 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.7717341e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 40/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1570 - accuracy: 0.9771 - val_loss: 0.2628 - val_accuracy: 0.9454 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.3572663e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 41/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1571 - accuracy: 0.9769 - val_loss: 0.2181 - val_accuracy: 0.9595 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.5019919e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 42/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1535 - accuracy: 0.9777 - val_loss: 0.2283 - val_accuracy: 0.9524 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.1530629e-02 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 43/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1532 - accuracy: 0.9779 - val_loss: 0.2165 - val_accuracy: 0.9581 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.2825965e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 44/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1538 - accuracy: 0.9773 - val_loss: 0.2055 - val_accuracy: 0.9626 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.0874640e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 45/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1531 - accuracy: 0.9781 - val_loss: 0.2030 - val_accuracy: 0.9626 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.6134688e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 46/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1546 - accuracy: 0.9768 - val_loss: 0.1999 - val_accuracy: 0.9636 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.2520741e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 47/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1510 - accuracy: 0.9778 - val_loss: 0.1979 - val_accuracy: 0.9630 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.4756119e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 48/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1512 - accuracy: 0.9768 - val_loss: 0.2025 - val_accuracy: 0.9616 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.1243339e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 49/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1548 - accuracy: 0.9765 - val_loss: 0.2188 - val_accuracy: 0.9601 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.4084835e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 50/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1517 - accuracy: 0.9782 - val_loss: 0.2193 - val_accuracy: 0.9560 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.2754159e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 3.11443741857911e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.2775 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 1. 1. 1.] ... [1. 0. 0. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.00010634585146621078 Thresholhold -5.833261820953339e-05 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.01770162130969144 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 209s 13ms/step - loss: 0.1514 - accuracy: 0.9771 - val_loss: 0.2123 - val_accuracy: 0.9579 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.3187897e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 52/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1494 - accuracy: 0.9786 - val_loss: 0.2294 - val_accuracy: 0.9575 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.2013354e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 53/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1464 - accuracy: 0.9791 - val_loss: 0.2306 - val_accuracy: 0.9560 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -8.1838742e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 54/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1548 - accuracy: 0.9764 - val_loss: 0.2206 - val_accuracy: 0.9585 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.3704723e-03 0.0000000e+00] Sparsity at: 0.24706987227648386 Epoch 55/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9784 - val_loss: 0.2049 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.0981226e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 56/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1524 - accuracy: 0.9776 - val_loss: 0.2009 - val_accuracy: 0.9638 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -9.9036321e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 57/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1496 - accuracy: 0.9781 - val_loss: 0.2472 - val_accuracy: 0.9480 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.4124332e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 58/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1495 - accuracy: 0.9780 - val_loss: 0.1942 - val_accuracy: 0.9638 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.9812107e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 59/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1456 - accuracy: 0.9789 - val_loss: 0.1957 - val_accuracy: 0.9669 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.6305955e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 60/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1491 - accuracy: 0.9779 - val_loss: 0.1935 - val_accuracy: 0.9659 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.7458041e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 61/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1489 - accuracy: 0.9781 - val_loss: 0.1985 - val_accuracy: 0.9634 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.7901105e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 62/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1460 - accuracy: 0.9791 - val_loss: 0.1874 - val_accuracy: 0.9681 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.8666169e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 63/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9781 - val_loss: 0.2019 - val_accuracy: 0.9610 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -9.6983503e-04 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 64/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1479 - accuracy: 0.9783 - val_loss: 0.2158 - val_accuracy: 0.9575 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.6440653e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 65/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1455 - accuracy: 0.9789 - val_loss: 0.2065 - val_accuracy: 0.9623 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.6598024e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 66/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1463 - accuracy: 0.9788 - val_loss: 0.2110 - val_accuracy: 0.9608 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.5956312e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 67/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1451 - accuracy: 0.9786 - val_loss: 0.2618 - val_accuracy: 0.9483 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.5059080e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 68/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1461 - accuracy: 0.9791 - val_loss: 0.2124 - val_accuracy: 0.9613 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.6270677e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 69/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1477 - accuracy: 0.9777 - val_loss: 0.1955 - val_accuracy: 0.9657 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.4302301e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 70/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1464 - accuracy: 0.9790 - val_loss: 0.2023 - val_accuracy: 0.9608 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.4237037e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 71/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1459 - accuracy: 0.9786 - val_loss: 0.1983 - val_accuracy: 0.9624 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 5.8640400e-04 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 72/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9789 - val_loss: 0.2219 - val_accuracy: 0.9564 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.7833997e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 73/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1488 - accuracy: 0.9768 - val_loss: 0.2202 - val_accuracy: 0.9573 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.1084672e-04 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 74/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1454 - accuracy: 0.9790 - val_loss: 0.2078 - val_accuracy: 0.9591 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.7799969e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 75/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1454 - accuracy: 0.9785 - val_loss: 0.2152 - val_accuracy: 0.9592 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.0641828e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 76/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1478 - accuracy: 0.9776 - val_loss: 0.2364 - val_accuracy: 0.9536 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.3382218e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 77/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1470 - accuracy: 0.9780 - val_loss: 0.2323 - val_accuracy: 0.9529 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.7808198e-04 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 78/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1436 - accuracy: 0.9789 - val_loss: 0.2440 - val_accuracy: 0.9491 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.1133333e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 79/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1448 - accuracy: 0.9786 - val_loss: 0.2023 - val_accuracy: 0.9631 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.4986507e-05 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 80/500 235/235 [==============================] - 4s 18ms/step - loss: 0.1467 - accuracy: 0.9779 - val_loss: 0.1921 - val_accuracy: 0.9662 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.2541380e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 81/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1432 - accuracy: 0.9794 - val_loss: 0.1963 - val_accuracy: 0.9642 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.2242961e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 82/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1456 - accuracy: 0.9784 - val_loss: 0.2080 - val_accuracy: 0.9592 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.2049425e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 83/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9790 - val_loss: 0.2129 - val_accuracy: 0.9575 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.7972395e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 84/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1459 - accuracy: 0.9780 - val_loss: 0.2259 - val_accuracy: 0.9565 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.9458019e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 85/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1465 - accuracy: 0.9784 - val_loss: 0.2253 - val_accuracy: 0.9577 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.7476991e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 86/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1431 - accuracy: 0.9790 - val_loss: 0.2116 - val_accuracy: 0.9590 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.3705303e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 87/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1447 - accuracy: 0.9785 - val_loss: 0.2305 - val_accuracy: 0.9555 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.4280575e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 88/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1429 - accuracy: 0.9793 - val_loss: 0.2169 - val_accuracy: 0.9593 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.8564592e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 89/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1432 - accuracy: 0.9786 - val_loss: 0.1941 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.1159312e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 90/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1430 - accuracy: 0.9786 - val_loss: 0.1982 - val_accuracy: 0.9625 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.0037460e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 91/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9789 - val_loss: 0.2042 - val_accuracy: 0.9595 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.5703730e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 92/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1433 - accuracy: 0.9782 - val_loss: 0.2173 - val_accuracy: 0.9562 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.9945925e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 93/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1407 - accuracy: 0.9790 - val_loss: 0.2060 - val_accuracy: 0.9608 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.2079871e-04 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 94/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1427 - accuracy: 0.9790 - val_loss: 0.2057 - val_accuracy: 0.9593 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -8.6928289e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 95/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1432 - accuracy: 0.9789 - val_loss: 0.1992 - val_accuracy: 0.9627 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.5615275e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 96/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1403 - accuracy: 0.9789 - val_loss: 0.2480 - val_accuracy: 0.9504 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.2404095e-03 -0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 97/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9793 - val_loss: 0.2210 - val_accuracy: 0.9571 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.0043127e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 98/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9787 - val_loss: 0.2025 - val_accuracy: 0.9615 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.0183817e-04 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 99/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9795 - val_loss: 0.1967 - val_accuracy: 0.9631 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.1805954e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 100/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1410 - accuracy: 0.9793 - val_loss: 0.1988 - val_accuracy: 0.9630 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.6555770e-03 0.0000000e+00] Sparsity at: 0.24707362885048836 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 4.122906951942523e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.2775 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 1. 1. 1.] ... [1. 0. 0. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 3.661272726674736e-05 Thresholhold 1.856839162428514e-07 Using suggest threshold. Applying new mask Percentage zeros 0.42693335 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [0. 0. 1. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.02442430927259509 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 259s 15ms/step - loss: 0.1424 - accuracy: 0.9787 - val_loss: 0.2137 - val_accuracy: 0.9588 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.6190293e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 102/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1399 - accuracy: 0.9792 - val_loss: 0.2070 - val_accuracy: 0.9612 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.2047195e-02 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 103/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1433 - accuracy: 0.9788 - val_loss: 0.2258 - val_accuracy: 0.9557 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.5016566e-04 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 104/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1404 - accuracy: 0.9790 - val_loss: 0.2019 - val_accuracy: 0.9629 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.2308168e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 105/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1419 - accuracy: 0.9786 - val_loss: 0.2208 - val_accuracy: 0.9553 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.5044698e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 106/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1406 - accuracy: 0.9795 - val_loss: 0.1921 - val_accuracy: 0.9628 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.2140858e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 107/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1414 - accuracy: 0.9790 - val_loss: 0.2213 - val_accuracy: 0.9536 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.0683170e-06 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 108/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1410 - accuracy: 0.9789 - val_loss: 0.2113 - val_accuracy: 0.9587 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.3053456e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 109/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9790 - val_loss: 0.2082 - val_accuracy: 0.9619 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.7220025e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 110/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9796 - val_loss: 0.2547 - val_accuracy: 0.9486 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.8190849e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 111/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1439 - accuracy: 0.9781 - val_loss: 0.1925 - val_accuracy: 0.9628 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.4573188e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 112/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1390 - accuracy: 0.9799 - val_loss: 0.1865 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.4481458e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 113/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1400 - accuracy: 0.9791 - val_loss: 0.1870 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.0912537e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 114/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1404 - accuracy: 0.9786 - val_loss: 0.2309 - val_accuracy: 0.9541 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.4038267e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 115/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1394 - accuracy: 0.9794 - val_loss: 0.2134 - val_accuracy: 0.9577 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.1599835e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 116/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.1906 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.4207626e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 117/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1446 - accuracy: 0.9779 - val_loss: 0.2384 - val_accuracy: 0.9507 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.1002485e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 118/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1397 - accuracy: 0.9790 - val_loss: 0.2140 - val_accuracy: 0.9584 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.6717114e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 119/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9794 - val_loss: 0.1945 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.0613670e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 120/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1380 - accuracy: 0.9798 - val_loss: 0.2029 - val_accuracy: 0.9616 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.1049351e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 121/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.2113 - val_accuracy: 0.9591 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.6375917e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 122/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1367 - accuracy: 0.9795 - val_loss: 0.1939 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.3219380e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 123/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9799 - val_loss: 0.1998 - val_accuracy: 0.9623 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.7361452e-04 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 124/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1382 - accuracy: 0.9801 - val_loss: 0.1978 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 7.8910717e-04 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 125/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1422 - accuracy: 0.9786 - val_loss: 0.1801 - val_accuracy: 0.9658 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.9103604e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 126/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.1917 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.1409272e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 127/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9804 - val_loss: 0.1930 - val_accuracy: 0.9630 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.5913878e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 128/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1352 - accuracy: 0.9801 - val_loss: 0.1994 - val_accuracy: 0.9606 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1284190e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 129/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9804 - val_loss: 0.2092 - val_accuracy: 0.9584 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.4070538e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 130/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1409 - accuracy: 0.9793 - val_loss: 0.2033 - val_accuracy: 0.9615 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.1309329e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 131/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1363 - accuracy: 0.9798 - val_loss: 0.1907 - val_accuracy: 0.9631 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.9956185e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 132/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1343 - accuracy: 0.9805 - val_loss: 0.1961 - val_accuracy: 0.9637 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.6847834e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 133/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1365 - accuracy: 0.9796 - val_loss: 0.1894 - val_accuracy: 0.9658 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.6326773e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 134/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1382 - accuracy: 0.9797 - val_loss: 0.2115 - val_accuracy: 0.9587 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.2767093e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 135/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9801 - val_loss: 0.2047 - val_accuracy: 0.9609 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.3010508e-04 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 136/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1377 - accuracy: 0.9792 - val_loss: 0.1945 - val_accuracy: 0.9630 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.6367331e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 137/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9801 - val_loss: 0.2004 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.3808409e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 138/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9801 - val_loss: 0.2018 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.3965506e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 139/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9797 - val_loss: 0.2066 - val_accuracy: 0.9597 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.6131141e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 140/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9795 - val_loss: 0.1909 - val_accuracy: 0.9633 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.6020334e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 141/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1394 - accuracy: 0.9795 - val_loss: 0.2272 - val_accuracy: 0.9544 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.0293133e-02 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 142/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1398 - accuracy: 0.9786 - val_loss: 0.2467 - val_accuracy: 0.9501 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.4314309e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 143/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1392 - accuracy: 0.9791 - val_loss: 0.1846 - val_accuracy: 0.9670 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.8391220e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 144/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1320 - accuracy: 0.9810 - val_loss: 0.1963 - val_accuracy: 0.9613 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.7051181e-04 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 145/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1354 - accuracy: 0.9799 - val_loss: 0.2164 - val_accuracy: 0.9571 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.2889595e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 146/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1364 - accuracy: 0.9794 - val_loss: 0.2013 - val_accuracy: 0.9620 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.2592855e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 147/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9799 - val_loss: 0.1905 - val_accuracy: 0.9646 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.4929804e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 148/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1370 - accuracy: 0.9793 - val_loss: 0.2195 - val_accuracy: 0.9578 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.4266124e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 149/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1385 - accuracy: 0.9794 - val_loss: 0.1930 - val_accuracy: 0.9639 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.5238187e-03 -0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 150/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9799 - val_loss: 0.2034 - val_accuracy: 0.9606 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.7608323e-03 0.0000000e+00] Sparsity at: 0.2951878287002254 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 5.1083703888624145e-34 Thresholhold 1.398882358516678e-34 Using suggest threshold. Applying new mask Percentage zeros 0.43816325 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 0. 1. 1.] ... [1. 0. 0. ... 0. 1. 0.] [0. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.00043749415078420464 Thresholhold -0.00010349297372158617 Using suggest threshold. Applying new mask Percentage zeros 0.42693335 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [0. 0. 1. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 0. 1. 0.] [1. 0. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.04167799063760702 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 240s 13ms/step - loss: 0.1339 - accuracy: 0.9799 - val_loss: 0.2133 - val_accuracy: 0.9573 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.5538598e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 152/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1350 - accuracy: 0.9800 - val_loss: 0.1801 - val_accuracy: 0.9665 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.1424405e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 153/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9800 - val_loss: 0.2063 - val_accuracy: 0.9606 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.6918487e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 154/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9805 - val_loss: 0.2045 - val_accuracy: 0.9600 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 7.9647638e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 155/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9802 - val_loss: 0.1785 - val_accuracy: 0.9677 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.3168419e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 156/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1348 - accuracy: 0.9801 - val_loss: 0.1980 - val_accuracy: 0.9638 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.5486054e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 157/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1342 - accuracy: 0.9805 - val_loss: 0.2258 - val_accuracy: 0.9555 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.2501264e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 158/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9789 - val_loss: 0.2050 - val_accuracy: 0.9586 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.7099762e-05 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 159/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9805 - val_loss: 0.1899 - val_accuracy: 0.9645 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.8772400e-06 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 160/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.2060 - val_accuracy: 0.9611 [ 1.39888236e-34 4.81357360e-34 4.61340023e-34 ... -0.00000000e+00 -1.01768135e-04 0.00000000e+00] Sparsity at: 0.437129977460556 Epoch 161/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9803 - val_loss: 0.1966 - val_accuracy: 0.9632 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -7.5908692e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 162/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9801 - val_loss: 0.2166 - val_accuracy: 0.9561 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.2618256e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 163/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1361 - accuracy: 0.9794 - val_loss: 0.1957 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.5243805e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 164/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9808 - val_loss: 0.2250 - val_accuracy: 0.9539 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 8.5082080e-05 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 165/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1375 - accuracy: 0.9794 - val_loss: 0.2121 - val_accuracy: 0.9599 [ 1.39888236e-34 4.81357360e-34 4.61340023e-34 ... 0.00000000e+00 -1.19433935e-05 -0.00000000e+00] Sparsity at: 0.437129977460556 Epoch 166/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1370 - accuracy: 0.9795 - val_loss: 0.2540 - val_accuracy: 0.9475 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.7680637e-06 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 167/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9803 - val_loss: 0.2080 - val_accuracy: 0.9602 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.5087634e-07 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 168/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1374 - accuracy: 0.9797 - val_loss: 0.2182 - val_accuracy: 0.9564 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.8471492e-07 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 169/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9795 - val_loss: 0.2165 - val_accuracy: 0.9575 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.5552089e-07 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 170/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9800 - val_loss: 0.1757 - val_accuracy: 0.9681 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.3312129e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 171/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1332 - accuracy: 0.9803 - val_loss: 0.2058 - val_accuracy: 0.9595 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.0961806e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 172/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1330 - accuracy: 0.9807 - val_loss: 0.2070 - val_accuracy: 0.9591 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.8697976e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 173/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1362 - accuracy: 0.9800 - val_loss: 0.1895 - val_accuracy: 0.9643 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.6966951e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 174/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9799 - val_loss: 0.2090 - val_accuracy: 0.9594 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.2618344e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 175/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9801 - val_loss: 0.2152 - val_accuracy: 0.9592 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.0913042e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 176/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1322 - accuracy: 0.9803 - val_loss: 0.2298 - val_accuracy: 0.9562 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.2144630e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 177/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1332 - accuracy: 0.9806 - val_loss: 0.2449 - val_accuracy: 0.9476 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.7857473e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 178/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9804 - val_loss: 0.2123 - val_accuracy: 0.9584 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.8535372e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 179/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9799 - val_loss: 0.2214 - val_accuracy: 0.9570 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.7287687e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 180/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1325 - accuracy: 0.9806 - val_loss: 0.2264 - val_accuracy: 0.9551 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.5616220e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 181/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9804 - val_loss: 0.1963 - val_accuracy: 0.9625 [ 1.39888236e-34 4.81357360e-34 4.61340023e-34 ... 0.00000000e+00 -1.03262115e-04 -0.00000000e+00] Sparsity at: 0.437129977460556 Epoch 182/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1326 - accuracy: 0.9806 - val_loss: 0.2075 - val_accuracy: 0.9623 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.3878462e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 183/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1314 - accuracy: 0.9805 - val_loss: 0.2103 - val_accuracy: 0.9574 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.2966636e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 184/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1334 - accuracy: 0.9802 - val_loss: 0.2104 - val_accuracy: 0.9601 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.0827970e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 185/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9806 - val_loss: 0.2045 - val_accuracy: 0.9604 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.7618227e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 186/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9795 - val_loss: 0.2080 - val_accuracy: 0.9581 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.4654805e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 187/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1323 - accuracy: 0.9805 - val_loss: 0.2290 - val_accuracy: 0.9548 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.1959257e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 188/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1383 - accuracy: 0.9792 - val_loss: 0.2375 - val_accuracy: 0.9517 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.1622006e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 189/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1371 - accuracy: 0.9794 - val_loss: 0.2000 - val_accuracy: 0.9620 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.0593296e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 190/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1333 - accuracy: 0.9808 - val_loss: 0.2139 - val_accuracy: 0.9596 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.4119891e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 191/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9803 - val_loss: 0.1924 - val_accuracy: 0.9642 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.5651505e-05 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 192/500 235/235 [==============================] - 4s 19ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2293 - val_accuracy: 0.9551 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.6354783e-03 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 193/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1342 - accuracy: 0.9802 - val_loss: 0.2152 - val_accuracy: 0.9574 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 9.7854389e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 194/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1316 - accuracy: 0.9811 - val_loss: 0.2007 - val_accuracy: 0.9606 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.6802651e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 195/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1309 - accuracy: 0.9807 - val_loss: 0.1978 - val_accuracy: 0.9604 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.0464070e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 196/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1358 - accuracy: 0.9799 - val_loss: 0.2315 - val_accuracy: 0.9523 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.9961939e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 197/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1336 - accuracy: 0.9811 - val_loss: 0.2001 - val_accuracy: 0.9622 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.0716910e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 198/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1327 - accuracy: 0.9807 - val_loss: 0.1952 - val_accuracy: 0.9626 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.2502997e-04 0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 199/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1349 - accuracy: 0.9795 - val_loss: 0.2141 - val_accuracy: 0.9580 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.6066333e-03 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 200/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1318 - accuracy: 0.9809 - val_loss: 0.2069 - val_accuracy: 0.9592 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.3081696e-04 -0.0000000e+00] Sparsity at: 0.437129977460556 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 1.7790788624055115e-05 Thresholhold 1.398882358516678e-34 Using suggest threshold. Applying new mask Percentage zeros 0.43911564 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 0. 1. 1.] ... [1. 0. 0. ... 0. 1. 0.] [0. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.0032467699331542033 Thresholhold 6.794191904191393e-06 Using suggest threshold. Applying new mask Percentage zeros 0.7631 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 0.] [0. 0. 1. ... 0. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.05935055366467168 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 172s 12ms/step - loss: 0.1339 - accuracy: 0.9799 - val_loss: 0.2166 - val_accuracy: 0.9563 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.0841874e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 202/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1355 - accuracy: 0.9794 - val_loss: 0.1834 - val_accuracy: 0.9646 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.5259359e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9794 - val_loss: 0.2174 - val_accuracy: 0.9555 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.0823895e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9801 - val_loss: 0.2080 - val_accuracy: 0.9582 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.3064937e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9800 - val_loss: 0.1991 - val_accuracy: 0.9615 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.7059729e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9805 - val_loss: 0.1786 - val_accuracy: 0.9676 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.9548881e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9797 - val_loss: 0.2110 - val_accuracy: 0.9586 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.7415751e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9806 - val_loss: 0.1881 - val_accuracy: 0.9637 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 5.4346700e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9802 - val_loss: 0.2092 - val_accuracy: 0.9589 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.8529620e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9808 - val_loss: 0.1746 - val_accuracy: 0.9690 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1761585e-05 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9798 - val_loss: 0.1863 - val_accuracy: 0.9658 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.3021492e-05 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9800 - val_loss: 0.2157 - val_accuracy: 0.9561 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.9737682e-05 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9801 - val_loss: 0.2052 - val_accuracy: 0.9604 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.3344270e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9802 - val_loss: 0.2737 - val_accuracy: 0.9426 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.3173285e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9796 - val_loss: 0.2023 - val_accuracy: 0.9618 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.5292045e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9792 - val_loss: 0.2392 - val_accuracy: 0.9513 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 5.1748141e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9801 - val_loss: 0.2030 - val_accuracy: 0.9602 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.5715660e-05 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9795 - val_loss: 0.1892 - val_accuracy: 0.9637 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.3138834e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9806 - val_loss: 0.1886 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.0561539e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9802 - val_loss: 0.2424 - val_accuracy: 0.9461 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.0211793e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9797 - val_loss: 0.2055 - val_accuracy: 0.9599 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.6267926e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9800 - val_loss: 0.1950 - val_accuracy: 0.9635 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.1458444e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9794 - val_loss: 0.1932 - val_accuracy: 0.9616 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.2837814e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9799 - val_loss: 0.2111 - val_accuracy: 0.9589 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.2420901e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 225/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.1898 - val_accuracy: 0.9639 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.1019445e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 226/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1335 - accuracy: 0.9797 - val_loss: 0.2075 - val_accuracy: 0.9591 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.2819757e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 227/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1320 - accuracy: 0.9805 - val_loss: 0.2158 - val_accuracy: 0.9572 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 7.4344169e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9797 - val_loss: 0.1888 - val_accuracy: 0.9629 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.0737314e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9793 - val_loss: 0.1932 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.1472593e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9804 - val_loss: 0.1932 - val_accuracy: 0.9634 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.9556246e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9793 - val_loss: 0.1940 - val_accuracy: 0.9648 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.8840354e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9808 - val_loss: 0.2125 - val_accuracy: 0.9577 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.3333550e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.1955 - val_accuracy: 0.9636 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.1824080e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9795 - val_loss: 0.2074 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.2734474e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9804 - val_loss: 0.1925 - val_accuracy: 0.9635 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.1261896e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 236/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1363 - accuracy: 0.9789 - val_loss: 0.2007 - val_accuracy: 0.9632 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 5.3408754e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9811 - val_loss: 0.2048 - val_accuracy: 0.9597 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.1480955e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9787 - val_loss: 0.1980 - val_accuracy: 0.9612 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.6401295e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9792 - val_loss: 0.1823 - val_accuracy: 0.9650 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.6325749e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9801 - val_loss: 0.1774 - val_accuracy: 0.9675 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.7101139e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9798 - val_loss: 0.1876 - val_accuracy: 0.9637 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.8466971e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9803 - val_loss: 0.1962 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.5662089e-04 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9797 - val_loss: 0.2053 - val_accuracy: 0.9579 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.6425705e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9795 - val_loss: 0.1863 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.6272781e-05 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 245/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1308 - accuracy: 0.9803 - val_loss: 0.1848 - val_accuracy: 0.9646 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.5862150e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9796 - val_loss: 0.1713 - val_accuracy: 0.9668 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.0402581e-03 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9798 - val_loss: 0.1954 - val_accuracy: 0.9598 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.0120177e-03 0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 248/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1324 - accuracy: 0.9797 - val_loss: 0.1850 - val_accuracy: 0.9660 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.3139959e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9808 - val_loss: 0.1752 - val_accuracy: 0.9678 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.7803303e-04 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9797 - val_loss: 0.2028 - val_accuracy: 0.9606 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.1050567e-05 -0.0000000e+00] Sparsity at: 0.4758564988730278 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.003530736334766582 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.43911564 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 0. 1. 1.] ... [1. 0. 0. ... 0. 1. 0.] [0. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.0119226070366939 Thresholhold 6.476342150563141e-06 Using suggest threshold. Applying new mask Percentage zeros 0.87166667 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.07383174323319519 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 208s 12ms/step - loss: 0.1342 - accuracy: 0.9794 - val_loss: 0.1843 - val_accuracy: 0.9650 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.6426386e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1302 - accuracy: 0.9799 - val_loss: 0.2053 - val_accuracy: 0.9598 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.3525995e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 253/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1320 - accuracy: 0.9802 - val_loss: 0.2091 - val_accuracy: 0.9597 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.4704706e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9802 - val_loss: 0.2004 - val_accuracy: 0.9609 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.4491310e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9813 - val_loss: 0.1802 - val_accuracy: 0.9659 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -9.2557384e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9798 - val_loss: 0.2079 - val_accuracy: 0.9598 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.4840075e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9787 - val_loss: 0.2022 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.3169934e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9811 - val_loss: 0.2116 - val_accuracy: 0.9576 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.4805001e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9796 - val_loss: 0.1963 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.9985343e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9807 - val_loss: 0.1760 - val_accuracy: 0.9669 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.0512780e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9811 - val_loss: 0.1855 - val_accuracy: 0.9648 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.6194466e-07 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9798 - val_loss: 0.1899 - val_accuracy: 0.9655 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.8897397e-06 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9806 - val_loss: 0.2073 - val_accuracy: 0.9579 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.2807987e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9793 - val_loss: 0.2038 - val_accuracy: 0.9619 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1697434e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9802 - val_loss: 0.1916 - val_accuracy: 0.9616 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.7746972e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9803 - val_loss: 0.1766 - val_accuracy: 0.9683 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3367013e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.1918 - val_accuracy: 0.9645 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.9967096e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1300 - accuracy: 0.9805 - val_loss: 0.1791 - val_accuracy: 0.9665 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.6408228e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9802 - val_loss: 0.1946 - val_accuracy: 0.9628 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.0519116e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9803 - val_loss: 0.1772 - val_accuracy: 0.9663 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.4505130e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1300 - accuracy: 0.9803 - val_loss: 0.1834 - val_accuracy: 0.9665 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.9567480e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9807 - val_loss: 0.1796 - val_accuracy: 0.9678 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.6778932e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9804 - val_loss: 0.1763 - val_accuracy: 0.9665 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.2897531e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9794 - val_loss: 0.2109 - val_accuracy: 0.9573 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.2628719e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9789 - val_loss: 0.2013 - val_accuracy: 0.9582 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.1662475e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9803 - val_loss: 0.1947 - val_accuracy: 0.9616 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.1107063e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9801 - val_loss: 0.1976 - val_accuracy: 0.9613 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -8.8958346e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9790 - val_loss: 0.1847 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.4006325e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9800 - val_loss: 0.1996 - val_accuracy: 0.9619 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.9275221e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9811 - val_loss: 0.1878 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.7257169e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9803 - val_loss: 0.2015 - val_accuracy: 0.9601 [ 1.39888236e-34 4.81357360e-34 4.61340023e-34 ... -0.00000000e+00 1.20821096e-04 0.00000000e+00] Sparsity at: 0.48809166040571 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9808 - val_loss: 0.2022 - val_accuracy: 0.9576 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.8836462e-06 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9793 - val_loss: 0.2103 - val_accuracy: 0.9610 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.9455225e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9797 - val_loss: 0.2077 - val_accuracy: 0.9593 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.2685582e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9804 - val_loss: 0.2023 - val_accuracy: 0.9589 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.1617626e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9794 - val_loss: 0.1923 - val_accuracy: 0.9635 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.7860759e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9804 - val_loss: 0.1938 - val_accuracy: 0.9628 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.1265378e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9797 - val_loss: 0.2086 - val_accuracy: 0.9584 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.9538085e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9806 - val_loss: 0.2098 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.5548644e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 290/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1327 - accuracy: 0.9800 - val_loss: 0.1736 - val_accuracy: 0.9680 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.5330020e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9803 - val_loss: 0.1889 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.3386445e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9816 - val_loss: 0.1904 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.6362326e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9784 - val_loss: 0.1946 - val_accuracy: 0.9638 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.6859782e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9803 - val_loss: 0.2159 - val_accuracy: 0.9579 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.9776520e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9811 - val_loss: 0.1759 - val_accuracy: 0.9661 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.2655028e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9812 - val_loss: 0.2086 - val_accuracy: 0.9602 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.7434205e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9808 - val_loss: 0.1944 - val_accuracy: 0.9620 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.1435795e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9808 - val_loss: 0.2189 - val_accuracy: 0.9545 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.3751520e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9798 - val_loss: 0.2024 - val_accuracy: 0.9610 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.1064452e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9812 - val_loss: 0.2119 - val_accuracy: 0.9585 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.0458655e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.010878022520181663 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.43911564 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 0. 1. 1.] ... [1. 0. 0. ... 0. 1. 0.] [0. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.022533535368353563 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.87166667 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.08570467849509633 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 245s 12ms/step - loss: 0.1320 - accuracy: 0.9798 - val_loss: 0.2068 - val_accuracy: 0.9589 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.0287430e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 302/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1300 - accuracy: 0.9804 - val_loss: 0.2070 - val_accuracy: 0.9577 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 8.2219206e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9810 - val_loss: 0.2098 - val_accuracy: 0.9601 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.8300686e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 304/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1316 - accuracy: 0.9796 - val_loss: 0.2109 - val_accuracy: 0.9585 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.3620086e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9799 - val_loss: 0.1815 - val_accuracy: 0.9671 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.1677590e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9805 - val_loss: 0.1864 - val_accuracy: 0.9642 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.5538680e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9800 - val_loss: 0.2154 - val_accuracy: 0.9572 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.8722789e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9802 - val_loss: 0.2025 - val_accuracy: 0.9612 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.2759237e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9801 - val_loss: 0.1985 - val_accuracy: 0.9631 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.0076985e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9801 - val_loss: 0.1962 - val_accuracy: 0.9639 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.1087283e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9815 - val_loss: 0.2109 - val_accuracy: 0.9581 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.2178175e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9806 - val_loss: 0.2034 - val_accuracy: 0.9604 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.4352013e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9800 - val_loss: 0.1925 - val_accuracy: 0.9620 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -8.4792264e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9811 - val_loss: 0.1966 - val_accuracy: 0.9621 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.2862558e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9795 - val_loss: 0.2004 - val_accuracy: 0.9608 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.6395365e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9808 - val_loss: 0.2006 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 9.3596835e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9801 - val_loss: 0.2293 - val_accuracy: 0.9532 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.3580963e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9806 - val_loss: 0.1987 - val_accuracy: 0.9603 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.8772028e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1299 - accuracy: 0.9805 - val_loss: 0.1858 - val_accuracy: 0.9654 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.3259499e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9812 - val_loss: 0.2389 - val_accuracy: 0.9523 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.0428694e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9796 - val_loss: 0.2031 - val_accuracy: 0.9600 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.8978580e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9787 - val_loss: 0.1871 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.0846011e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9803 - val_loss: 0.2017 - val_accuracy: 0.9618 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.3994758e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9810 - val_loss: 0.2027 - val_accuracy: 0.9613 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.1443392e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9794 - val_loss: 0.1805 - val_accuracy: 0.9650 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.5654352e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9807 - val_loss: 0.1783 - val_accuracy: 0.9682 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.3012943e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9798 - val_loss: 0.1998 - val_accuracy: 0.9608 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.5866906e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9805 - val_loss: 0.1827 - val_accuracy: 0.9650 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.4256855e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9812 - val_loss: 0.1876 - val_accuracy: 0.9646 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.4135535e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9804 - val_loss: 0.1790 - val_accuracy: 0.9669 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.1074486e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 331/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1261 - accuracy: 0.9808 - val_loss: 0.2042 - val_accuracy: 0.9609 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.8034944e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9804 - val_loss: 0.1871 - val_accuracy: 0.9636 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 9.6679345e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9810 - val_loss: 0.1991 - val_accuracy: 0.9624 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.8885365e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9808 - val_loss: 0.1957 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.3094368e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9797 - val_loss: 0.1938 - val_accuracy: 0.9624 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.1122239e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9792 - val_loss: 0.2322 - val_accuracy: 0.9548 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.9193843e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9806 - val_loss: 0.1825 - val_accuracy: 0.9664 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.0637764e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9804 - val_loss: 0.1996 - val_accuracy: 0.9616 [ 1.39888236e-34 4.81357360e-34 4.61340023e-34 ... -0.00000000e+00 -1.10249166e-04 -0.00000000e+00] Sparsity at: 0.48809166040571 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9807 - val_loss: 0.1942 - val_accuracy: 0.9639 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.5398652e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9801 - val_loss: 0.1996 - val_accuracy: 0.9616 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.2029074e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9810 - val_loss: 0.2385 - val_accuracy: 0.9517 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.9186452e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9811 - val_loss: 0.2367 - val_accuracy: 0.9535 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.5352000e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9796 - val_loss: 0.1966 - val_accuracy: 0.9628 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.6529242e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9802 - val_loss: 0.1841 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.2776029e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9804 - val_loss: 0.2268 - val_accuracy: 0.9551 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.3630217e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9798 - val_loss: 0.1876 - val_accuracy: 0.9642 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.4473923e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9799 - val_loss: 0.1808 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -8.5819709e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9797 - val_loss: 0.1893 - val_accuracy: 0.9659 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.0475174e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9804 - val_loss: 0.1844 - val_accuracy: 0.9663 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.0007545e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9803 - val_loss: 0.1860 - val_accuracy: 0.9655 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.3572280e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.017323238427237486 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.43911564 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 0. 1. 1.] ... [1. 0. 0. ... 0. 1. 0.] [0. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.03224054172972535 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.87166667 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.09469104772470516 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 244s 12ms/step - loss: 0.1272 - accuracy: 0.9809 - val_loss: 0.1909 - val_accuracy: 0.9640 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.4577816e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 352/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1274 - accuracy: 0.9807 - val_loss: 0.2014 - val_accuracy: 0.9627 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.1046820e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9795 - val_loss: 0.2078 - val_accuracy: 0.9590 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.4049340e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 354/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1274 - accuracy: 0.9804 - val_loss: 0.1845 - val_accuracy: 0.9654 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -9.9451099e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9792 - val_loss: 0.2041 - val_accuracy: 0.9589 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.5704495e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9811 - val_loss: 0.2001 - val_accuracy: 0.9601 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.9935438e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9800 - val_loss: 0.1957 - val_accuracy: 0.9627 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.0952558e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9800 - val_loss: 0.1820 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.8357998e-06 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9812 - val_loss: 0.1940 - val_accuracy: 0.9624 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.4250965e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9808 - val_loss: 0.2013 - val_accuracy: 0.9615 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 7.0493860e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9810 - val_loss: 0.1892 - val_accuracy: 0.9632 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 5.5408187e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9810 - val_loss: 0.1806 - val_accuracy: 0.9648 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 4.0703555e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9805 - val_loss: 0.1850 - val_accuracy: 0.9650 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.0017204e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9805 - val_loss: 0.1853 - val_accuracy: 0.9655 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.4355180e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9804 - val_loss: 0.2095 - val_accuracy: 0.9571 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.7988788e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9794 - val_loss: 0.2021 - val_accuracy: 0.9612 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.3307489e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9812 - val_loss: 0.2014 - val_accuracy: 0.9595 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -8.3155447e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9798 - val_loss: 0.2023 - val_accuracy: 0.9618 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.0727261e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9800 - val_loss: 0.1870 - val_accuracy: 0.9658 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.6732412e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9801 - val_loss: 0.2046 - val_accuracy: 0.9586 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.7912083e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9800 - val_loss: 0.1822 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 7.3126762e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9803 - val_loss: 0.1806 - val_accuracy: 0.9658 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.6330113e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9797 - val_loss: 0.2035 - val_accuracy: 0.9615 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.5580232e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9796 - val_loss: 0.1988 - val_accuracy: 0.9637 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.4837763e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9814 - val_loss: 0.2069 - val_accuracy: 0.9584 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.0624164e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9799 - val_loss: 0.1917 - val_accuracy: 0.9631 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.2077266e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 377/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1262 - accuracy: 0.9809 - val_loss: 0.2139 - val_accuracy: 0.9576 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.8564976e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9797 - val_loss: 0.1912 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.2543010e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 379/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1275 - accuracy: 0.9804 - val_loss: 0.1978 - val_accuracy: 0.9626 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.8632263e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 380/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1276 - accuracy: 0.9805 - val_loss: 0.1987 - val_accuracy: 0.9626 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.6061452e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1299 - accuracy: 0.9805 - val_loss: 0.2044 - val_accuracy: 0.9600 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.4569253e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9801 - val_loss: 0.1980 - val_accuracy: 0.9614 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -7.1604373e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9805 - val_loss: 0.1920 - val_accuracy: 0.9642 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.7270191e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9802 - val_loss: 0.1773 - val_accuracy: 0.9640 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.2098746e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 385/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1279 - accuracy: 0.9798 - val_loss: 0.2012 - val_accuracy: 0.9621 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.6086413e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9805 - val_loss: 0.1725 - val_accuracy: 0.9677 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.5462115e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9806 - val_loss: 0.2034 - val_accuracy: 0.9627 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.6892169e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9815 - val_loss: 0.1990 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.5410886e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9807 - val_loss: 0.1827 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.7342695e-06 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 390/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1302 - accuracy: 0.9807 - val_loss: 0.2097 - val_accuracy: 0.9585 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.1302849e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9814 - val_loss: 0.2099 - val_accuracy: 0.9617 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1500490e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9803 - val_loss: 0.2135 - val_accuracy: 0.9603 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.0443031e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9802 - val_loss: 0.1866 - val_accuracy: 0.9651 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.9640802e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9807 - val_loss: 0.1828 - val_accuracy: 0.9677 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.9065368e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9791 - val_loss: 0.1889 - val_accuracy: 0.9631 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -7.9919130e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9805 - val_loss: 0.2029 - val_accuracy: 0.9600 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.8484246e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9797 - val_loss: 0.1911 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.9648864e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9799 - val_loss: 0.1993 - val_accuracy: 0.9612 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.5801664e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9800 - val_loss: 0.2161 - val_accuracy: 0.9573 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.6813051e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9799 - val_loss: 0.2013 - val_accuracy: 0.9607 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -8.0838875e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.021713767837582942 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.43911564 tf.Tensor( [[1. 1. 1. ... 0. 1. 1.] [0. 1. 0. ... 1. 0. 1.] [1. 0. 1. ... 0. 1. 1.] ... [1. 0. 0. ... 0. 1. 0.] [0. 1. 1. ... 1. 0. 0.] [1. 1. 1. ... 1. 0. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.03694960108197343 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.87166667 tf.Tensor( [[1. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 1. ... 0. 1. 1.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.09687688139530692 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 0. 1. 0. 0. 1. 0. 1. 1.] [0. 0. 1. 0. 0. 1. 0. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 1. 0. 1. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 0. 0. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 0. 0.] [1. 0. 1. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 1. 1. 0. 0. 1. 1. 0.] [0. 0. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 0. 0. 0. 1. 0.] [1. 0. 1. 0. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 0. 1. 0. 1.] [0. 1. 0. 1. 1. 1. 0. 0. 1. 0.] [0. 1. 1. 0. 0. 1. 0. 0. 0. 0.] [0. 1. 0. 0. 1. 0. 0. 0. 1. 0.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 1.] [0. 0. 1. 0. 0. 0. 0. 0. 1. 1.] [1. 0. 0. 1. 1. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 0. 0. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 0. 1.] [0. 0. 1. 0. 0. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 1. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 1. 1. 1. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 1.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 0. 0. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 1. 1. 0. 1. 1. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 1. 1.] [0. 0. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 1. 0. 1. 1. 1. 1. 1. 0. 0.] [0. 1. 1. 1. 1. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 0. 0. 1. 1. 0. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 1. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 1.] [0. 0. 1. 1. 0. 0. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 1. 1. 0. 0. 0. 1. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 0. 0. 0. 1.] [1. 0. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 1. 0. 1. 1. 1. 0. 0.] [0. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 1. 0.] [0. 1. 0. 0. 0. 1. 1. 1. 0. 1.] [0. 0. 1. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 1. 0. 0. 0. 1.] [0. 0. 1. 0. 1. 0. 0. 1. 1. 1.] [1. 1. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 242s 12ms/step - loss: 0.1299 - accuracy: 0.9802 - val_loss: 0.1964 - val_accuracy: 0.9634 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.2531057e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 402/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1253 - accuracy: 0.9815 - val_loss: 0.2007 - val_accuracy: 0.9621 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.7649336e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9798 - val_loss: 0.1866 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.0797048e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9808 - val_loss: 0.1980 - val_accuracy: 0.9624 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.7734433e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9798 - val_loss: 0.1863 - val_accuracy: 0.9644 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.1009780e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9805 - val_loss: 0.1841 - val_accuracy: 0.9655 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.9803252e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9802 - val_loss: 0.1900 - val_accuracy: 0.9660 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.9773498e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9804 - val_loss: 0.1908 - val_accuracy: 0.9643 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.2134158e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9809 - val_loss: 0.1982 - val_accuracy: 0.9598 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.5278238e-07 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9802 - val_loss: 0.2018 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.3455713e-09 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9799 - val_loss: 0.2158 - val_accuracy: 0.9559 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -8.5652246e-10 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9799 - val_loss: 0.1915 - val_accuracy: 0.9635 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.1703126e-15 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9804 - val_loss: 0.1885 - val_accuracy: 0.9644 [1.39888236e-34 4.81357360e-34 4.61340023e-34 ... 0.00000000e+00 1.18233166e-20 0.00000000e+00] Sparsity at: 0.48809166040571 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9797 - val_loss: 0.2303 - val_accuracy: 0.9543 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.3822716e-26 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 415/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1305 - accuracy: 0.9802 - val_loss: 0.1886 - val_accuracy: 0.9642 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.5642930e-31 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9797 - val_loss: 0.2094 - val_accuracy: 0.9578 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9816 - val_loss: 0.1962 - val_accuracy: 0.9644 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9805 - val_loss: 0.1914 - val_accuracy: 0.9657 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9801 - val_loss: 0.2127 - val_accuracy: 0.9571 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9804 - val_loss: 0.1926 - val_accuracy: 0.9641 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9795 - val_loss: 0.1979 - val_accuracy: 0.9605 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9798 - val_loss: 0.1921 - val_accuracy: 0.9648 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9805 - val_loss: 0.2039 - val_accuracy: 0.9632 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9804 - val_loss: 0.1896 - val_accuracy: 0.9637 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1321 - accuracy: 0.9793 - val_loss: 0.1925 - val_accuracy: 0.9662 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 2.3659349e-34 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 426/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1271 - accuracy: 0.9808 - val_loss: 0.1940 - val_accuracy: 0.9630 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.5643091e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9810 - val_loss: 0.2024 - val_accuracy: 0.9609 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -3.4917784e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9805 - val_loss: 0.2085 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 5.1602966e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9796 - val_loss: 0.2068 - val_accuracy: 0.9578 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.8680256e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9814 - val_loss: 0.2488 - val_accuracy: 0.9480 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -8.7814115e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9810 - val_loss: 0.1997 - val_accuracy: 0.9613 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.9954630e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9796 - val_loss: 0.1953 - val_accuracy: 0.9632 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1197824e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9803 - val_loss: 0.1766 - val_accuracy: 0.9673 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.8281140e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9804 - val_loss: 0.2000 - val_accuracy: 0.9603 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.9694909e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9798 - val_loss: 0.2039 - val_accuracy: 0.9606 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.8631099e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9810 - val_loss: 0.1859 - val_accuracy: 0.9663 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.4609578e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9808 - val_loss: 0.1835 - val_accuracy: 0.9637 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.8266556e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9809 - val_loss: 0.1860 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.8776259e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9806 - val_loss: 0.1838 - val_accuracy: 0.9653 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.8344529e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9796 - val_loss: 0.1848 - val_accuracy: 0.9634 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.6816432e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9803 - val_loss: 0.1787 - val_accuracy: 0.9655 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.7559162e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9803 - val_loss: 0.1860 - val_accuracy: 0.9663 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.3259020e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 443/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1330 - accuracy: 0.9797 - val_loss: 0.2151 - val_accuracy: 0.9569 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -9.9821005e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9801 - val_loss: 0.1902 - val_accuracy: 0.9630 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.0405128e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1300 - accuracy: 0.9802 - val_loss: 0.1981 - val_accuracy: 0.9603 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.5771826e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9803 - val_loss: 0.1823 - val_accuracy: 0.9659 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.1225374e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9796 - val_loss: 0.1965 - val_accuracy: 0.9621 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.2475654e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9787 - val_loss: 0.1974 - val_accuracy: 0.9625 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 6.2016112e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9806 - val_loss: 0.1860 - val_accuracy: 0.9621 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.0099871e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9805 - val_loss: 0.2184 - val_accuracy: 0.9559 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 5.1190867e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9802 - val_loss: 0.1710 - val_accuracy: 0.9682 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -9.7378834e-06 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1241 - accuracy: 0.9815 - val_loss: 0.2050 - val_accuracy: 0.9587 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.4975001e-05 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9801 - val_loss: 0.1883 - val_accuracy: 0.9621 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -6.3573339e-06 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9790 - val_loss: 0.1882 - val_accuracy: 0.9638 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 8.0610218e-05 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9802 - val_loss: 0.2036 - val_accuracy: 0.9625 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.2415971e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9809 - val_loss: 0.1841 - val_accuracy: 0.9636 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.5177488e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9798 - val_loss: 0.1762 - val_accuracy: 0.9674 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -6.9175183e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9809 - val_loss: 0.2069 - val_accuracy: 0.9597 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 3.0604794e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9806 - val_loss: 0.1796 - val_accuracy: 0.9668 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.2529758e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9804 - val_loss: 0.1876 - val_accuracy: 0.9623 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 6.2467298e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9805 - val_loss: 0.2196 - val_accuracy: 0.9544 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.3983153e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9801 - val_loss: 0.1787 - val_accuracy: 0.9649 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.6073945e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9800 - val_loss: 0.1722 - val_accuracy: 0.9699 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.8695957e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9805 - val_loss: 0.1854 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.1063369e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9804 - val_loss: 0.1736 - val_accuracy: 0.9697 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.3175045e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9801 - val_loss: 0.2059 - val_accuracy: 0.9591 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 7.6385541e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9807 - val_loss: 0.1787 - val_accuracy: 0.9662 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 5.5458688e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9804 - val_loss: 0.2259 - val_accuracy: 0.9563 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.2886905e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9801 - val_loss: 0.1997 - val_accuracy: 0.9614 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.9550936e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9807 - val_loss: 0.1918 - val_accuracy: 0.9624 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.8418157e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9801 - val_loss: 0.1697 - val_accuracy: 0.9699 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.0449234e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9797 - val_loss: 0.2031 - val_accuracy: 0.9598 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.4533716e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9801 - val_loss: 0.1825 - val_accuracy: 0.9669 [1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.0492996e-02 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9801 - val_loss: 0.1865 - val_accuracy: 0.9632 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.8096509e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9798 - val_loss: 0.1826 - val_accuracy: 0.9674 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.7263809e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9808 - val_loss: 0.1898 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1228092e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9800 - val_loss: 0.2005 - val_accuracy: 0.9611 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.5121604e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9803 - val_loss: 0.2214 - val_accuracy: 0.9537 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -7.2913917e-07 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9794 - val_loss: 0.1817 - val_accuracy: 0.9656 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.7112724e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1304 - accuracy: 0.9796 - val_loss: 0.1961 - val_accuracy: 0.9635 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 2.7726165e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9801 - val_loss: 0.1840 - val_accuracy: 0.9670 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -2.0365163e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9802 - val_loss: 0.2100 - val_accuracy: 0.9601 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 3.1877335e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9798 - val_loss: 0.1947 - val_accuracy: 0.9605 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.5924698e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9808 - val_loss: 0.1892 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.6475280e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9800 - val_loss: 0.1978 - val_accuracy: 0.9599 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 1.7149337e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9808 - val_loss: 0.1727 - val_accuracy: 0.9686 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.7060778e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 487/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1269 - accuracy: 0.9800 - val_loss: 0.1944 - val_accuracy: 0.9619 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -5.6231697e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9802 - val_loss: 0.2001 - val_accuracy: 0.9626 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -5.9556961e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.1789 - val_accuracy: 0.9671 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 8.1373140e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9808 - val_loss: 0.1741 - val_accuracy: 0.9690 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.4102547e-04 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9803 - val_loss: 0.1901 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -1.2546194e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9806 - val_loss: 0.2010 - val_accuracy: 0.9623 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -2.1622840e-03 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9805 - val_loss: 0.1883 - val_accuracy: 0.9652 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 4.6879746e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1243 - accuracy: 0.9811 - val_loss: 0.2153 - val_accuracy: 0.9577 [ 1.39888236e-34 4.81357360e-34 4.61340023e-34 ... 0.00000000e+00 1.21089986e-04 -0.00000000e+00] Sparsity at: 0.48809166040571 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9797 - val_loss: 0.2007 - val_accuracy: 0.9639 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 1.3613561e-03 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9807 - val_loss: 0.1929 - val_accuracy: 0.9622 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -7.2531204e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 497/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1285 - accuracy: 0.9806 - val_loss: 0.1829 - val_accuracy: 0.9663 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -1.0037678e-04 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9796 - val_loss: 0.1980 - val_accuracy: 0.9617 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... -0.0000000e+00 -4.2148949e-06 0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9797 - val_loss: 0.1764 - val_accuracy: 0.9677 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -3.7922968e-07 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9803 - val_loss: 0.2003 - val_accuracy: 0.9602 [ 1.3988824e-34 4.8135736e-34 4.6134002e-34 ... 0.0000000e+00 -4.3595131e-07 -0.0000000e+00] Sparsity at: 0.48809166040571 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.037129007279872894 Thresholhold -0.05450941622257233 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06132306158542633 Thresholhold -0.07481430470943451 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.1178489625453949 Thresholhold -0.06855818629264832 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 1/235 [..............................] - ETA: 4:21:57 - loss: 2.7905 - accuracy: 0.1445WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0094s vs `on_train_batch_begin` time: 11.0806s). Check your callbacks. 235/235 [==============================] - 70s 12ms/step - loss: 0.2415 - accuracy: 0.9289 - val_loss: 0.2132 - val_accuracy: 0.9550 [-0.05450942 0.01009626 -0.00054583 ... 0.20011221 -0.21515071 -0.14325681] Sparsity at: 0.0 Epoch 2/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0876 - accuracy: 0.9750 - val_loss: 0.0966 - val_accuracy: 0.9700 [-0.05450942 0.01009626 -0.00054583 ... 0.23081218 -0.23711504 -0.1560789 ] Sparsity at: 0.0 Epoch 3/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0505 - accuracy: 0.9862 - val_loss: 0.0833 - val_accuracy: 0.9744 [-0.05450942 0.01009626 -0.00054583 ... 0.25628632 -0.26043 -0.16595905] Sparsity at: 0.0 Epoch 4/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0308 - accuracy: 0.9922 - val_loss: 0.0861 - val_accuracy: 0.9733 [-0.05450942 0.01009626 -0.00054583 ... 0.27751678 -0.27961394 -0.17310259] Sparsity at: 0.0 Epoch 5/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0194 - accuracy: 0.9955 - val_loss: 0.0822 - val_accuracy: 0.9735 [-0.05450942 0.01009626 -0.00054583 ... 0.300424 -0.29422602 -0.17671813] Sparsity at: 0.0 Epoch 6/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0138 - accuracy: 0.9966 - val_loss: 0.0823 - val_accuracy: 0.9760 [-0.05450942 0.01009626 -0.00054583 ... 0.31929427 -0.2952289 -0.18168859] Sparsity at: 0.0 Epoch 7/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0138 - accuracy: 0.9964 - val_loss: 0.0894 - val_accuracy: 0.9729 [-0.05450942 0.01009626 -0.00054583 ... 0.3338897 -0.3126545 -0.18281323] Sparsity at: 0.0 Epoch 8/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0122 - accuracy: 0.9969 - val_loss: 0.0847 - val_accuracy: 0.9764 [-0.05450942 0.01009626 -0.00054583 ... 0.34222835 -0.3229267 -0.19074386] Sparsity at: 0.0 Epoch 9/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0106 - accuracy: 0.9969 - val_loss: 0.0832 - val_accuracy: 0.9768 [-0.05450942 0.01009626 -0.00054583 ... 0.35670444 -0.32905862 -0.19204962] Sparsity at: 0.0 Epoch 10/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0101 - accuracy: 0.9972 - val_loss: 0.0837 - val_accuracy: 0.9764 [-0.05450942 0.01009626 -0.00054583 ... 0.372736 -0.32508072 -0.19110572] Sparsity at: 0.0 Epoch 11/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0083 - accuracy: 0.9978 - val_loss: 0.0839 - val_accuracy: 0.9776 [-0.05450942 0.01009626 -0.00054583 ... 0.38408643 -0.33403382 -0.19220103] Sparsity at: 0.0 Epoch 12/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 0.0832 - val_accuracy: 0.9794 [-0.05450942 0.01009626 -0.00054583 ... 0.3908822 -0.33916074 -0.19416702] Sparsity at: 0.0 Epoch 13/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0064 - accuracy: 0.9982 - val_loss: 0.0784 - val_accuracy: 0.9787 [-0.05450942 0.01009626 -0.00054583 ... 0.38757458 -0.3382323 -0.19588149] Sparsity at: 0.0 Epoch 14/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9990 - val_loss: 0.0800 - val_accuracy: 0.9803 [-0.05450942 0.01009626 -0.00054583 ... 0.40115485 -0.34620863 -0.20064116] Sparsity at: 0.0 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9994 - val_loss: 0.0718 - val_accuracy: 0.9809 [-0.05450942 0.01009626 -0.00054583 ... 0.4026549 -0.3460693 -0.20430736] Sparsity at: 0.0 Epoch 16/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9996 - val_loss: 0.0914 - val_accuracy: 0.9779 [-0.05450942 0.01009626 -0.00054583 ... 0.41177487 -0.3490294 -0.208024 ] Sparsity at: 0.0 Epoch 17/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.1279 - val_accuracy: 0.9700 [-0.05450942 0.01009626 -0.00054583 ... 0.4224475 -0.35416237 -0.21556702] Sparsity at: 0.0 Epoch 18/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0184 - accuracy: 0.9936 - val_loss: 0.1095 - val_accuracy: 0.9725 [-0.05450942 0.01009626 -0.00054583 ... 0.42155692 -0.34116215 -0.20000446] Sparsity at: 0.0 Epoch 19/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0115 - accuracy: 0.9960 - val_loss: 0.0867 - val_accuracy: 0.9785 [-0.05450942 0.01009626 -0.00054583 ... 0.414257 -0.35070923 -0.20100692] Sparsity at: 0.0 Epoch 20/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.0868 - val_accuracy: 0.9810 [-0.05450942 0.01009626 -0.00054583 ... 0.4127119 -0.36144352 -0.1839759 ] Sparsity at: 0.0 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0035 - accuracy: 0.9989 - val_loss: 0.0832 - val_accuracy: 0.9806 [-0.05450942 0.01009626 -0.00054583 ... 0.4171977 -0.36277395 -0.1878449 ] Sparsity at: 0.0 Epoch 22/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0728 - val_accuracy: 0.9830 [-0.05450942 0.01009626 -0.00054583 ... 0.42150843 -0.3692222 -0.19237576] Sparsity at: 0.0 Epoch 23/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0795 - val_accuracy: 0.9818 [-0.05450942 0.01009626 -0.00054583 ... 0.422935 -0.3767858 -0.18638399] Sparsity at: 0.0 Epoch 24/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0770 - val_accuracy: 0.9824 [-0.05450942 0.01009626 -0.00054583 ... 0.43026227 -0.37381005 -0.19352132] Sparsity at: 0.0 Epoch 25/500 235/235 [==============================] - 3s 13ms/step - loss: 8.1831e-04 - accuracy: 0.9998 - val_loss: 0.0745 - val_accuracy: 0.9829 [-0.05450942 0.01009626 -0.00054583 ... 0.43124744 -0.3765246 -0.19485505] Sparsity at: 0.0 Epoch 26/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0923 - val_accuracy: 0.9805 [-0.05450942 0.01009626 -0.00054583 ... 0.43784767 -0.3794938 -0.2001888 ] Sparsity at: 0.0 Epoch 27/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0086 - accuracy: 0.9971 - val_loss: 0.1498 - val_accuracy: 0.9694 [-0.05450942 0.01009626 -0.00054583 ... 0.43473956 -0.38066217 -0.20399846] Sparsity at: 0.0 Epoch 28/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0154 - accuracy: 0.9945 - val_loss: 0.0926 - val_accuracy: 0.9786 [-0.05450942 0.01009626 -0.00054583 ... 0.43695235 -0.4059898 -0.20897159] Sparsity at: 0.0 Epoch 29/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0066 - accuracy: 0.9977 - val_loss: 0.0774 - val_accuracy: 0.9819 [-0.05450942 0.01009626 -0.00054583 ... 0.4457631 -0.4184451 -0.22712761] Sparsity at: 0.0 Epoch 30/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.0730 - val_accuracy: 0.9831 [-0.05450942 0.01009626 -0.00054583 ... 0.44658524 -0.42340267 -0.22533636] Sparsity at: 0.0 Epoch 31/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3199e-04 - accuracy: 0.9999 - val_loss: 0.0740 - val_accuracy: 0.9832 [-0.05450942 0.01009626 -0.00054583 ... 0.44621664 -0.42699742 -0.23091614] Sparsity at: 0.0 Epoch 32/500 235/235 [==============================] - 3s 13ms/step - loss: 6.1709e-04 - accuracy: 0.9999 - val_loss: 0.0735 - val_accuracy: 0.9832 [-0.05450942 0.01009626 -0.00054583 ... 0.4481061 -0.42815065 -0.22955613] Sparsity at: 0.0 Epoch 33/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3586e-04 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9841 [-0.05450942 0.01009626 -0.00054583 ... 0.4505352 -0.428865 -0.22655794] Sparsity at: 0.0 Epoch 34/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4834e-04 - accuracy: 1.0000 - val_loss: 0.0710 - val_accuracy: 0.9837 [-0.05450942 0.01009626 -0.00054583 ... 0.45070252 -0.42981657 -0.2270369 ] Sparsity at: 0.0 Epoch 35/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1477e-04 - accuracy: 1.0000 - val_loss: 0.0697 - val_accuracy: 0.9840 [-0.05450942 0.01009626 -0.00054583 ... 0.45224392 -0.43037412 -0.22719708] Sparsity at: 0.0 Epoch 36/500 235/235 [==============================] - 3s 13ms/step - loss: 8.7302e-05 - accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 0.9844 [-0.05450942 0.01009626 -0.00054583 ... 0.4533458 -0.43150717 -0.2279303 ] Sparsity at: 0.0 Epoch 37/500 235/235 [==============================] - 3s 13ms/step - loss: 7.1450e-05 - accuracy: 1.0000 - val_loss: 0.0697 - val_accuracy: 0.9844 [-0.05450942 0.01009626 -0.00054583 ... 0.4548255 -0.4321433 -0.22809035] Sparsity at: 0.0 Epoch 38/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8799e-05 - accuracy: 1.0000 - val_loss: 0.0702 - val_accuracy: 0.9841 [-0.05450942 0.01009626 -0.00054583 ... 0.45590773 -0.43305388 -0.22838162] Sparsity at: 0.0 Epoch 39/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3525e-05 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9844 [-0.05450942 0.01009626 -0.00054583 ... 0.4573869 -0.4330939 -0.22914332] Sparsity at: 0.0 Epoch 40/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9402e-05 - accuracy: 1.0000 - val_loss: 0.0709 - val_accuracy: 0.9845 [-0.05450942 0.01009626 -0.00054583 ... 0.45915425 -0.43429145 -0.22970274] Sparsity at: 0.0 Epoch 41/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1711e-05 - accuracy: 1.0000 - val_loss: 0.0712 - val_accuracy: 0.9848 [-0.05450942 0.01009626 -0.00054583 ... 0.46076664 -0.43507972 -0.23040633] Sparsity at: 0.0 Epoch 42/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0267e-05 - accuracy: 1.0000 - val_loss: 0.0717 - val_accuracy: 0.9845 [-0.05450942 0.01009626 -0.00054583 ... 0.46173775 -0.43692407 -0.23004994] Sparsity at: 0.0 Epoch 43/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3067e-05 - accuracy: 1.0000 - val_loss: 0.0725 - val_accuracy: 0.9843 [-0.05450942 0.01009626 -0.00054583 ... 0.46294153 -0.43767175 -0.23116162] Sparsity at: 0.0 Epoch 44/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0726e-05 - accuracy: 1.0000 - val_loss: 0.0729 - val_accuracy: 0.9845 [-0.05450942 0.01009626 -0.00054583 ... 0.46450704 -0.43877536 -0.23205411] Sparsity at: 0.0 Epoch 45/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6020e-05 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 0.9846 [-0.05450942 0.01009626 -0.00054583 ... 0.46586427 -0.43968672 -0.2325571 ] Sparsity at: 0.0 Epoch 46/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5947e-05 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9850 [-0.05450942 0.01009626 -0.00054583 ... 0.46685046 -0.44053036 -0.23343515] Sparsity at: 0.0 Epoch 47/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2879e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9847 [-0.05450942 0.01009626 -0.00054583 ... 0.46866867 -0.44179937 -0.2332024 ] Sparsity at: 0.0 Epoch 48/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9453e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9850 [-0.05450942 0.01009626 -0.00054583 ... 0.4702337 -0.44306484 -0.23376909] Sparsity at: 0.0 Epoch 49/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7743e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9852 [-0.05450942 0.01009626 -0.00054583 ... 0.47160578 -0.44475976 -0.23459777] Sparsity at: 0.0 Epoch 50/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5825e-05 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9853 [-0.05450942 0.01009626 -0.00054583 ... 0.4742282 -0.44578722 -0.23552185] Sparsity at: 0.0 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.06909565646716764 Thresholhold -0.05450941622257233 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.0964097025008499 Thresholhold -0.11463846266269684 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.31271971456216363 Thresholhold -0.25922611355781555 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 118s 11ms/step - loss: 1.3968e-05 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9853 [-0.05450942 0.01009626 -0.00054583 ... 0.47402266 -0.44713575 -0.23630936] Sparsity at: 0.0 Epoch 52/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0266 - accuracy: 0.9933 - val_loss: 0.3552 - val_accuracy: 0.9409 [-0.05450942 0.01009626 -0.00054583 ... 0.4500926 -0.42372838 -0.22058588] Sparsity at: 0.0 Epoch 53/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0344 - accuracy: 0.9893 - val_loss: 0.0794 - val_accuracy: 0.9800 [-0.05450942 0.01009626 -0.00054583 ... 0.43176877 -0.39148265 -0.23400038] Sparsity at: 0.0 Epoch 54/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0064 - accuracy: 0.9982 - val_loss: 0.0738 - val_accuracy: 0.9819 [-0.05450942 0.01009626 -0.00054583 ... 0.43429634 -0.38510275 -0.22832192] Sparsity at: 0.0 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.0726 - val_accuracy: 0.9834 [-0.05450942 0.01009626 -0.00054583 ... 0.4404288 -0.38835803 -0.23018515] Sparsity at: 0.0 Epoch 56/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4611e-04 - accuracy: 0.9999 - val_loss: 0.0731 - val_accuracy: 0.9832 [-0.05450942 0.01009626 -0.00054583 ... 0.44761673 -0.39999166 -0.2327564 ] Sparsity at: 0.0 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9249e-04 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9837 [-0.05450942 0.01009626 -0.00054583 ... 0.45130435 -0.40536836 -0.23289822] Sparsity at: 0.0 Epoch 58/500 235/235 [==============================] - 4s 16ms/step - loss: 5.5136e-04 - accuracy: 1.0000 - val_loss: 0.0720 - val_accuracy: 0.9845 [-0.05450942 0.01009626 -0.00054583 ... 0.4533759 -0.40681177 -0.23341732] Sparsity at: 0.0 Epoch 59/500 235/235 [==============================] - 3s 12ms/step - loss: 3.5280e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9839 [-0.05450942 0.01009626 -0.00054583 ... 0.4551178 -0.4085616 -0.23288314] Sparsity at: 0.0 Epoch 60/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0987e-04 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9839 [-0.05450942 0.01009626 -0.00054583 ... 0.45634022 -0.41029918 -0.2335798 ] Sparsity at: 0.0 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0848 - val_accuracy: 0.9815 [-0.05450942 0.01009626 -0.00054583 ... 0.45866343 -0.41304365 -0.22501664] Sparsity at: 0.0 Epoch 62/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0908 - val_accuracy: 0.9810 [-0.05450942 0.01009626 -0.00054583 ... 0.46451667 -0.41809878 -0.22718015] Sparsity at: 0.0 Epoch 63/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1005 - val_accuracy: 0.9777 [-0.05450942 0.01009626 -0.00054583 ... 0.47567096 -0.4167286 -0.22029394] Sparsity at: 0.0 Epoch 64/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0074 - accuracy: 0.9977 - val_loss: 0.1017 - val_accuracy: 0.9784 [-0.05450942 0.01009626 -0.00054583 ... 0.48086745 -0.39974797 -0.22285235] Sparsity at: 0.0 Epoch 65/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.0925 - val_accuracy: 0.9801 [-0.05450942 0.01009626 -0.00054583 ... 0.48978716 -0.39499664 -0.21975265] Sparsity at: 0.0 Epoch 66/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9991 - val_loss: 0.1008 - val_accuracy: 0.9777 [-0.05450942 0.01009626 -0.00054583 ... 0.5009468 -0.40092227 -0.22066136] Sparsity at: 0.0 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 0.0830 - val_accuracy: 0.9822 [-0.05450942 0.01009626 -0.00054583 ... 0.50589806 -0.40682143 -0.22615469] Sparsity at: 0.0 Epoch 68/500 235/235 [==============================] - 3s 13ms/step - loss: 7.3658e-04 - accuracy: 0.9999 - val_loss: 0.0778 - val_accuracy: 0.9841 [-0.05450942 0.01009626 -0.00054583 ... 0.5028206 -0.408657 -0.21802443] Sparsity at: 0.0 Epoch 69/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7994e-04 - accuracy: 1.0000 - val_loss: 0.0788 - val_accuracy: 0.9842 [-0.05450942 0.01009626 -0.00054583 ... 0.50290906 -0.4097446 -0.21905568] Sparsity at: 0.0 Epoch 70/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4084e-04 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9843 [-0.05450942 0.01009626 -0.00054583 ... 0.50359344 -0.4114527 -0.21965334] Sparsity at: 0.0 Epoch 71/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3888e-05 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9843 [-0.05450942 0.01009626 -0.00054583 ... 0.50358266 -0.41242063 -0.21970175] Sparsity at: 0.0 Epoch 72/500 235/235 [==============================] - 3s 13ms/step - loss: 7.9682e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9837 [-0.05450942 0.01009626 -0.00054583 ... 0.50401646 -0.41674367 -0.21965604] Sparsity at: 0.0 Epoch 73/500 235/235 [==============================] - 3s 13ms/step - loss: 6.5596e-05 - accuracy: 1.0000 - val_loss: 0.0800 - val_accuracy: 0.9846 [-0.05450942 0.01009626 -0.00054583 ... 0.5049147 -0.4157207 -0.22052668] Sparsity at: 0.0 Epoch 74/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0833e-05 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9843 [-0.05450942 0.01009626 -0.00054583 ... 0.505586 -0.4154534 -0.22047366] Sparsity at: 0.0 Epoch 75/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1604e-05 - accuracy: 1.0000 - val_loss: 0.0813 - val_accuracy: 0.9839 [-0.05450942 0.01009626 -0.00054583 ... 0.5061006 -0.4164136 -0.22065751] Sparsity at: 0.0 Epoch 76/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7609e-05 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9836 [-0.05450942 0.01009626 -0.00054583 ... 0.5066256 -0.41811085 -0.22083157] Sparsity at: 0.0 Epoch 77/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2355e-05 - accuracy: 1.0000 - val_loss: 0.0821 - val_accuracy: 0.9835 [-0.05450942 0.01009626 -0.00054583 ... 0.50753474 -0.41904444 -0.22056343] Sparsity at: 0.0 Epoch 78/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9969e-05 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9836 [-0.05450942 0.01009626 -0.00054583 ... 0.5082601 -0.4198181 -0.22080778] Sparsity at: 0.0 Epoch 79/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6588e-05 - accuracy: 1.0000 - val_loss: 0.0829 - val_accuracy: 0.9836 [-0.05450942 0.01009626 -0.00054583 ... 0.5090798 -0.42144704 -0.22115612] Sparsity at: 0.0 Epoch 80/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2502e-05 - accuracy: 1.0000 - val_loss: 0.0832 - val_accuracy: 0.9838 [-0.05450942 0.01009626 -0.00054583 ... 0.5092405 -0.4224372 -0.22154526] Sparsity at: 0.0 Epoch 81/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2821e-04 - accuracy: 0.9999 - val_loss: 0.1025 - val_accuracy: 0.9819 [-0.05450942 0.01009626 -0.00054583 ... 0.51181537 -0.43092996 -0.22237253] Sparsity at: 0.0 Epoch 82/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0259 - accuracy: 0.9923 - val_loss: 0.1156 - val_accuracy: 0.9766 [-0.05450942 0.01009626 -0.00054583 ... 0.5088173 -0.39345688 -0.2363766 ] Sparsity at: 0.0 Epoch 83/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.0888 - val_accuracy: 0.9806 [-0.05450942 0.01009626 -0.00054583 ... 0.5148693 -0.39103022 -0.25137112] Sparsity at: 0.0 Epoch 84/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0787 - val_accuracy: 0.9835 [-0.05450942 0.01009626 -0.00054583 ... 0.5178211 -0.39362714 -0.2505413 ] Sparsity at: 0.0 Epoch 85/500 235/235 [==============================] - 3s 13ms/step - loss: 6.3642e-04 - accuracy: 0.9999 - val_loss: 0.0786 - val_accuracy: 0.9837 [-0.05450942 0.01009626 -0.00054583 ... 0.5180767 -0.3996931 -0.25266585] Sparsity at: 0.0 Epoch 86/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0374e-04 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9836 [-0.05450942 0.01009626 -0.00054583 ... 0.51702195 -0.40092906 -0.25554967] Sparsity at: 0.0 Epoch 87/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6259e-04 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9837 [-0.05450942 0.01009626 -0.00054583 ... 0.5179631 -0.40158147 -0.25568837] Sparsity at: 0.0 Epoch 88/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2847e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9840 [-0.05450942 0.01009626 -0.00054583 ... 0.5187971 -0.40289387 -0.25568503] Sparsity at: 0.0 Epoch 89/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0333e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9842 [-0.05450942 0.01009626 -0.00054583 ... 0.5197888 -0.40443364 -0.25692943] Sparsity at: 0.0 Epoch 90/500 235/235 [==============================] - 3s 13ms/step - loss: 8.8469e-05 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9843 [-0.05450942 0.01009626 -0.00054583 ... 0.52025795 -0.4067398 -0.2564937 ] Sparsity at: 0.0 Epoch 91/500 235/235 [==============================] - 3s 13ms/step - loss: 7.6731e-05 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9848 [-0.05450942 0.01009626 -0.00054583 ... 0.5193875 -0.4079405 -0.25507882] Sparsity at: 0.0 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3650e-05 - accuracy: 1.0000 - val_loss: 0.0775 - val_accuracy: 0.9845 [-0.05450942 0.01009626 -0.00054583 ... 0.5210929 -0.40864143 -0.2555256 ] Sparsity at: 0.0 Epoch 93/500 235/235 [==============================] - 3s 13ms/step - loss: 5.6063e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9845 [-0.05450942 0.01009626 -0.00054583 ... 0.52308494 -0.41075328 -0.2559555 ] Sparsity at: 0.0 Epoch 94/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1882e-05 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9846 [-0.05450942 0.01009626 -0.00054583 ... 0.52526563 -0.41190317 -0.25715274] Sparsity at: 0.0 Epoch 95/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1107 - val_accuracy: 0.9794 [-0.05450942 0.01009626 -0.00054583 ... 0.52682143 -0.41498017 -0.26308203] Sparsity at: 0.0 Epoch 96/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.1271 - val_accuracy: 0.9741 [-0.05450942 0.01009626 -0.00054583 ... 0.5111164 -0.4350201 -0.20471412] Sparsity at: 0.0 Epoch 97/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9977 - val_loss: 0.0982 - val_accuracy: 0.9795 [-0.05450942 0.01009626 -0.00054583 ... 0.49661228 -0.44186455 -0.20365007] Sparsity at: 0.0 Epoch 98/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0838 - val_accuracy: 0.9831 [-0.05450942 0.01009626 -0.00054583 ... 0.50466925 -0.4558257 -0.21113397] Sparsity at: 0.0 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1573e-04 - accuracy: 0.9999 - val_loss: 0.0883 - val_accuracy: 0.9829 [-0.05450942 0.01009626 -0.00054583 ... 0.5060526 -0.4552143 -0.20996371] Sparsity at: 0.0 Epoch 100/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5341e-04 - accuracy: 1.0000 - val_loss: 0.0844 - val_accuracy: 0.9829 [-0.05450942 0.01009626 -0.00054583 ... 0.5073188 -0.45698458 -0.21132706] Sparsity at: 0.0 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.1352814660744759 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.16507129524108777 Thresholhold -0.2088223397731781 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.3850435044503051 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 218s 11ms/step - loss: 2.4730e-04 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9831 [-0.05450942 0.01009626 -0.00054583 ... 0.5098376 -0.46093944 -0.21183197] Sparsity at: 0.0 Epoch 102/500 235/235 [==============================] - 3s 12ms/step - loss: 1.2795e-04 - accuracy: 1.0000 - val_loss: 0.0843 - val_accuracy: 0.9826 [-0.05450942 0.01009626 -0.00054583 ... 0.5109204 -0.46400246 -0.21282776] Sparsity at: 0.0 Epoch 103/500 235/235 [==============================] - 3s 13ms/step - loss: 9.9090e-05 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9827 [-0.05450942 0.01009626 -0.00054583 ... 0.51286036 -0.4644988 -0.21343563] Sparsity at: 0.0 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7650e-05 - accuracy: 1.0000 - val_loss: 0.0860 - val_accuracy: 0.9829 [-0.05450942 0.01009626 -0.00054583 ... 0.51378995 -0.46560282 -0.21390921] Sparsity at: 0.0 Epoch 105/500 235/235 [==============================] - 3s 13ms/step - loss: 9.3512e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9829 [-0.05450942 0.01009626 -0.00054583 ... 0.5154258 -0.4659413 -0.21420777] Sparsity at: 0.0 Epoch 106/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9325e-05 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9829 [-0.05450942 0.01009626 -0.00054583 ... 0.51690406 -0.4678133 -0.21485157] Sparsity at: 0.0 Epoch 107/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1052e-05 - accuracy: 1.0000 - val_loss: 0.0846 - val_accuracy: 0.9835 [-0.05450942 0.01009626 -0.00054583 ... 0.51829904 -0.46924478 -0.21567523] Sparsity at: 0.0 Epoch 108/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3297e-05 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9830 [-0.05450942 0.01009626 -0.00054583 ... 0.51961863 -0.4711746 -0.2160236 ] Sparsity at: 0.0 Epoch 109/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4525e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9834 [-0.05450942 0.01009626 -0.00054583 ... 0.52031744 -0.47234893 -0.21591881] Sparsity at: 0.0 Epoch 110/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0436e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9835 [-0.05450942 0.01009626 -0.00054583 ... 0.5223311 -0.47317252 -0.21725197] Sparsity at: 0.0 Epoch 111/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0068 - accuracy: 0.9980 - val_loss: 0.1675 - val_accuracy: 0.9700 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4756826e-01 -4.7751001e-01 -2.2416425e-01] Sparsity at: 0.0 Epoch 112/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0110 - accuracy: 0.9962 - val_loss: 0.1104 - val_accuracy: 0.9798 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4852962e-01 -5.0338262e-01 -2.1961421e-01] Sparsity at: 0.0 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.0953 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5486488e-01 -4.8553309e-01 -2.2373247e-01] Sparsity at: 0.0 Epoch 114/500 235/235 [==============================] - 3s 13ms/step - loss: 7.1221e-04 - accuracy: 0.9999 - val_loss: 0.0925 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4833508e-01 -4.9209702e-01 -2.2393090e-01] Sparsity at: 0.0 Epoch 115/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5735e-04 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4848009e-01 -4.9562880e-01 -2.2257921e-01] Sparsity at: 0.0 Epoch 116/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3363e-04 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4881269e-01 -4.9610874e-01 -2.2035985e-01] Sparsity at: 0.0 Epoch 117/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3055e-04 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5028546e-01 -4.9697098e-01 -2.2120795e-01] Sparsity at: 0.0 Epoch 118/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4360e-04 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5199885e-01 -4.9912673e-01 -2.2096348e-01] Sparsity at: 0.0 Epoch 119/500 235/235 [==============================] - 3s 13ms/step - loss: 6.5715e-05 - accuracy: 1.0000 - val_loss: 0.0908 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5230999e-01 -5.0001198e-01 -2.2137100e-01] Sparsity at: 0.0 Epoch 120/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8175e-05 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5315089e-01 -4.9991241e-01 -2.2161718e-01] Sparsity at: 0.0 Epoch 121/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0649e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5410177e-01 -5.0104022e-01 -2.2274962e-01] Sparsity at: 0.0 Epoch 122/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7714e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5518633e-01 -5.0169367e-01 -2.2346123e-01] Sparsity at: 0.0 Epoch 123/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9011e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5656302e-01 -5.0333875e-01 -2.2264168e-01] Sparsity at: 0.0 Epoch 124/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6941e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5755949e-01 -5.0290197e-01 -2.1934767e-01] Sparsity at: 0.0 Epoch 125/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5726e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5766273e-01 -5.0362474e-01 -2.2434416e-01] Sparsity at: 0.0 Epoch 126/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9229e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5093408e-01 -5.0399685e-01 -2.2418515e-01] Sparsity at: 0.0 Epoch 127/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6980e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5330592e-01 -5.0491387e-01 -2.2462662e-01] Sparsity at: 0.0 Epoch 128/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.1414 - val_accuracy: 0.9741 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6162858e-01 -5.0079077e-01 -1.7489073e-01] Sparsity at: 0.0 Epoch 129/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0131 - accuracy: 0.9958 - val_loss: 0.1042 - val_accuracy: 0.9808 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4967439e-01 -4.7797152e-01 -1.8903105e-01] Sparsity at: 0.0 Epoch 130/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.0983 - val_accuracy: 0.9833 [-0.05450942 0.01009626 -0.00054583 ... 0.5431443 -0.47834787 -0.18943347] Sparsity at: 0.0 Epoch 131/500 235/235 [==============================] - 3s 13ms/step - loss: 6.5468e-04 - accuracy: 0.9998 - val_loss: 0.0937 - val_accuracy: 0.9834 [-0.05450942 0.01009626 -0.00054583 ... 0.54512405 -0.48063335 -0.19428793] Sparsity at: 0.0 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1556e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9838 [-0.05450942 0.01009626 -0.00054583 ... 0.5445734 -0.4832988 -0.1957009 ] Sparsity at: 0.0 Epoch 133/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2573e-04 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9840 [-0.05450942 0.01009626 -0.00054583 ... 0.5454544 -0.48659348 -0.19400413] Sparsity at: 0.0 Epoch 134/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5070e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.4615158e-01 -4.8916301e-01 -1.9545834e-01] Sparsity at: 0.0 Epoch 135/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9182e-04 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9833 [-0.05450942 0.01009626 -0.00054583 ... 0.54100174 -0.47908148 -0.19805025] Sparsity at: 0.0 Epoch 136/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3282e-04 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9834 [-0.05450942 0.01009626 -0.00054583 ... 0.5374653 -0.47435302 -0.19837436] Sparsity at: 0.0 Epoch 137/500 235/235 [==============================] - 3s 13ms/step - loss: 8.2159e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9841 [-0.05450942 0.01009626 -0.00054583 ... 0.53856033 -0.47618106 -0.20012961] Sparsity at: 0.0 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1428e-05 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9839 [-0.05450942 0.01009626 -0.00054583 ... 0.5382029 -0.47669077 -0.19918498] Sparsity at: 0.0 Epoch 139/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9968e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9841 [-0.05450942 0.01009626 -0.00054583 ... 0.5396785 -0.4784272 -0.20000376] Sparsity at: 0.0 Epoch 140/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0261e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9839 [-0.05450942 0.01009626 -0.00054583 ... 0.54026437 -0.47754407 -0.2004942 ] Sparsity at: 0.0 Epoch 141/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8617e-05 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9841 [-0.05450942 0.01009626 -0.00054583 ... 0.5418829 -0.47902533 -0.20157439] Sparsity at: 0.0 Epoch 142/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5384e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9843 [-0.05450942 0.01009626 -0.00054583 ... 0.54275215 -0.48037454 -0.20097703] Sparsity at: 0.0 Epoch 143/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0945e-05 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9844 [-0.05450942 0.01009626 -0.00054583 ... 0.54448193 -0.48220098 -0.20153861] Sparsity at: 0.0 Epoch 144/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6439e-04 - accuracy: 0.9999 - val_loss: 0.1091 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5036187e-01 -4.8188365e-01 -2.0731044e-01] Sparsity at: 0.0 Epoch 145/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 0.1226 - val_accuracy: 0.9793 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5921155e-01 -4.6094036e-01 -2.4732493e-01] Sparsity at: 0.0 Epoch 146/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9987 - val_loss: 0.0954 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6782877e-01 -4.8143187e-01 -2.4280065e-01] Sparsity at: 0.0 Epoch 147/500 235/235 [==============================] - 3s 13ms/step - loss: 7.4757e-04 - accuracy: 0.9999 - val_loss: 0.0910 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6883681e-01 -4.7698998e-01 -2.4918419e-01] Sparsity at: 0.0 Epoch 148/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3281e-04 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6755543e-01 -4.7616354e-01 -2.5090572e-01] Sparsity at: 0.0 Epoch 149/500 235/235 [==============================] - 3s 13ms/step - loss: 9.6320e-05 - accuracy: 1.0000 - val_loss: 0.0879 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6882417e-01 -4.7710124e-01 -2.5124630e-01] Sparsity at: 0.0 Epoch 150/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2292e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6861424e-01 -4.7872934e-01 -2.5126636e-01] Sparsity at: 0.0 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.2041903307203352 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.23595483062840916 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.46597818951360637 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 211s 11ms/step - loss: 5.9790e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9848 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6960404e-01 -4.7787893e-01 -2.5150684e-01] Sparsity at: 0.0 Epoch 152/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9070e-05 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7106405e-01 -4.7951719e-01 -2.5316265e-01] Sparsity at: 0.0 Epoch 153/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8152e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7169241e-01 -4.6882790e-01 -2.5356436e-01] Sparsity at: 0.0 Epoch 154/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6960e-04 - accuracy: 0.9999 - val_loss: 0.1001 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7180673e-01 -4.7230881e-01 -2.5347248e-01] Sparsity at: 0.0 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0206e-04 - accuracy: 0.9999 - val_loss: 0.0923 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7356030e-01 -4.7869748e-01 -2.5663173e-01] Sparsity at: 0.0 Epoch 156/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1034 - val_accuracy: 0.9804 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7376856e-01 -4.7249600e-01 -2.6266131e-01] Sparsity at: 0.0 Epoch 157/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1130 - val_accuracy: 0.9809 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5414444e-01 -4.5605209e-01 -2.7101049e-01] Sparsity at: 0.0 Epoch 158/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.1182 - val_accuracy: 0.9804 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6084085e-01 -4.7327054e-01 -2.6655492e-01] Sparsity at: 0.0 Epoch 159/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1045 - val_accuracy: 0.9824 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5163813e-01 -4.7459912e-01 -2.6064277e-01] Sparsity at: 0.0 Epoch 160/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0972 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5893713e-01 -4.6699986e-01 -2.6785979e-01] Sparsity at: 0.0 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2640e-04 - accuracy: 0.9998 - val_loss: 0.0917 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6246644e-01 -4.6958256e-01 -2.8054103e-01] Sparsity at: 0.0 Epoch 162/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7224e-04 - accuracy: 0.9999 - val_loss: 0.0942 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5952770e-01 -4.6740809e-01 -2.7980986e-01] Sparsity at: 0.0 Epoch 163/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1082e-04 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5863732e-01 -4.6855721e-01 -2.7815163e-01] Sparsity at: 0.0 Epoch 164/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5684e-04 - accuracy: 0.9999 - val_loss: 0.0921 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5910909e-01 -4.7185543e-01 -2.7828011e-01] Sparsity at: 0.0 Epoch 165/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1238e-04 - accuracy: 0.9999 - val_loss: 0.0895 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6067818e-01 -4.7410694e-01 -2.7886909e-01] Sparsity at: 0.0 Epoch 166/500 235/235 [==============================] - 4s 15ms/step - loss: 4.8391e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6087750e-01 -4.7298512e-01 -2.8149799e-01] Sparsity at: 0.0 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4517e-05 - accuracy: 1.0000 - val_loss: 0.0879 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6156349e-01 -4.7440800e-01 -2.8115082e-01] Sparsity at: 0.0 Epoch 168/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9775e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6080776e-01 -4.7517166e-01 -2.8209046e-01] Sparsity at: 0.0 Epoch 169/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1540e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6094074e-01 -4.7561094e-01 -2.8281242e-01] Sparsity at: 0.0 Epoch 170/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1134e-04 - accuracy: 0.9999 - val_loss: 0.0906 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7047063e-01 -4.7941777e-01 -2.9215595e-01] Sparsity at: 0.0 Epoch 171/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.1317 - val_accuracy: 0.9794 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6219161e-01 -4.7771823e-01 -2.7193606e-01] Sparsity at: 0.0 Epoch 172/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.1229 - val_accuracy: 0.9788 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5606359e-01 -5.0218654e-01 -2.8006905e-01] Sparsity at: 0.0 Epoch 173/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.1074 - val_accuracy: 0.9806 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5460852e-01 -4.8575169e-01 -2.8051063e-01] Sparsity at: 0.0 Epoch 174/500 235/235 [==============================] - 4s 15ms/step - loss: 6.5333e-04 - accuracy: 0.9998 - val_loss: 0.0927 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5542815e-01 -4.8218656e-01 -2.8372705e-01] Sparsity at: 0.0 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4194e-04 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9828 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5674964e-01 -4.8481402e-01 -2.8598803e-01] Sparsity at: 0.0 Epoch 176/500 235/235 [==============================] - 3s 13ms/step - loss: 6.1571e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5749750e-01 -4.8585925e-01 -2.8692228e-01] Sparsity at: 0.0 Epoch 177/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7130e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5812055e-01 -4.8745656e-01 -2.8622872e-01] Sparsity at: 0.0 Epoch 178/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7702e-05 - accuracy: 1.0000 - val_loss: 0.0925 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.5978703e-01 -4.8864526e-01 -2.8615063e-01] Sparsity at: 0.0 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6382e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6168419e-01 -4.9550152e-01 -2.8663486e-01] Sparsity at: 0.0 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9173e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6232744e-01 -4.9583161e-01 -2.8687659e-01] Sparsity at: 0.0 Epoch 181/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4341e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6259316e-01 -4.9520400e-01 -2.8571296e-01] Sparsity at: 0.0 Epoch 182/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1186e-05 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6331700e-01 -4.9533704e-01 -2.8492793e-01] Sparsity at: 0.0 Epoch 183/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7427e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6451696e-01 -4.9562234e-01 -2.8519151e-01] Sparsity at: 0.0 Epoch 184/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5304e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6537735e-01 -4.9554607e-01 -2.8567448e-01] Sparsity at: 0.0 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2432e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6590921e-01 -4.9582335e-01 -2.8581676e-01] Sparsity at: 0.0 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2009e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6678361e-01 -4.9645853e-01 -2.8545409e-01] Sparsity at: 0.0 Epoch 187/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1461e-05 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6724823e-01 -4.9656019e-01 -2.8577724e-01] Sparsity at: 0.0 Epoch 188/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2388e-06 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6856245e-01 -4.9697095e-01 -2.8609407e-01] Sparsity at: 0.0 Epoch 189/500 235/235 [==============================] - 3s 13ms/step - loss: 9.0506e-06 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.6950045e-01 -4.9824056e-01 -2.8709444e-01] Sparsity at: 0.0 Epoch 190/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0925e-05 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7010698e-01 -5.0036812e-01 -2.8691503e-01] Sparsity at: 0.0 Epoch 191/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2677e-04 - accuracy: 0.9999 - val_loss: 0.1199 - val_accuracy: 0.9806 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.7042623e-01 -4.8601174e-01 -2.9815650e-01] Sparsity at: 0.0 Epoch 192/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0097 - accuracy: 0.9970 - val_loss: 0.1167 - val_accuracy: 0.9804 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.8843267e-01 -5.1557732e-01 -3.0202088e-01] Sparsity at: 0.0 Epoch 193/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9989 - val_loss: 0.1044 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0070002e-01 -5.1226181e-01 -3.0256060e-01] Sparsity at: 0.0 Epoch 194/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7385e-04 - accuracy: 0.9998 - val_loss: 0.0976 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 5.9982592e-01 -5.2158481e-01 -2.9850927e-01] Sparsity at: 0.0 Epoch 195/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4735e-04 - accuracy: 0.9999 - val_loss: 0.1038 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0121626e-01 -5.2410632e-01 -2.9390094e-01] Sparsity at: 0.0 Epoch 196/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2273e-04 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0332149e-01 -5.1959753e-01 -2.9815087e-01] Sparsity at: 0.0 Epoch 197/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7156e-05 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0324466e-01 -5.2098483e-01 -2.9800597e-01] Sparsity at: 0.0 Epoch 198/500 235/235 [==============================] - 3s 13ms/step - loss: 4.8372e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0329521e-01 -5.2132809e-01 -2.9784495e-01] Sparsity at: 0.0 Epoch 199/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2301e-05 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0370916e-01 -5.2256691e-01 -2.9715100e-01] Sparsity at: 0.0 Epoch 200/500 235/235 [==============================] - 3s 13ms/step - loss: 8.1394e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0448521e-01 -5.2294427e-01 -2.9898939e-01] Sparsity at: 0.0 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.2874537197822491 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.3186411199774071 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.5636029235072613 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 204s 11ms/step - loss: 8.4325e-04 - accuracy: 0.9998 - val_loss: 0.1146 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0950238e-01 -4.9925497e-01 -3.3555302e-01] Sparsity at: 0.0 Epoch 202/500 235/235 [==============================] - 3s 12ms/step - loss: 4.8157e-04 - accuracy: 0.9998 - val_loss: 0.1110 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2005216e-01 -5.0502318e-01 -3.3103040e-01] Sparsity at: 0.0 Epoch 203/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7465e-04 - accuracy: 0.9999 - val_loss: 0.1037 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1956078e-01 -5.0318015e-01 -3.3429950e-01] Sparsity at: 0.0 Epoch 204/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0407e-04 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1864352e-01 -5.0518948e-01 -3.3523580e-01] Sparsity at: 0.0 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1573e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2882578e-01 -5.0963444e-01 -3.3437878e-01] Sparsity at: 0.0 Epoch 206/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4746e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2864375e-01 -5.0653112e-01 -3.3664536e-01] Sparsity at: 0.0 Epoch 207/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1144 - val_accuracy: 0.9819 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1742079e-01 -5.0197595e-01 -3.3811587e-01] Sparsity at: 0.0 Epoch 208/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1166 - val_accuracy: 0.9819 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3147867e-01 -5.0792593e-01 -3.3347246e-01] Sparsity at: 0.0 Epoch 209/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1295 - val_accuracy: 0.9800 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1288697e-01 -5.3304881e-01 -3.4310421e-01] Sparsity at: 0.0 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1074 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1201382e-01 -5.4386103e-01 -3.5203356e-01] Sparsity at: 0.0 Epoch 211/500 235/235 [==============================] - 4s 15ms/step - loss: 4.4846e-04 - accuracy: 0.9999 - val_loss: 0.1057 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1244524e-01 -5.5831331e-01 -3.5050705e-01] Sparsity at: 0.0 Epoch 212/500 235/235 [==============================] - 4s 15ms/step - loss: 6.1539e-04 - accuracy: 0.9998 - val_loss: 0.1031 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1057192e-01 -5.4338759e-01 -3.5213125e-01] Sparsity at: 0.0 Epoch 213/500 235/235 [==============================] - 4s 16ms/step - loss: 1.4683e-04 - accuracy: 1.0000 - val_loss: 0.1002 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1560261e-01 -5.4469484e-01 -3.5494003e-01] Sparsity at: 0.0 Epoch 214/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7782e-05 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1470604e-01 -5.4480261e-01 -3.5545653e-01] Sparsity at: 0.0 Epoch 215/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9864e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1483705e-01 -5.4412687e-01 -3.5600471e-01] Sparsity at: 0.0 Epoch 216/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9205e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1407834e-01 -5.4772061e-01 -3.5647425e-01] Sparsity at: 0.0 Epoch 217/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1620e-05 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1434227e-01 -5.4673374e-01 -3.5614845e-01] Sparsity at: 0.0 Epoch 218/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3827e-05 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1519468e-01 -5.4605353e-01 -3.5597736e-01] Sparsity at: 0.0 Epoch 219/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4964e-05 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9848 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1545092e-01 -5.4578584e-01 -3.5559243e-01] Sparsity at: 0.0 Epoch 220/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5088e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1640811e-01 -5.4461604e-01 -3.5773984e-01] Sparsity at: 0.0 Epoch 221/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2197e-04 - accuracy: 0.9998 - val_loss: 0.1117 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2371159e-01 -5.4464835e-01 -3.4565946e-01] Sparsity at: 0.0 Epoch 222/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1337 - val_accuracy: 0.9816 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2378913e-01 -5.5754745e-01 -3.4456840e-01] Sparsity at: 0.0 Epoch 223/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.1125 - val_accuracy: 0.9810 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1221707e-01 -5.3908062e-01 -3.2429215e-01] Sparsity at: 0.0 Epoch 224/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.1025 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0205883e-01 -5.1781064e-01 -3.2461664e-01] Sparsity at: 0.0 Epoch 225/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0683e-04 - accuracy: 0.9998 - val_loss: 0.1032 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0636050e-01 -5.3595763e-01 -3.3571878e-01] Sparsity at: 0.0 Epoch 226/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5532e-04 - accuracy: 0.9999 - val_loss: 0.1008 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0159755e-01 -5.3635305e-01 -3.3443895e-01] Sparsity at: 0.0 Epoch 227/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4194e-05 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0271400e-01 -5.3576726e-01 -3.3441862e-01] Sparsity at: 0.0 Epoch 228/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1829e-05 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0365713e-01 -5.3585714e-01 -3.3521560e-01] Sparsity at: 0.0 Epoch 229/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6280e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0427231e-01 -5.3575593e-01 -3.3483583e-01] Sparsity at: 0.0 Epoch 230/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6473e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0428178e-01 -5.3447664e-01 -3.3509734e-01] Sparsity at: 0.0 Epoch 231/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3108e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0493410e-01 -5.3514642e-01 -3.3485800e-01] Sparsity at: 0.0 Epoch 232/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4552e-05 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0522890e-01 -5.3617030e-01 -3.3513358e-01] Sparsity at: 0.0 Epoch 233/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3754e-05 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0550493e-01 -5.3695834e-01 -3.3578059e-01] Sparsity at: 0.0 Epoch 234/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3490e-05 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0590279e-01 -5.3796405e-01 -3.3490509e-01] Sparsity at: 0.0 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5802e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0634965e-01 -5.3727800e-01 -3.3665434e-01] Sparsity at: 0.0 Epoch 236/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1274 - val_accuracy: 0.9801 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2343144e-01 -5.2422190e-01 -3.4441346e-01] Sparsity at: 0.0 Epoch 237/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9986 - val_loss: 0.1342 - val_accuracy: 0.9797 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0570931e-01 -5.2343947e-01 -3.6213100e-01] Sparsity at: 0.0 Epoch 238/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1082 - val_accuracy: 0.9818 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1439687e-01 -5.1665121e-01 -3.5663036e-01] Sparsity at: 0.0 Epoch 239/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1581e-04 - accuracy: 0.9999 - val_loss: 0.1090 - val_accuracy: 0.9825 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.0527170e-01 -5.2081329e-01 -3.4511650e-01] Sparsity at: 0.0 Epoch 240/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2663e-04 - accuracy: 0.9999 - val_loss: 0.1035 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2369365e-01 -5.2157253e-01 -3.5754129e-01] Sparsity at: 0.0 Epoch 241/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1863e-04 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3262939e-01 -5.2139896e-01 -3.5675633e-01] Sparsity at: 0.0 Epoch 242/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9487e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9828 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2323916e-01 -5.2096403e-01 -3.5696104e-01] Sparsity at: 0.0 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9562e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2304211e-01 -5.2134120e-01 -3.5657158e-01] Sparsity at: 0.0 Epoch 244/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7930e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2343466e-01 -5.2140492e-01 -3.5696056e-01] Sparsity at: 0.0 Epoch 245/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8687e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2266308e-01 -5.2152824e-01 -3.5601309e-01] Sparsity at: 0.0 Epoch 246/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3092e-05 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2398028e-01 -5.2178323e-01 -3.5591933e-01] Sparsity at: 0.0 Epoch 247/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6499e-05 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9827 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2495810e-01 -5.2217859e-01 -3.5527581e-01] Sparsity at: 0.0 Epoch 248/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1571e-05 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2548947e-01 -5.2138007e-01 -3.5780495e-01] Sparsity at: 0.0 Epoch 249/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2561e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2659645e-01 -5.2187186e-01 -3.5933426e-01] Sparsity at: 0.0 Epoch 250/500 235/235 [==============================] - 3s 13ms/step - loss: 9.4007e-06 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2719643e-01 -5.2243006e-01 -3.5930550e-01] Sparsity at: 0.0 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.38124093667401837 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.4081035179382546 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.6657418879585748 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 209s 11ms/step - loss: 1.2386e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2878633e-01 -5.2314866e-01 -3.5944691e-01] Sparsity at: 0.0 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2838e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2868971e-01 -5.2224553e-01 -3.5884094e-01] Sparsity at: 0.0 Epoch 253/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0250e-04 - accuracy: 0.9998 - val_loss: 0.1213 - val_accuracy: 0.9817 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5136111e-01 -5.2299011e-01 -3.5210609e-01] Sparsity at: 0.0 Epoch 254/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.1222 - val_accuracy: 0.9821 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2739509e-01 -4.9890453e-01 -3.3374158e-01] Sparsity at: 0.0 Epoch 255/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0960 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3421464e-01 -5.2200115e-01 -3.3001900e-01] Sparsity at: 0.0 Epoch 256/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9957e-04 - accuracy: 0.9998 - val_loss: 0.0928 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2529624e-01 -5.2404702e-01 -3.2779345e-01] Sparsity at: 0.0 Epoch 257/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0174e-04 - accuracy: 0.9999 - val_loss: 0.1015 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2676585e-01 -5.2519840e-01 -3.2440919e-01] Sparsity at: 0.0 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7626e-04 - accuracy: 0.9999 - val_loss: 0.1015 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.1347389e-01 -5.2910566e-01 -3.2490751e-01] Sparsity at: 0.0 Epoch 259/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4313e-04 - accuracy: 0.9999 - val_loss: 0.0976 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.2336451e-01 -5.2847540e-01 -3.2726857e-01] Sparsity at: 0.0 Epoch 260/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2480e-04 - accuracy: 0.9999 - val_loss: 0.1010 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3136858e-01 -5.2624869e-01 -3.3697766e-01] Sparsity at: 0.0 Epoch 261/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0950e-04 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3385004e-01 -5.2697378e-01 -3.3868650e-01] Sparsity at: 0.0 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5391e-05 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3998175e-01 -5.2718550e-01 -3.3963835e-01] Sparsity at: 0.0 Epoch 263/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6144e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3847589e-01 -5.2757126e-01 -3.4037256e-01] Sparsity at: 0.0 Epoch 264/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7041e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3930726e-01 -5.2752674e-01 -3.4020925e-01] Sparsity at: 0.0 Epoch 265/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1979e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3923216e-01 -5.2727044e-01 -3.4029010e-01] Sparsity at: 0.0 Epoch 266/500 235/235 [==============================] - 3s 13ms/step - loss: 9.7601e-06 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3910252e-01 -5.2789539e-01 -3.4019661e-01] Sparsity at: 0.0 Epoch 267/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5079e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.3944036e-01 -5.2793550e-01 -3.4031159e-01] Sparsity at: 0.0 Epoch 268/500 235/235 [==============================] - 3s 13ms/step - loss: 9.5232e-06 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4026928e-01 -5.2946043e-01 -3.4021139e-01] Sparsity at: 0.0 Epoch 269/500 235/235 [==============================] - 3s 13ms/step - loss: 9.5407e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4353728e-01 -5.2862191e-01 -3.4088215e-01] Sparsity at: 0.0 Epoch 270/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0790e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4340949e-01 -5.2913517e-01 -3.4093595e-01] Sparsity at: 0.0 Epoch 271/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2693e-06 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4388776e-01 -5.2918202e-01 -3.4088764e-01] Sparsity at: 0.0 Epoch 272/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1534e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4251763e-01 -5.3003001e-01 -3.3886793e-01] Sparsity at: 0.0 Epoch 273/500 235/235 [==============================] - 3s 13ms/step - loss: 6.5365e-06 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4144254e-01 -5.3202510e-01 -3.3753058e-01] Sparsity at: 0.0 Epoch 274/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0968e-06 - accuracy: 1.0000 - val_loss: 0.0958 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4319319e-01 -5.3212756e-01 -3.3813250e-01] Sparsity at: 0.0 Epoch 275/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5047e-06 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4375597e-01 -5.3256160e-01 -3.3832178e-01] Sparsity at: 0.0 Epoch 276/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0697e-06 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4356261e-01 -5.3301734e-01 -3.3845496e-01] Sparsity at: 0.0 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0959e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4830291e-01 -5.3391296e-01 -3.3869076e-01] Sparsity at: 0.0 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5371e-06 - accuracy: 1.0000 - val_loss: 0.0966 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4803529e-01 -5.3480995e-01 -3.3888236e-01] Sparsity at: 0.0 Epoch 279/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1643e-06 - accuracy: 1.0000 - val_loss: 0.0969 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4931709e-01 -5.3604400e-01 -3.3942103e-01] Sparsity at: 0.0 Epoch 280/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5175e-06 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4941317e-01 -5.3699249e-01 -3.3933860e-01] Sparsity at: 0.0 Epoch 281/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8655e-06 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5151393e-01 -5.3807986e-01 -3.3978361e-01] Sparsity at: 0.0 Epoch 282/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6941e-06 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5202975e-01 -5.3808928e-01 -3.3889192e-01] Sparsity at: 0.0 Epoch 283/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4472e-06 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5303344e-01 -5.3946525e-01 -3.4164351e-01] Sparsity at: 0.0 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3050e-06 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5430802e-01 -5.4000169e-01 -3.4160930e-01] Sparsity at: 0.0 Epoch 285/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7688e-06 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5452904e-01 -5.4090899e-01 -3.4107906e-01] Sparsity at: 0.0 Epoch 286/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7692e-06 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5524411e-01 -5.4166412e-01 -3.4125334e-01] Sparsity at: 0.0 Epoch 287/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4157e-06 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5493286e-01 -5.4281932e-01 -3.4182611e-01] Sparsity at: 0.0 Epoch 288/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2568e-06 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5572679e-01 -5.4265201e-01 -3.4187028e-01] Sparsity at: 0.0 Epoch 289/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9160e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5667081e-01 -5.4489005e-01 -3.4288964e-01] Sparsity at: 0.0 Epoch 290/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1954e-06 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5741098e-01 -5.4669249e-01 -3.4242389e-01] Sparsity at: 0.0 Epoch 291/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2667e-06 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5799427e-01 -5.5042946e-01 -3.4277761e-01] Sparsity at: 0.0 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5017e-05 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9760 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5678263e-01 -5.5002117e-01 -3.4314448e-01] Sparsity at: 0.0 Epoch 293/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0093 - accuracy: 0.9975 - val_loss: 0.1343 - val_accuracy: 0.9806 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.6712904e-01 -5.5039155e-01 -3.6629134e-01] Sparsity at: 0.0 Epoch 294/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1173 - val_accuracy: 0.9818 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4340782e-01 -5.5193847e-01 -3.7858319e-01] Sparsity at: 0.0 Epoch 295/500 235/235 [==============================] - 3s 13ms/step - loss: 5.7169e-04 - accuracy: 0.9998 - val_loss: 0.1111 - val_accuracy: 0.9827 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4791560e-01 -5.5488485e-01 -3.7428433e-01] Sparsity at: 0.0 Epoch 296/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3328e-04 - accuracy: 0.9999 - val_loss: 0.1085 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4995569e-01 -5.5822307e-01 -3.7549898e-01] Sparsity at: 0.0 Epoch 297/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3872e-04 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9823 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5111738e-01 -5.5866361e-01 -3.7619758e-01] Sparsity at: 0.0 Epoch 298/500 235/235 [==============================] - 3s 13ms/step - loss: 6.9456e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4915556e-01 -5.5945152e-01 -3.7620771e-01] Sparsity at: 0.0 Epoch 299/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9311e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9828 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4924091e-01 -5.5903774e-01 -3.7618771e-01] Sparsity at: 0.0 Epoch 300/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4901e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.4857632e-01 -5.5785728e-01 -3.7585333e-01] Sparsity at: 0.0 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.4840575341487643 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.5021257151600054 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.7491546624237984 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 210s 11ms/step - loss: 6.5044e-05 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9828 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5228260e-01 -5.5734634e-01 -3.7600517e-01] Sparsity at: 0.0 Epoch 302/500 235/235 [==============================] - 3s 12ms/step - loss: 3.7288e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5375954e-01 -5.5553335e-01 -3.7614349e-01] Sparsity at: 0.0 Epoch 303/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6152e-05 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5406585e-01 -5.6139666e-01 -3.7696561e-01] Sparsity at: 0.0 Epoch 304/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4945e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5418690e-01 -5.6010503e-01 -3.7628663e-01] Sparsity at: 0.0 Epoch 305/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1716e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5429169e-01 -5.6056571e-01 -3.7585855e-01] Sparsity at: 0.0 Epoch 306/500 235/235 [==============================] - 3s 13ms/step - loss: 7.4003e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9823 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5462208e-01 -5.6075162e-01 -3.7579769e-01] Sparsity at: 0.0 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1395 - val_accuracy: 0.9799 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.5355134e-01 -5.2712643e-01 -3.8587490e-01] Sparsity at: 0.0 Epoch 308/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9993 - val_loss: 0.1451 - val_accuracy: 0.9794 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7497033e-01 -5.3689229e-01 -3.9461696e-01] Sparsity at: 0.0 Epoch 309/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1170 - val_accuracy: 0.9812 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.6212726e-01 -5.4914683e-01 -4.0399969e-01] Sparsity at: 0.0 Epoch 310/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1239 - val_accuracy: 0.9816 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.6534543e-01 -5.5245560e-01 -3.7220427e-01] Sparsity at: 0.0 Epoch 311/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3574e-04 - accuracy: 0.9998 - val_loss: 0.1141 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7173475e-01 -5.5312985e-01 -3.7821293e-01] Sparsity at: 0.0 Epoch 312/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0177e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7269552e-01 -5.5374986e-01 -3.8368362e-01] Sparsity at: 0.0 Epoch 313/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5104e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7167526e-01 -5.5432463e-01 -3.8489488e-01] Sparsity at: 0.0 Epoch 314/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6261e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7177296e-01 -5.5459368e-01 -3.8556916e-01] Sparsity at: 0.0 Epoch 315/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6205e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7240047e-01 -5.5320251e-01 -3.8584906e-01] Sparsity at: 0.0 Epoch 316/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4158e-05 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7290407e-01 -5.5384910e-01 -3.8663006e-01] Sparsity at: 0.0 Epoch 317/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3918e-05 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7150152e-01 -5.5443913e-01 -3.8646147e-01] Sparsity at: 0.0 Epoch 318/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5730e-05 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7176443e-01 -5.5571240e-01 -3.8649982e-01] Sparsity at: 0.0 Epoch 319/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2412e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7181522e-01 -5.5701846e-01 -3.8615495e-01] Sparsity at: 0.0 Epoch 320/500 235/235 [==============================] - 3s 13ms/step - loss: 8.8805e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7128992e-01 -5.5714381e-01 -3.8658482e-01] Sparsity at: 0.0 Epoch 321/500 235/235 [==============================] - 3s 13ms/step - loss: 7.4642e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7117435e-01 -5.5738598e-01 -3.8607830e-01] Sparsity at: 0.0 Epoch 322/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3088e-06 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.7052221e-01 -5.5736411e-01 -3.8592941e-01] Sparsity at: 0.0 Epoch 323/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1410 - val_accuracy: 0.9797 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.6706741e-01 -5.7001120e-01 -3.9759171e-01] Sparsity at: 0.0 Epoch 324/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1418 - val_accuracy: 0.9801 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.8438065e-01 -5.2542758e-01 -3.6692393e-01] Sparsity at: 0.0 Epoch 325/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1284 - val_accuracy: 0.9821 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1262634e-01 -5.4822046e-01 -3.8940325e-01] Sparsity at: 0.0 Epoch 326/500 235/235 [==============================] - 3s 13ms/step - loss: 8.9549e-04 - accuracy: 0.9998 - val_loss: 0.1225 - val_accuracy: 0.9821 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0459276e-01 -5.5135691e-01 -3.7489915e-01] Sparsity at: 0.0 Epoch 327/500 235/235 [==============================] - 3s 13ms/step - loss: 6.2977e-04 - accuracy: 0.9998 - val_loss: 0.1173 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0080155e-01 -5.6030804e-01 -3.7608519e-01] Sparsity at: 0.0 Epoch 328/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2176e-04 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.9982362e-01 -5.6664425e-01 -3.7336165e-01] Sparsity at: 0.0 Epoch 329/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0171e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 6.9964826e-01 -5.6587100e-01 -3.7368536e-01] Sparsity at: 0.0 Epoch 330/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5569e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0078719e-01 -5.6707168e-01 -3.7355444e-01] Sparsity at: 0.0 Epoch 331/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7089e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0200819e-01 -5.6650913e-01 -3.7195584e-01] Sparsity at: 0.0 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4384e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0109540e-01 -5.6838727e-01 -3.7140197e-01] Sparsity at: 0.0 Epoch 333/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1887e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0155942e-01 -5.6862968e-01 -3.7182504e-01] Sparsity at: 0.0 Epoch 334/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0908e-04 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0635587e-01 -5.6764275e-01 -3.7442493e-01] Sparsity at: 0.0 Epoch 335/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3605e-05 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0906579e-01 -5.6291771e-01 -3.7445685e-01] Sparsity at: 0.0 Epoch 336/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5988e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0489526e-01 -5.6233752e-01 -3.7392166e-01] Sparsity at: 0.0 Epoch 337/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8824e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9848 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0397627e-01 -5.6645334e-01 -3.6914515e-01] Sparsity at: 0.0 Epoch 338/500 235/235 [==============================] - 3s 13ms/step - loss: 9.5715e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0395643e-01 -5.6660706e-01 -3.6929032e-01] Sparsity at: 0.0 Epoch 339/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0100e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0404893e-01 -5.6640774e-01 -3.6937401e-01] Sparsity at: 0.0 Epoch 340/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1861e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0460624e-01 -5.6706387e-01 -3.7056348e-01] Sparsity at: 0.0 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0278e-05 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0501941e-01 -5.6745756e-01 -3.7264466e-01] Sparsity at: 0.0 Epoch 342/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9632e-05 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0786303e-01 -5.6676489e-01 -3.7610215e-01] Sparsity at: 0.0 Epoch 343/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1395 - val_accuracy: 0.9809 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5297624e-01 -5.6421214e-01 -4.0648663e-01] Sparsity at: 0.0 Epoch 344/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.1281 - val_accuracy: 0.9807 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1896613e-01 -5.6390363e-01 -3.9479387e-01] Sparsity at: 0.0 Epoch 345/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3142e-04 - accuracy: 0.9997 - val_loss: 0.1125 - val_accuracy: 0.9826 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2380459e-01 -5.6272143e-01 -3.9444566e-01] Sparsity at: 0.0 Epoch 346/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6689e-04 - accuracy: 0.9999 - val_loss: 0.1127 - val_accuracy: 0.9819 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1746206e-01 -5.6643170e-01 -3.9546755e-01] Sparsity at: 0.0 Epoch 347/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2436e-04 - accuracy: 1.0000 - val_loss: 0.1128 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1617639e-01 -5.6855470e-01 -3.9525202e-01] Sparsity at: 0.0 Epoch 348/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1092e-04 - accuracy: 0.9999 - val_loss: 0.1148 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1348655e-01 -5.6832910e-01 -3.9307153e-01] Sparsity at: 0.0 Epoch 349/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5693e-05 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1369278e-01 -5.6889236e-01 -3.9391711e-01] Sparsity at: 0.0 Epoch 350/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6908e-05 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1477169e-01 -5.6885922e-01 -3.9647427e-01] Sparsity at: 0.0 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.5940021586559041 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.5993241925912614 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.8300940364296068 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 214s 11ms/step - loss: 1.8836e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1529114e-01 -5.6959337e-01 -3.9495748e-01] Sparsity at: 0.0 Epoch 352/500 235/235 [==============================] - 3s 12ms/step - loss: 1.4700e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1578276e-01 -5.7047671e-01 -3.9545009e-01] Sparsity at: 0.0 Epoch 353/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5937e-05 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2179407e-01 -5.7083851e-01 -3.9582655e-01] Sparsity at: 0.0 Epoch 354/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7916e-04 - accuracy: 0.9999 - val_loss: 0.1170 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2937107e-01 -5.6852484e-01 -4.0021750e-01] Sparsity at: 0.0 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1260 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1368587e-01 -5.7733423e-01 -3.8858193e-01] Sparsity at: 0.0 Epoch 356/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1325 - val_accuracy: 0.9808 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1448648e-01 -5.6969279e-01 -3.8571319e-01] Sparsity at: 0.0 Epoch 357/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1265 - val_accuracy: 0.9820 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0180959e-01 -5.7633668e-01 -4.0235785e-01] Sparsity at: 0.0 Epoch 358/500 235/235 [==============================] - 3s 13ms/step - loss: 4.8881e-04 - accuracy: 0.9999 - val_loss: 0.1182 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0467728e-01 -5.6393576e-01 -4.0121877e-01] Sparsity at: 0.0 Epoch 359/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0939e-04 - accuracy: 0.9999 - val_loss: 0.1183 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0579892e-01 -5.6408697e-01 -4.0253165e-01] Sparsity at: 0.0 Epoch 360/500 235/235 [==============================] - 3s 13ms/step - loss: 6.9575e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0301265e-01 -5.6143004e-01 -4.0327480e-01] Sparsity at: 0.0 Epoch 361/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3759e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0206022e-01 -5.5868477e-01 -4.0360621e-01] Sparsity at: 0.0 Epoch 362/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6491e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0208901e-01 -5.6069535e-01 -4.0419844e-01] Sparsity at: 0.0 Epoch 363/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5677e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0221454e-01 -5.6052208e-01 -4.0401483e-01] Sparsity at: 0.0 Epoch 364/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0043e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0208615e-01 -5.6182092e-01 -4.0402654e-01] Sparsity at: 0.0 Epoch 365/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4164e-05 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0283574e-01 -5.6466419e-01 -4.0959105e-01] Sparsity at: 0.0 Epoch 366/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8409e-04 - accuracy: 0.9999 - val_loss: 0.1247 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0692307e-01 -5.6885320e-01 -4.0941599e-01] Sparsity at: 0.0 Epoch 367/500 235/235 [==============================] - 3s 13ms/step - loss: 7.9737e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9858 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0750093e-01 -5.7378286e-01 -4.0056372e-01] Sparsity at: 0.0 Epoch 368/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4833e-05 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9857 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0882583e-01 -5.7588649e-01 -4.0319535e-01] Sparsity at: 0.0 Epoch 369/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4380e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9862 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0953685e-01 -5.8122599e-01 -4.0075362e-01] Sparsity at: 0.0 Epoch 370/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4355e-05 - accuracy: 1.0000 - val_loss: 0.1111 - val_accuracy: 0.9855 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.0993143e-01 -5.9116876e-01 -3.8649681e-01] Sparsity at: 0.0 Epoch 371/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0129e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1026665e-01 -5.9196216e-01 -3.8612637e-01] Sparsity at: 0.0 Epoch 372/500 235/235 [==============================] - 3s 13ms/step - loss: 5.7895e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9857 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1015251e-01 -5.9211612e-01 -3.8570943e-01] Sparsity at: 0.0 Epoch 373/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9729e-06 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1017760e-01 -5.9285569e-01 -3.8497800e-01] Sparsity at: 0.0 Epoch 374/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1339e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9856 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1025163e-01 -5.9300107e-01 -3.8514194e-01] Sparsity at: 0.0 Epoch 375/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2927e-06 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1067280e-01 -5.9037477e-01 -3.8439739e-01] Sparsity at: 0.0 Epoch 376/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3205e-06 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9855 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1095383e-01 -5.9109157e-01 -3.8548061e-01] Sparsity at: 0.0 Epoch 377/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8044e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9855 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1098715e-01 -5.9215111e-01 -3.8554680e-01] Sparsity at: 0.0 Epoch 378/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5292e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1126789e-01 -5.9289366e-01 -3.8870525e-01] Sparsity at: 0.0 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7128e-06 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9855 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1131331e-01 -5.9336215e-01 -3.8855308e-01] Sparsity at: 0.0 Epoch 380/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5147e-06 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1130067e-01 -5.9428436e-01 -3.8712949e-01] Sparsity at: 0.0 Epoch 381/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3417e-06 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1592885e-01 -5.9577453e-01 -3.8571432e-01] Sparsity at: 0.0 Epoch 382/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4059e-06 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1367675e-01 -5.9682691e-01 -3.8676134e-01] Sparsity at: 0.0 Epoch 383/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1154e-06 - accuracy: 1.0000 - val_loss: 0.1111 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1473700e-01 -5.9591788e-01 -3.8876802e-01] Sparsity at: 0.0 Epoch 384/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7320e-06 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9855 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1423244e-01 -5.9560019e-01 -3.8925385e-01] Sparsity at: 0.0 Epoch 385/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3127e-06 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1459347e-01 -5.9645391e-01 -3.8985214e-01] Sparsity at: 0.0 Epoch 386/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0702e-06 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1544689e-01 -5.9748000e-01 -3.8986361e-01] Sparsity at: 0.0 Epoch 387/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5183e-06 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1546721e-01 -5.9847289e-01 -3.8869891e-01] Sparsity at: 0.0 Epoch 388/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2015e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1595049e-01 -6.0001868e-01 -3.8917929e-01] Sparsity at: 0.0 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1568e-06 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1584302e-01 -6.0047722e-01 -3.8693663e-01] Sparsity at: 0.0 Epoch 390/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5372e-07 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.1574992e-01 -6.0128659e-01 -3.8694969e-01] Sparsity at: 0.0 Epoch 391/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.1903 - val_accuracy: 0.9718 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7610165e-01 -5.9374231e-01 -4.4736576e-01] Sparsity at: 0.0 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 0.1312 - val_accuracy: 0.9809 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2421026e-01 -5.7925481e-01 -4.3199533e-01] Sparsity at: 0.0 Epoch 393/500 235/235 [==============================] - 3s 13ms/step - loss: 7.5205e-04 - accuracy: 0.9998 - val_loss: 0.1178 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2492504e-01 -5.7882887e-01 -4.1366154e-01] Sparsity at: 0.0 Epoch 394/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6470e-04 - accuracy: 0.9999 - val_loss: 0.1091 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3260576e-01 -5.7625413e-01 -4.2663273e-01] Sparsity at: 0.0 Epoch 395/500 235/235 [==============================] - 3s 13ms/step - loss: 7.8114e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3305577e-01 -5.8003402e-01 -4.2172727e-01] Sparsity at: 0.0 Epoch 396/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4253e-05 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9848 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3229116e-01 -5.8007765e-01 -4.2126936e-01] Sparsity at: 0.0 Epoch 397/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6762e-05 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3286659e-01 -5.7917684e-01 -4.2010236e-01] Sparsity at: 0.0 Epoch 398/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5279e-04 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3296195e-01 -5.7867706e-01 -4.2213607e-01] Sparsity at: 0.0 Epoch 399/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2817e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3408920e-01 -5.7804376e-01 -4.2255884e-01] Sparsity at: 0.0 Epoch 400/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1646e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3430389e-01 -5.7770503e-01 -4.2296356e-01] Sparsity at: 0.0 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.6727506433394481 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6748570536334668 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.8916560984470863 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 209s 11ms/step - loss: 1.0476e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9856 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3461992e-01 -5.7798070e-01 -4.2328224e-01] Sparsity at: 0.0 Epoch 402/500 235/235 [==============================] - 3s 12ms/step - loss: 1.0708e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9857 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3501724e-01 -5.7910603e-01 -4.2368123e-01] Sparsity at: 0.0 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4804e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9862 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3581117e-01 -5.7880759e-01 -4.2384246e-01] Sparsity at: 0.0 Epoch 404/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8060e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3704863e-01 -5.7966167e-01 -4.2404339e-01] Sparsity at: 0.0 Epoch 405/500 235/235 [==============================] - 3s 13ms/step - loss: 9.8769e-06 - accuracy: 1.0000 - val_loss: 0.1020 - val_accuracy: 0.9855 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3696053e-01 -5.8020210e-01 -4.2402256e-01] Sparsity at: 0.0 Epoch 406/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4792e-06 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3773038e-01 -5.8093137e-01 -4.2508972e-01] Sparsity at: 0.0 Epoch 407/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1250e-06 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9856 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3812926e-01 -5.8083844e-01 -4.2523092e-01] Sparsity at: 0.0 Epoch 408/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6084e-06 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3855639e-01 -5.8066791e-01 -4.2530608e-01] Sparsity at: 0.0 Epoch 409/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1166 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4693584e-01 -5.7909679e-01 -4.2715743e-01] Sparsity at: 0.0 Epoch 410/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.1236 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3885489e-01 -5.7413155e-01 -4.0021637e-01] Sparsity at: 0.0 Epoch 411/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1124 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4534070e-01 -5.7154894e-01 -3.6661658e-01] Sparsity at: 0.0 Epoch 412/500 235/235 [==============================] - 3s 13ms/step - loss: 6.6900e-04 - accuracy: 0.9998 - val_loss: 0.1116 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5580573e-01 -5.7684404e-01 -3.8299996e-01] Sparsity at: 0.0 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0554e-04 - accuracy: 0.9999 - val_loss: 0.1111 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5910127e-01 -5.8389193e-01 -3.8520378e-01] Sparsity at: 0.0 Epoch 414/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9216e-04 - accuracy: 0.9999 - val_loss: 0.1103 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7360153e-01 -5.8851850e-01 -3.8928694e-01] Sparsity at: 0.0 Epoch 415/500 235/235 [==============================] - 3s 13ms/step - loss: 8.0992e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7152365e-01 -5.8784527e-01 -3.9880115e-01] Sparsity at: 0.0 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6261e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7058154e-01 -5.8703071e-01 -3.9640656e-01] Sparsity at: 0.0 Epoch 417/500 235/235 [==============================] - 4s 15ms/step - loss: 2.1384e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6968920e-01 -5.8879471e-01 -3.9701700e-01] Sparsity at: 0.0 Epoch 418/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1601e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6954162e-01 -5.8925849e-01 -3.9707559e-01] Sparsity at: 0.0 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6450e-06 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6944101e-01 -5.8986121e-01 -3.9709815e-01] Sparsity at: 0.0 Epoch 420/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0065e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6967978e-01 -5.9008545e-01 -3.9717415e-01] Sparsity at: 0.0 Epoch 421/500 235/235 [==============================] - 3s 13ms/step - loss: 6.1661e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6935273e-01 -5.8983612e-01 -3.9734587e-01] Sparsity at: 0.0 Epoch 422/500 235/235 [==============================] - 3s 13ms/step - loss: 5.6247e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6958573e-01 -5.9043860e-01 -3.9740649e-01] Sparsity at: 0.0 Epoch 423/500 235/235 [==============================] - 3s 13ms/step - loss: 6.2170e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6981461e-01 -5.9111857e-01 -3.9734247e-01] Sparsity at: 0.0 Epoch 424/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4751e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6967418e-01 -5.9147847e-01 -3.9737681e-01] Sparsity at: 0.0 Epoch 425/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1320e-06 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7006787e-01 -5.9181589e-01 -3.9693892e-01] Sparsity at: 0.0 Epoch 426/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2057e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9848 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6945156e-01 -5.9196174e-01 -3.9696586e-01] Sparsity at: 0.0 Epoch 427/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4202e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6864702e-01 -5.9245372e-01 -3.9619201e-01] Sparsity at: 0.0 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0746e-06 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6872611e-01 -5.9274894e-01 -3.9603540e-01] Sparsity at: 0.0 Epoch 429/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7672e-06 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6917666e-01 -5.9277546e-01 -3.9620230e-01] Sparsity at: 0.0 Epoch 430/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7326e-06 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6946557e-01 -5.9260476e-01 -3.9626563e-01] Sparsity at: 0.0 Epoch 431/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0894e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6955831e-01 -5.9552687e-01 -3.9629373e-01] Sparsity at: 0.0 Epoch 432/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1424e-06 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6966625e-01 -5.9631538e-01 -3.9657286e-01] Sparsity at: 0.0 Epoch 433/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5376e-06 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9848 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6966262e-01 -5.9683400e-01 -3.9721814e-01] Sparsity at: 0.0 Epoch 434/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1321e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6971877e-01 -5.9718692e-01 -3.9651179e-01] Sparsity at: 0.0 Epoch 435/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6499e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6947546e-01 -5.9729528e-01 -3.9698821e-01] Sparsity at: 0.0 Epoch 436/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2924e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6969296e-01 -5.9761596e-01 -3.9709359e-01] Sparsity at: 0.0 Epoch 437/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3264e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7013183e-01 -5.9810382e-01 -3.9718273e-01] Sparsity at: 0.0 Epoch 438/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6109e-04 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9758 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6544523e-01 -5.9299684e-01 -3.9017054e-01] Sparsity at: 0.0 Epoch 439/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9977 - val_loss: 0.1308 - val_accuracy: 0.9816 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3219794e-01 -5.9769565e-01 -3.4375000e-01] Sparsity at: 0.0 Epoch 440/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.1114 - val_accuracy: 0.9844 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4667168e-01 -6.1183149e-01 -3.3176506e-01] Sparsity at: 0.0 Epoch 441/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9384e-04 - accuracy: 0.9999 - val_loss: 0.1024 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4285513e-01 -6.1646193e-01 -3.4001493e-01] Sparsity at: 0.0 Epoch 442/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4309e-04 - accuracy: 0.9999 - val_loss: 0.1053 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4833882e-01 -6.1934280e-01 -3.5285234e-01] Sparsity at: 0.0 Epoch 443/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2678e-05 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4881679e-01 -6.1997378e-01 -3.5236308e-01] Sparsity at: 0.0 Epoch 444/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7894e-05 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4862301e-01 -6.2043577e-01 -3.5233986e-01] Sparsity at: 0.0 Epoch 445/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6884e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4912566e-01 -6.2048554e-01 -3.5119575e-01] Sparsity at: 0.0 Epoch 446/500 235/235 [==============================] - 3s 13ms/step - loss: 6.8490e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4937463e-01 -6.2040502e-01 -3.5087770e-01] Sparsity at: 0.0 Epoch 447/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4858e-05 - accuracy: 1.0000 - val_loss: 0.0996 - val_accuracy: 0.9847 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5340468e-01 -6.2293601e-01 -3.5134894e-01] Sparsity at: 0.0 Epoch 448/500 235/235 [==============================] - 3s 13ms/step - loss: 7.8609e-05 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9836 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5069332e-01 -6.2446421e-01 -3.4712365e-01] Sparsity at: 0.0 Epoch 449/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0402e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5074881e-01 -6.2054253e-01 -3.4951410e-01] Sparsity at: 0.0 Epoch 450/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5749e-04 - accuracy: 0.9999 - val_loss: 0.1043 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5251329e-01 -6.1540824e-01 -3.6382011e-01] Sparsity at: 0.0 Epoch 451/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7539e-04 - accuracy: 0.9999 - val_loss: 0.1120 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5435370e-01 -6.0671258e-01 -3.6066976e-01] Sparsity at: 0.0 Epoch 452/500 235/235 [==============================] - 3s 13ms/step - loss: 5.6391e-04 - accuracy: 0.9999 - val_loss: 0.1072 - val_accuracy: 0.9843 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5592023e-01 -6.0904813e-01 -3.6552656e-01] Sparsity at: 0.0 Epoch 453/500 235/235 [==============================] - 3s 13ms/step - loss: 9.3323e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5613189e-01 -6.1462474e-01 -3.6918551e-01] Sparsity at: 0.0 Epoch 454/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2709e-04 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6059026e-01 -6.0792422e-01 -3.7340620e-01] Sparsity at: 0.0 Epoch 455/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0792e-05 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9845 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6127696e-01 -6.0936952e-01 -3.7385431e-01] Sparsity at: 0.0 Epoch 456/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5655e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6228040e-01 -6.0754859e-01 -3.7487000e-01] Sparsity at: 0.0 Epoch 457/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4194e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9851 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6223189e-01 -6.0826296e-01 -3.7493998e-01] Sparsity at: 0.0 Epoch 458/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6868e-04 - accuracy: 0.9999 - val_loss: 0.1174 - val_accuracy: 0.9841 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4726385e-01 -6.0825342e-01 -3.4532258e-01] Sparsity at: 0.0 Epoch 459/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5223e-04 - accuracy: 0.9999 - val_loss: 0.1241 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2740740e-01 -6.1075622e-01 -3.2073340e-01] Sparsity at: 0.0 Epoch 460/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1233e-04 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9839 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.2967494e-01 -6.0117024e-01 -3.2066119e-01] Sparsity at: 0.0 Epoch 461/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3525e-04 - accuracy: 0.9999 - val_loss: 0.1119 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4135548e-01 -5.9574664e-01 -3.2225445e-01] Sparsity at: 0.0 Epoch 462/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1330 - val_accuracy: 0.9814 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6414126e-01 -6.5896976e-01 -3.3058769e-01] Sparsity at: 0.0 Epoch 463/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7661e-04 - accuracy: 0.9998 - val_loss: 0.1216 - val_accuracy: 0.9827 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4377865e-01 -6.6160327e-01 -3.0569476e-01] Sparsity at: 0.0 Epoch 464/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7809e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9842 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4701214e-01 -6.5026665e-01 -3.1252399e-01] Sparsity at: 0.0 Epoch 465/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1783e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4871802e-01 -6.5005457e-01 -3.1413868e-01] Sparsity at: 0.0 Epoch 466/500 235/235 [==============================] - 3s 12ms/step - loss: 1.7099e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9846 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4841762e-01 -6.4922339e-01 -3.1461388e-01] Sparsity at: 0.0 Epoch 467/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1302e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4794185e-01 -6.4062595e-01 -3.1482184e-01] Sparsity at: 0.0 Epoch 468/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4846e-05 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4610692e-01 -6.4239001e-01 -3.1474069e-01] Sparsity at: 0.0 Epoch 469/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0385e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9849 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4585545e-01 -6.4170700e-01 -3.1495425e-01] Sparsity at: 0.0 Epoch 470/500 235/235 [==============================] - 3s 13ms/step - loss: 9.3403e-06 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9850 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4596298e-01 -6.3994187e-01 -3.1587914e-01] Sparsity at: 0.0 Epoch 471/500 235/235 [==============================] - 3s 13ms/step - loss: 7.6517e-06 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4574560e-01 -6.3983369e-01 -3.1573099e-01] Sparsity at: 0.0 Epoch 472/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8014e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4720848e-01 -6.3993770e-01 -3.1616858e-01] Sparsity at: 0.0 Epoch 473/500 235/235 [==============================] - 3s 13ms/step - loss: 6.6018e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4711269e-01 -6.4036345e-01 -3.1714511e-01] Sparsity at: 0.0 Epoch 474/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9154e-06 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4754840e-01 -6.4072776e-01 -3.1738245e-01] Sparsity at: 0.0 Epoch 475/500 235/235 [==============================] - 3s 13ms/step - loss: 5.2430e-06 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9852 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4876243e-01 -6.4267790e-01 -3.1763420e-01] Sparsity at: 0.0 Epoch 476/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3790e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9853 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4699795e-01 -6.4273345e-01 -3.1575289e-01] Sparsity at: 0.0 Epoch 477/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2744e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9854 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4720383e-01 -6.4004707e-01 -3.1602839e-01] Sparsity at: 0.0 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1503 - val_accuracy: 0.9809 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.3536319e-01 -5.8803338e-01 -3.1316033e-01] Sparsity at: 0.0 Epoch 479/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1369 - val_accuracy: 0.9822 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6523989e-01 -6.0393202e-01 -3.2994890e-01] Sparsity at: 0.0 Epoch 480/500 235/235 [==============================] - 3s 13ms/step - loss: 4.8271e-04 - accuracy: 0.9998 - val_loss: 0.1222 - val_accuracy: 0.9835 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6797712e-01 -6.0805738e-01 -3.2744566e-01] Sparsity at: 0.0 Epoch 481/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4052e-04 - accuracy: 0.9999 - val_loss: 0.1238 - val_accuracy: 0.9833 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.6056999e-01 -6.0650009e-01 -3.2275259e-01] Sparsity at: 0.0 Epoch 482/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8407e-04 - accuracy: 0.9999 - val_loss: 0.1256 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5879365e-01 -6.0564893e-01 -3.2200885e-01] Sparsity at: 0.0 Epoch 483/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9926e-04 - accuracy: 0.9999 - val_loss: 0.1266 - val_accuracy: 0.9822 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5497359e-01 -6.0548425e-01 -3.2120198e-01] Sparsity at: 0.0 Epoch 484/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8543e-05 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9821 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5703675e-01 -6.0277194e-01 -3.2363817e-01] Sparsity at: 0.0 Epoch 485/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3913e-05 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5733453e-01 -6.0269451e-01 -3.2379088e-01] Sparsity at: 0.0 Epoch 486/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6388e-04 - accuracy: 0.9999 - val_loss: 0.1261 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5811005e-01 -6.0211420e-01 -3.2391828e-01] Sparsity at: 0.0 Epoch 487/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1107e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4751836e-01 -6.0466290e-01 -3.2242948e-01] Sparsity at: 0.0 Epoch 488/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3391e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9834 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4699318e-01 -6.0472918e-01 -3.2351807e-01] Sparsity at: 0.0 Epoch 489/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2000e-06 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4763680e-01 -6.0612792e-01 -3.2381690e-01] Sparsity at: 0.0 Epoch 490/500 235/235 [==============================] - 3s 13ms/step - loss: 6.8251e-06 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9831 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4891263e-01 -6.0607654e-01 -3.2493290e-01] Sparsity at: 0.0 Epoch 491/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4724e-06 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9829 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5007588e-01 -6.0527229e-01 -3.2631123e-01] Sparsity at: 0.0 Epoch 492/500 235/235 [==============================] - 3s 13ms/step - loss: 7.5828e-06 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5083011e-01 -6.0621345e-01 -3.2674077e-01] Sparsity at: 0.0 Epoch 493/500 235/235 [==============================] - 3s 13ms/step - loss: 7.1515e-06 - accuracy: 1.0000 - val_loss: 0.1250 - val_accuracy: 0.9830 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.4888313e-01 -6.0576189e-01 -3.2200968e-01] Sparsity at: 0.0 Epoch 494/500 235/235 [==============================] - 3s 13ms/step - loss: 8.7922e-06 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9832 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5318819e-01 -6.1297250e-01 -3.2618743e-01] Sparsity at: 0.0 Epoch 495/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1993e-05 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9840 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5668854e-01 -6.0483903e-01 -3.2665747e-01] Sparsity at: 0.0 Epoch 496/500 235/235 [==============================] - 3s 13ms/step - loss: 6.8975e-05 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9827 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.5705552e-01 -5.9470004e-01 -3.2464722e-01] Sparsity at: 0.0 Epoch 497/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 0.1578 - val_accuracy: 0.9793 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7798253e-01 -5.7939446e-01 -3.6559799e-01] Sparsity at: 0.0 Epoch 498/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1281 - val_accuracy: 0.9823 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.9288447e-01 -5.7333845e-01 -3.9446467e-01] Sparsity at: 0.0 Epoch 499/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9743e-04 - accuracy: 0.9998 - val_loss: 0.1325 - val_accuracy: 0.9838 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.8001994e-01 -5.6135154e-01 -3.9496699e-01] Sparsity at: 0.0 Epoch 500/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5556e-04 - accuracy: 0.9999 - val_loss: 0.1317 - val_accuracy: 0.9837 [-5.4509416e-02 1.0096259e-02 -5.4582953e-04 ... 7.7146870e-01 -5.7559741e-01 -3.9863393e-01] Sparsity at: 0.0 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.042017221450805664 Thresholhold -0.06162944808602333 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08907948434352875 Thresholhold -0.10798515379428864 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10679344832897186 Thresholhold -0.06120911240577698 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:17 - loss: 4.5710 - accuracy: 0.1719WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0051s vs `on_train_batch_begin` time: 2.4617s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 1.5551 - accuracy: 0.8563 - val_loss: 0.9205 - val_accuracy: 0.9020 [-1.3205649e-06 -4.8343789e-08 -2.5207021e-09 ... -1.7735681e-01 -4.2541802e-02 5.5344618e-04] Sparsity at: 0.0 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8710 - accuracy: 0.8971 - val_loss: 0.8248 - val_accuracy: 0.9012 [ 5.61612735e-12 2.32025635e-13 -1.00695954e-14 ... -1.62530556e-01 -1.96039286e-02 2.24268693e-03] Sparsity at: 0.0 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8323 - accuracy: 0.8974 - val_loss: 0.8108 - val_accuracy: 0.9007 [-1.2643050e-18 -1.0408753e-18 5.6017149e-20 ... -1.4950198e-01 -1.4024131e-03 -1.9112439e-03] Sparsity at: 0.0 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8233 - accuracy: 0.8974 - val_loss: 0.8057 - val_accuracy: 0.8992 [ 1.1974747e-22 -3.0707448e-24 4.4265993e-26 ... -1.3795950e-01 1.2661943e-02 -5.7758889e-03] Sparsity at: 0.0 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8181 - accuracy: 0.8974 - val_loss: 0.8024 - val_accuracy: 0.8987 [ 2.9976895e-28 4.4539898e-30 -1.0685576e-30 ... -1.2915209e-01 2.4032211e-02 -7.7792057e-03] Sparsity at: 0.0 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8151 - accuracy: 0.8978 - val_loss: 0.7991 - val_accuracy: 0.8992 [ 5.9517933e-34 2.3500391e-34 2.2159964e-32 ... -1.2216675e-01 3.4022849e-02 -8.5950708e-03] Sparsity at: 0.0 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8131 - accuracy: 0.8979 - val_loss: 0.7969 - val_accuracy: 0.8987 [ 2.1660260e-34 2.3500391e-34 -3.6349234e-30 ... -1.1706496e-01 4.2687733e-02 -8.4911967e-03] Sparsity at: 0.0 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8113 - accuracy: 0.8981 - val_loss: 0.7962 - val_accuracy: 0.8994 [ 2.1660260e-34 2.3500391e-34 -1.3978995e-06 ... -1.1328870e-01 5.0311845e-02 -7.3674787e-03] Sparsity at: 0.0 Epoch 9/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8102 - accuracy: 0.8981 - val_loss: 0.7958 - val_accuracy: 0.8985 [ 2.1660260e-34 2.3500391e-34 -4.3361278e-12 ... -1.1087439e-01 5.8021829e-02 -6.4131520e-03] Sparsity at: 0.0 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8091 - accuracy: 0.8987 - val_loss: 0.7951 - val_accuracy: 0.8988 [ 2.1660260e-34 2.3500391e-34 2.6003137e-17 ... -1.0863491e-01 6.4445101e-02 -5.2099591e-03] Sparsity at: 0.0 Epoch 11/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8084 - accuracy: 0.8983 - val_loss: 0.7935 - val_accuracy: 0.8990 [ 2.1660260e-34 2.3500391e-34 -2.3381148e-22 ... -1.0696783e-01 7.0514143e-02 -3.9761793e-03] Sparsity at: 0.0 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8080 - accuracy: 0.8987 - val_loss: 0.7934 - val_accuracy: 0.8996 [ 2.16602604e-34 2.35003912e-34 -5.53556129e-06 ... -1.06254585e-01 7.65981227e-02 -2.72436673e-03] Sparsity at: 0.0 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8075 - accuracy: 0.8987 - val_loss: 0.7917 - val_accuracy: 0.9005 [ 2.1660260e-34 2.3500391e-34 -9.4719413e-11 ... -1.0558700e-01 8.1611246e-02 -1.6018897e-03] Sparsity at: 0.0 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8070 - accuracy: 0.8991 - val_loss: 0.7912 - val_accuracy: 0.9007 [ 2.16602604e-34 2.35003912e-34 5.26896051e-16 ... -1.05248705e-01 8.64722282e-02 -7.24337180e-04] Sparsity at: 0.0 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8067 - accuracy: 0.8993 - val_loss: 0.7918 - val_accuracy: 0.9007 [ 2.16602604e-34 2.35003912e-34 -1.47129431e-09 ... -1.04935884e-01 9.06326100e-02 2.02169584e-04] Sparsity at: 0.0 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8065 - accuracy: 0.8989 - val_loss: 0.7913 - val_accuracy: 0.9007 [ 2.1660260e-34 2.3500391e-34 -2.3916074e-09 ... -1.0455627e-01 9.4427384e-02 1.0654160e-03] Sparsity at: 0.0 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8062 - accuracy: 0.8992 - val_loss: 0.7908 - val_accuracy: 0.9006 [ 2.1660260e-34 2.3500391e-34 1.5097068e-14 ... -1.0460574e-01 9.7965449e-02 1.5696820e-03] Sparsity at: 0.0 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8057 - accuracy: 0.8995 - val_loss: 0.7910 - val_accuracy: 0.9004 [ 2.16602604e-34 2.35003912e-34 3.80127688e-07 ... -1.04720630e-01 1.01084016e-01 2.52033910e-03] Sparsity at: 0.0 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.8994 - val_loss: 0.7901 - val_accuracy: 0.9018 [ 2.1660260e-34 2.3500391e-34 1.6393388e-09 ... -1.0455029e-01 1.0347389e-01 3.0736746e-03] Sparsity at: 0.0 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8996 - val_loss: 0.7902 - val_accuracy: 0.9011 [ 2.1660260e-34 2.3500391e-34 1.3422409e-14 ... -1.0462484e-01 1.0621593e-01 3.5031275e-03] Sparsity at: 0.0 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.8997 - val_loss: 0.7890 - val_accuracy: 0.9024 [ 2.16602604e-34 2.35003912e-34 -1.07750475e-05 ... -1.04435094e-01 1.07824951e-01 4.10581799e-03] Sparsity at: 0.0 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8054 - accuracy: 0.8997 - val_loss: 0.7892 - val_accuracy: 0.9021 [ 2.1660260e-34 2.3500391e-34 5.6322599e-11 ... -1.0444410e-01 1.0976944e-01 4.6280744e-03] Sparsity at: 0.0 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9000 - val_loss: 0.7899 - val_accuracy: 0.9014 [ 2.16602604e-34 2.35003912e-34 -1.98538255e-14 ... -1.04480505e-01 1.11393012e-01 5.04942006e-03] Sparsity at: 0.0 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8999 - val_loss: 0.7898 - val_accuracy: 0.9015 [ 2.1660260e-34 2.3500391e-34 -8.5523766e-10 ... -1.0432904e-01 1.1281988e-01 5.4170885e-03] Sparsity at: 0.0 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9002 - val_loss: 0.7894 - val_accuracy: 0.9021 [ 2.16602604e-34 2.35003912e-34 -3.79743979e-13 ... -1.04096480e-01 1.13918714e-01 5.92254661e-03] Sparsity at: 0.0 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9000 - val_loss: 0.7891 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 1.3922614e-05 ... -1.0404226e-01 1.1462061e-01 6.5571698e-03] Sparsity at: 0.0 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.9002 - val_loss: 0.7894 - val_accuracy: 0.9023 [ 2.1660260e-34 2.3500391e-34 5.8171030e-11 ... -1.0361782e-01 1.1562388e-01 6.1145765e-03] Sparsity at: 0.0 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9000 - val_loss: 0.7893 - val_accuracy: 0.9014 [ 2.16602604e-34 2.35003912e-34 7.79296201e-08 ... -1.03679374e-01 1.16307065e-01 6.63901167e-03] Sparsity at: 0.0 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9003 - val_loss: 0.7890 - val_accuracy: 0.9019 [ 2.1660260e-34 2.3500391e-34 9.4815200e-10 ... -1.0326217e-01 1.1728546e-01 6.8496661e-03] Sparsity at: 0.0 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9005 - val_loss: 0.7891 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 -4.0738880e-13 ... -1.0345131e-01 1.1790537e-01 7.0318724e-03] Sparsity at: 0.0 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9002 - val_loss: 0.7890 - val_accuracy: 0.9026 [ 2.16602604e-34 2.35003912e-34 -4.27853948e-08 ... -1.02870174e-01 1.18426777e-01 6.89500896e-03] Sparsity at: 0.0 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9007 - val_loss: 0.7895 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 -4.1693056e-14 ... -1.0257340e-01 1.1884518e-01 7.1710302e-03] Sparsity at: 0.0 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9007 - val_loss: 0.7881 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.6677791e-07 ... -1.0261066e-01 1.1929432e-01 7.5754649e-03] Sparsity at: 0.0 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9006 - val_loss: 0.7889 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 2.6858646e-12 ... -1.0215940e-01 1.1980158e-01 7.4140839e-03] Sparsity at: 0.0 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7895 - val_accuracy: 0.9023 [ 2.1660260e-34 2.3500391e-34 -4.1741819e-06 ... -1.0198027e-01 1.2032953e-01 7.9710959e-03] Sparsity at: 0.0 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9007 - val_loss: 0.7885 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -1.24806120e-11 ... -1.01966016e-01 1.20785087e-01 8.18054006e-03] Sparsity at: 0.0 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -2.73279293e-05 ... -1.01689473e-01 1.20898284e-01 8.15034006e-03] Sparsity at: 0.0 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 1.38273476e-10 ... -1.01651676e-01 1.21659353e-01 8.42591748e-03] Sparsity at: 0.0 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9006 - val_loss: 0.7891 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 -1.0361981e-04 ... -1.0180908e-01 1.2199034e-01 8.3231432e-03] Sparsity at: 0.0 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -9.4042518e-10 ... -1.0154715e-01 1.2213847e-01 8.4032761e-03] Sparsity at: 0.0 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7884 - val_accuracy: 0.9027 [ 2.16602604e-34 2.35003912e-34 -8.18828738e-09 ... -1.01530753e-01 1.22399464e-01 8.43476132e-03] Sparsity at: 0.0 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9008 - val_loss: 0.7883 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -4.94157959e-09 ... -1.01429515e-01 1.22700371e-01 8.54835752e-03] Sparsity at: 0.0 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -3.76863714e-11 ... -1.00892186e-01 1.22679248e-01 8.47051200e-03] Sparsity at: 0.0 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 5.2716231e-09 ... -1.0106509e-01 1.2278392e-01 8.7984437e-03] Sparsity at: 0.0 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7879 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 1.7454630e-12 ... -1.0067753e-01 1.2328408e-01 8.5900435e-03] Sparsity at: 0.0 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 2.45417606e-08 ... -1.00501135e-01 1.23044893e-01 8.60073604e-03] Sparsity at: 0.0 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7881 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 -4.93951993e-13 ... -1.00759186e-01 1.23120397e-01 8.68034177e-03] Sparsity at: 0.0 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 -9.5595162e-08 ... -1.0045973e-01 1.2335862e-01 8.5055120e-03] Sparsity at: 0.0 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -8.7486192e-13 ... -1.0047456e-01 1.2372674e-01 8.6227581e-03] Sparsity at: 0.0 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7883 - val_accuracy: 0.9040 [ 2.16602604e-34 2.35003912e-34 2.84388648e-07 ... -1.00322999e-01 1.23401135e-01 8.64373054e-03] Sparsity at: 0.0 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.00915514860354194 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.036097921937487065 Thresholhold -0.06856929510831833 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.11574094451669836 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 46s 7ms/step - loss: 0.8040 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 1.6453102e-12 ... -1.0000724e-01 1.2363015e-01 8.6111184e-03] Sparsity at: 0.0 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7886 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -7.1637118e-07 ... -1.0027568e-01 1.2385195e-01 8.5295215e-03] Sparsity at: 0.0 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 7.3845825e-12 ... -9.9964030e-02 1.2353751e-01 8.2884375e-03] Sparsity at: 0.0 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9021 [ 2.1660260e-34 2.3500391e-34 8.8664683e-07 ... -9.9746153e-02 1.2373705e-01 8.2060015e-03] Sparsity at: 0.0 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9008 - val_loss: 0.7883 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 -2.80812179e-11 ... -9.94590595e-02 1.23957165e-01 8.07343237e-03] Sparsity at: 0.0 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9028 [ 2.16602604e-34 2.35003912e-34 -4.34666617e-06 ... -9.95887965e-02 1.23954244e-01 8.13579746e-03] Sparsity at: 0.0 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 1.1211625e-10 ... -9.9212125e-02 1.2372702e-01 7.6875449e-03] Sparsity at: 0.0 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9044 [ 2.1660260e-34 2.3500391e-34 -1.0305887e-06 ... -9.9204823e-02 1.2393509e-01 7.5690080e-03] Sparsity at: 0.0 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 1.19795884e-09 ... -9.92273390e-02 1.23632945e-01 7.61712156e-03] Sparsity at: 0.0 Epoch 60/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -4.6252936e-12 ... -9.8711058e-02 1.2364761e-01 7.5401450e-03] Sparsity at: 0.0 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 1.8374916e-08 ... -9.8907493e-02 1.2350740e-01 7.3776031e-03] Sparsity at: 0.0 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 3.0171525e-13 ... -9.8878734e-02 1.2382139e-01 7.5573400e-03] Sparsity at: 0.0 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7888 - val_accuracy: 0.9021 [ 2.16602604e-34 2.35003912e-34 -1.74478032e-07 ... -9.86311659e-02 1.23427935e-01 7.68833980e-03] Sparsity at: 0.0 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 4.9983780e-13 ... -9.8617427e-02 1.2338685e-01 7.3522748e-03] Sparsity at: 0.0 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -6.6352311e-07 ... -9.8587722e-02 1.2323004e-01 7.2905472e-03] Sparsity at: 0.0 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7889 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 6.0581462e-12 ... -9.8664761e-02 1.2345547e-01 7.4921534e-03] Sparsity at: 0.0 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9009 - val_loss: 0.7889 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -4.9139799e-06 ... -9.8300889e-02 1.2318038e-01 7.1581635e-03] Sparsity at: 0.0 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9005 - val_loss: 0.7882 - val_accuracy: 0.9042 [ 2.1660260e-34 2.3500391e-34 -7.4312882e-11 ... -9.7884536e-02 1.2294847e-01 7.0410557e-03] Sparsity at: 0.0 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 7.9210302e-05 ... -9.8067157e-02 1.2327841e-01 6.9400631e-03] Sparsity at: 0.0 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 6.9163408e-10 ... -9.7821139e-02 1.2315126e-01 6.6557755e-03] Sparsity at: 0.0 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 5.3430538e-10 ... -9.7845003e-02 1.2295124e-01 7.1684401e-03] Sparsity at: 0.0 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -7.3171340e-09 ... -9.8003305e-02 1.2300588e-01 7.2030090e-03] Sparsity at: 0.0 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -6.5043684e-13 ... -9.8043591e-02 1.2297179e-01 6.9011776e-03] Sparsity at: 0.0 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -3.72407172e-08 ... -9.79931429e-02 1.22921996e-01 7.26905884e-03] Sparsity at: 0.0 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -4.5371825e-14 ... -9.7844921e-02 1.2295132e-01 7.2539728e-03] Sparsity at: 0.0 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 2.5068209e-07 ... -9.7756229e-02 1.2298052e-01 7.0465095e-03] Sparsity at: 0.0 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7886 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -1.2570969e-12 ... -9.8069221e-02 1.2293230e-01 6.9023226e-03] Sparsity at: 0.0 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 -6.5326759e-07 ... -9.7691648e-02 1.2275473e-01 7.0728757e-03] Sparsity at: 0.0 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7882 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.5054334e-12 ... -9.7587541e-02 1.2255965e-01 6.5375259e-03] Sparsity at: 0.0 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 6.6796798e-05 ... -9.7386755e-02 1.2223854e-01 6.8525658e-03] Sparsity at: 0.0 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 -2.5403077e-10 ... -9.7440563e-02 1.2239065e-01 6.8055037e-03] Sparsity at: 0.0 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7873 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -6.22334895e-09 ... -9.71255153e-02 1.21845916e-01 6.54944032e-03] Sparsity at: 0.0 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7892 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 5.0010618e-09 ... -9.7704850e-02 1.2256291e-01 6.4622951e-03] Sparsity at: 0.0 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -1.1566091e-12 ... -9.7668409e-02 1.2254113e-01 6.2182229e-03] Sparsity at: 0.0 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 4.11459240e-08 ... -9.71793234e-02 1.22211255e-01 6.36436744e-03] Sparsity at: 0.0 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7892 - val_accuracy: 0.9023 [ 2.1660260e-34 2.3500391e-34 -2.3269505e-13 ... -9.7313061e-02 1.2256114e-01 6.2853787e-03] Sparsity at: 0.0 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7888 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -9.8411135e-08 ... -9.7215168e-02 1.2226288e-01 6.3559138e-03] Sparsity at: 0.0 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -1.4530465e-12 ... -9.7305700e-02 1.2194739e-01 6.3968627e-03] Sparsity at: 0.0 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7876 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -1.8861141e-07 ... -9.7074747e-02 1.2176514e-01 6.0217646e-03] Sparsity at: 0.0 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -2.5579309e-12 ... -9.7093269e-02 1.2192043e-01 6.1159977e-03] Sparsity at: 0.0 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 1.0252292e-05 ... -9.7039454e-02 1.2164108e-01 5.8161723e-03] Sparsity at: 0.0 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 4.7190005e-11 ... -9.7213544e-02 1.2182612e-01 5.9348890e-03] Sparsity at: 0.0 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 1.4972247e-04 ... -9.7126305e-02 1.2150061e-01 5.8374153e-03] Sparsity at: 0.0 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7879 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -1.2245449e-10 ... -9.7019352e-02 1.2190378e-01 5.5013690e-03] Sparsity at: 0.0 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7886 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 6.4689498e-10 ... -9.7127266e-02 1.2179118e-01 5.7266690e-03] Sparsity at: 0.0 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 4.52062254e-09 ... -9.68534946e-02 1.21476084e-01 5.42884693e-03] Sparsity at: 0.0 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -3.0263845e-13 ... -9.7083762e-02 1.2131371e-01 5.4557747e-03] Sparsity at: 0.0 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -6.0808212e-08 ... -9.7220466e-02 1.2147357e-01 5.5546863e-03] Sparsity at: 0.0 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 4.01593147e-13 ... -9.68614668e-02 1.21158734e-01 5.36596077e-03] Sparsity at: 0.0 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7884 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 4.9359312e-07 ... -9.7060896e-02 1.2133976e-01 5.1674028e-03] Sparsity at: 0.0 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.014708364099192517 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.04548157419801857 Thresholhold -0.0645592212677002 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.1286502590551155 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 53s 7ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9027 [ 2.16602604e-34 2.35003912e-34 2.79422731e-12 ... -9.71402302e-02 1.20879635e-01 4.90643457e-03] Sparsity at: 0.0 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9025 [ 2.16602604e-34 2.35003912e-34 -3.03700449e-06 ... -9.72235948e-02 1.21013544e-01 4.88534849e-03] Sparsity at: 0.0 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -6.9283364e-12 ... -9.7325288e-02 1.2118961e-01 4.4934880e-03] Sparsity at: 0.0 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -1.5197233e-05 ... -9.7079918e-02 1.2105858e-01 4.5556165e-03] Sparsity at: 0.0 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 7.3672374e-11 ... -9.7418077e-02 1.2105993e-01 4.7238539e-03] Sparsity at: 0.0 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 3.1915839e-05 ... -9.7155385e-02 1.2078542e-01 4.4937059e-03] Sparsity at: 0.0 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 2.8996230e-10 ... -9.7243927e-02 1.2086421e-01 4.6679997e-03] Sparsity at: 0.0 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -1.8639948e-05 ... -9.7475395e-02 1.2105953e-01 4.2736516e-03] Sparsity at: 0.0 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -6.5653027e-10 ... -9.7421840e-02 1.2049760e-01 4.0403479e-03] Sparsity at: 0.0 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7870 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.6173111e-11 ... -9.7342648e-02 1.2074792e-01 4.0978370e-03] Sparsity at: 0.0 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -1.1586430e-08 ... -9.6977845e-02 1.2075350e-01 3.3640112e-03] Sparsity at: 0.0 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -2.2562814e-13 ... -9.7149476e-02 1.2063864e-01 3.4207613e-03] Sparsity at: 0.0 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -8.4847329e-08 ... -9.7358614e-02 1.2024760e-01 3.9689345e-03] Sparsity at: 0.0 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -1.1080773e-12 ... -9.7101718e-02 1.2062112e-01 3.8814293e-03] Sparsity at: 0.0 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7883 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 8.4616335e-07 ... -9.7484112e-02 1.2063508e-01 3.9754892e-03] Sparsity at: 0.0 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7893 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 4.7346424e-12 ... -9.7356521e-02 1.2043872e-01 3.8454600e-03] Sparsity at: 0.0 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7887 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 1.0483108e-05 ... -9.7376145e-02 1.2058238e-01 3.5123341e-03] Sparsity at: 0.0 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7885 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -4.0463074e-11 ... -9.7385406e-02 1.2084827e-01 3.2860136e-03] Sparsity at: 0.0 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 2.1756416e-05 ... -9.7490899e-02 1.2088891e-01 3.6296572e-03] Sparsity at: 0.0 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7885 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 1.3137491e-09 ... -9.7259887e-02 1.2064642e-01 3.4481718e-03] Sparsity at: 0.0 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -4.7470278e-11 ... -9.7076550e-02 1.2051379e-01 3.3611022e-03] Sparsity at: 0.0 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 1.4670893e-08 ... -9.6782044e-02 1.2027787e-01 2.8280038e-03] Sparsity at: 0.0 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9028 [ 2.16602604e-34 2.35003912e-34 1.51341396e-13 ... -9.70256329e-02 1.20238714e-01 3.01626162e-03] Sparsity at: 0.0 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -8.9415053e-08 ... -9.7184233e-02 1.2040429e-01 3.0033137e-03] Sparsity at: 0.0 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -8.32622816e-14 ... -9.73508954e-02 1.20344274e-01 3.12919798e-03] Sparsity at: 0.0 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 6.4694416e-07 ... -9.7192332e-02 1.2018148e-01 2.7840296e-03] Sparsity at: 0.0 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -7.3657261e-12 ... -9.7190410e-02 1.1988987e-01 2.7156852e-03] Sparsity at: 0.0 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 9.98311589e-06 ... -9.68742520e-02 1.19972415e-01 2.79180845e-03] Sparsity at: 0.0 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -9.30401589e-13 ... -9.67142284e-02 1.19725816e-01 2.98653310e-03] Sparsity at: 0.0 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 1.5978871e-06 ... -9.6988514e-02 1.1991366e-01 3.0524095e-03] Sparsity at: 0.0 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 6.7893874e-10 ... -9.7167835e-02 1.1985377e-01 3.0049200e-03] Sparsity at: 0.0 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 1.0064081e-12 ... -9.6865274e-02 1.1976625e-01 2.8798364e-03] Sparsity at: 0.0 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9024 [ 2.16602604e-34 2.35003912e-34 -3.97779232e-09 ... -9.69731957e-02 1.19776495e-01 2.80328165e-03] Sparsity at: 0.0 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7886 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 2.7068240e-13 ... -9.7379789e-02 1.2036324e-01 2.9289066e-03] Sparsity at: 0.0 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -4.8864769e-07 ... -9.6915834e-02 1.1986538e-01 2.8026041e-03] Sparsity at: 0.0 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7880 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 2.9037278e-12 ... -9.6954107e-02 1.1982196e-01 2.8092824e-03] Sparsity at: 0.0 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -1.2274721e-05 ... -9.6897244e-02 1.1952463e-01 2.9610861e-03] Sparsity at: 0.0 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 8.25594523e-11 ... -9.71164778e-02 1.19513415e-01 2.96823401e-03] Sparsity at: 0.0 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7887 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 5.5957571e-06 ... -9.7000144e-02 1.1974403e-01 2.4567340e-03] Sparsity at: 0.0 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7883 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -8.6247898e-10 ... -9.6962549e-02 1.1942009e-01 2.7084923e-03] Sparsity at: 0.0 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -3.03921888e-11 ... -9.67779681e-02 1.19541444e-01 2.69276882e-03] Sparsity at: 0.0 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -9.2552490e-09 ... -9.6977256e-02 1.1937625e-01 2.7654774e-03] Sparsity at: 0.0 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 -2.0934207e-13 ... -9.6662499e-02 1.1892152e-01 2.8035061e-03] Sparsity at: 0.0 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -1.8825972e-09 ... -9.6725717e-02 1.1925315e-01 2.9784557e-03] Sparsity at: 0.0 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 -6.2213906e-13 ... -9.6685015e-02 1.1931898e-01 2.7437985e-03] Sparsity at: 0.0 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -8.6099720e-07 ... -9.6654192e-02 1.1942066e-01 2.5409604e-03] Sparsity at: 0.0 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -4.9153685e-12 ... -9.6880190e-02 1.1949421e-01 2.7386195e-03] Sparsity at: 0.0 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7890 - val_accuracy: 0.9020 [ 2.1660260e-34 2.3500391e-34 -3.8123526e-06 ... -9.6700191e-02 1.1912562e-01 2.4994416e-03] Sparsity at: 0.0 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7876 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 -4.09335621e-11 ... -9.65089798e-02 1.19078375e-01 2.55264482e-03] Sparsity at: 0.0 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9026 [ 2.16602604e-34 2.35003912e-34 -5.23788913e-05 ... -9.64474380e-02 1.19286716e-01 2.45680753e-03] Sparsity at: 0.0 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.020803199581568288 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.05591662801998787 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.1408818395884932 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8033 - accuracy: 0.9018 - val_loss: 0.7881 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 3.6619929e-11 ... -9.6580260e-02 1.1910656e-01 2.6568747e-03] Sparsity at: 0.0 Epoch 152/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -2.2391673e-06 ... -9.6932195e-02 1.1919407e-01 2.7150910e-03] Sparsity at: 0.0 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -1.7990511e-09 ... -9.6603841e-02 1.1926375e-01 2.8114533e-03] Sparsity at: 0.0 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9009 - val_loss: 0.7876 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -3.6222533e-10 ... -9.6340299e-02 1.1917290e-01 2.8354160e-03] Sparsity at: 0.0 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -5.8077898e-09 ... -9.6539445e-02 1.1905880e-01 2.8775437e-03] Sparsity at: 0.0 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -2.6255072e-12 ... -9.6509233e-02 1.1883052e-01 2.7396532e-03] Sparsity at: 0.0 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7885 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 3.4824858e-08 ... -9.6605420e-02 1.1912230e-01 2.9710745e-03] Sparsity at: 0.0 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -5.6038683e-16 ... -9.6622750e-02 1.1941165e-01 2.8413485e-03] Sparsity at: 0.0 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7890 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 1.40526708e-07 ... -9.66509134e-02 1.19145416e-01 2.59761140e-03] Sparsity at: 0.0 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7870 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -1.0402730e-12 ... -9.6590072e-02 1.1878300e-01 2.6390764e-03] Sparsity at: 0.0 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -9.4023585e-07 ... -9.6608140e-02 1.1920593e-01 2.7647687e-03] Sparsity at: 0.0 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 6.7455584e-13 ... -9.6854463e-02 1.1936492e-01 2.7359154e-03] Sparsity at: 0.0 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 2.7540141e-06 ... -9.6537173e-02 1.1872142e-01 2.6772402e-03] Sparsity at: 0.0 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7888 - val_accuracy: 0.9025 [ 2.1660260e-34 2.3500391e-34 -1.3675547e-11 ... -9.6797854e-02 1.1905740e-01 2.6128897e-03] Sparsity at: 0.0 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 -1.3585609e-05 ... -9.6634559e-02 1.1906743e-01 2.8046893e-03] Sparsity at: 0.0 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -3.5801785e-11 ... -9.6938528e-02 1.1915258e-01 2.7632308e-03] Sparsity at: 0.0 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 1.68348910e-04 ... -9.66620147e-02 1.18900634e-01 2.85071414e-03] Sparsity at: 0.0 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.3919457e-10 ... -9.6317515e-02 1.1889163e-01 2.7481902e-03] Sparsity at: 0.0 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 -3.14312576e-09 ... -9.65864137e-02 1.19008616e-01 2.51066568e-03] Sparsity at: 0.0 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -1.47188794e-09 ... -9.65026543e-02 1.18838936e-01 2.48828111e-03] Sparsity at: 0.0 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 7.3569709e-12 ... -9.6462451e-02 1.1888755e-01 2.3645042e-03] Sparsity at: 0.0 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7884 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 2.55236881e-08 ... -9.65768918e-02 1.19034834e-01 2.88954074e-03] Sparsity at: 0.0 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 6.3266059e-13 ... -9.6415073e-02 1.1883083e-01 2.8056968e-03] Sparsity at: 0.0 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -3.1154045e-08 ... -9.6525386e-02 1.1915801e-01 3.1197423e-03] Sparsity at: 0.0 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 1.5149950e-14 ... -9.6221969e-02 1.1855579e-01 2.6419265e-03] Sparsity at: 0.0 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -1.31637080e-07 ... -9.64419469e-02 1.18899465e-01 2.68231006e-03] Sparsity at: 0.0 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7887 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 1.60267112e-12 ... -9.63397697e-02 1.18625335e-01 2.96521722e-03] Sparsity at: 0.0 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 3.5516737e-07 ... -9.6298061e-02 1.1903628e-01 2.8488128e-03] Sparsity at: 0.0 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -2.6913020e-13 ... -9.6474864e-02 1.1915302e-01 2.7468621e-03] Sparsity at: 0.0 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7887 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 -1.9917941e-07 ... -9.6267074e-02 1.1880699e-01 2.6461885e-03] Sparsity at: 0.0 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 3.7669746e-12 ... -9.6356757e-02 1.1881157e-01 3.0019286e-03] Sparsity at: 0.0 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7880 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -5.9884201e-07 ... -9.6398540e-02 1.1877456e-01 2.7909693e-03] Sparsity at: 0.0 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -2.1847019e-12 ... -9.6387528e-02 1.1889373e-01 2.9383923e-03] Sparsity at: 0.0 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 5.3655244e-06 ... -9.6158706e-02 1.1894097e-01 3.0706285e-03] Sparsity at: 0.0 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 3.44929710e-11 ... -9.63250399e-02 1.19054615e-01 3.05892900e-03] Sparsity at: 0.0 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 5.0792511e-05 ... -9.6353278e-02 1.1879031e-01 2.9770581e-03] Sparsity at: 0.0 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -2.3545893e-10 ... -9.6241102e-02 1.1863084e-01 2.8560949e-03] Sparsity at: 0.0 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -1.1394823e-05 ... -9.6046142e-02 1.1877998e-01 2.7560133e-03] Sparsity at: 0.0 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 1.72704517e-10 ... -9.62879434e-02 1.18974574e-01 3.02266539e-03] Sparsity at: 0.0 Epoch 190/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -5.4520000e-11 ... -9.6491553e-02 1.1929038e-01 3.3337504e-03] Sparsity at: 0.0 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9039 [ 2.16602604e-34 2.35003912e-34 -3.63323238e-09 ... -9.64286700e-02 1.19018145e-01 3.30658117e-03] Sparsity at: 0.0 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -6.4167814e-14 ... -9.6307836e-02 1.1880210e-01 3.0820197e-03] Sparsity at: 0.0 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 4.7288302e-07 ... -9.6134439e-02 1.1928761e-01 3.0975449e-03] Sparsity at: 0.0 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 -2.84533681e-12 ... -9.60668996e-02 1.18882336e-01 2.92986888e-03] Sparsity at: 0.0 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 8.4072490e-06 ... -9.6113347e-02 1.1886991e-01 3.0404455e-03] Sparsity at: 0.0 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -2.6811109e-10 ... -9.6393518e-02 1.1909443e-01 3.1580201e-03] Sparsity at: 0.0 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9027 [ 2.16602604e-34 2.35003912e-34 -6.78626183e-11 ... -9.60306600e-02 1.18720554e-01 3.00747599e-03] Sparsity at: 0.0 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7883 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 4.8426294e-09 ... -9.6100725e-02 1.1879939e-01 3.0489608e-03] Sparsity at: 0.0 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9020 - val_loss: 0.7881 - val_accuracy: 0.9024 [ 2.16602604e-34 2.35003912e-34 1.41832320e-13 ... -9.61509645e-02 1.18734434e-01 3.43717332e-03] Sparsity at: 0.0 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7872 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 -1.43744515e-07 ... -9.62034985e-02 1.18793294e-01 3.19583109e-03] Sparsity at: 0.0 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.027556433328772556 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.0659883460651347 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.15769569784143656 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 50s 7ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 9.3493854e-13 ... -9.6538365e-02 1.1886690e-01 2.9982189e-03] Sparsity at: 0.0 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -1.2972907e-07 ... -9.6250750e-02 1.1906825e-01 3.1209202e-03] Sparsity at: 0.0 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 3.6280094e-13 ... -9.6179582e-02 1.1904890e-01 3.4341931e-03] Sparsity at: 0.0 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7891 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -1.4574807e-06 ... -9.6239060e-02 1.1927409e-01 3.3770159e-03] Sparsity at: 0.0 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 6.5037600e-11 ... -9.6329443e-02 1.1863651e-01 2.8768338e-03] Sparsity at: 0.0 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 9.1593480e-05 ... -9.6054897e-02 1.1879218e-01 3.0702408e-03] Sparsity at: 0.0 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7882 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -7.6268769e-11 ... -9.6451513e-02 1.1897182e-01 2.9313217e-03] Sparsity at: 0.0 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7881 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 8.5456859e-12 ... -9.6411280e-02 1.1885888e-01 2.9694489e-03] Sparsity at: 0.0 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 8.8605248e-09 ... -9.6175134e-02 1.1901124e-01 3.0101412e-03] Sparsity at: 0.0 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -1.2308070e-13 ... -9.6559554e-02 1.1892750e-01 3.0880678e-03] Sparsity at: 0.0 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7882 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 -4.33963578e-07 ... -9.63640511e-02 1.19063586e-01 3.06394137e-03] Sparsity at: 0.0 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -5.1551108e-13 ... -9.6371450e-02 1.1898365e-01 3.1051950e-03] Sparsity at: 0.0 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7886 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -7.6709221e-06 ... -9.6671633e-02 1.1911969e-01 3.1551465e-03] Sparsity at: 0.0 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7886 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -4.40653555e-11 ... -9.65431333e-02 1.19192265e-01 3.22962878e-03] Sparsity at: 0.0 Epoch 215/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 6.0191516e-05 ... -9.6514642e-02 1.1932000e-01 3.3573692e-03] Sparsity at: 0.0 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7868 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -3.7454451e-10 ... -9.6780226e-02 1.1917350e-01 3.2635916e-03] Sparsity at: 0.0 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7874 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -3.7221553e-09 ... -9.6471243e-02 1.1910212e-01 3.0926869e-03] Sparsity at: 0.0 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 5.3804827e-09 ... -9.6443877e-02 1.1925979e-01 3.3886791e-03] Sparsity at: 0.0 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -1.9796494e-13 ... -9.6502274e-02 1.1885010e-01 3.2876329e-03] Sparsity at: 0.0 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 6.24459773e-08 ... -9.65651870e-02 1.18894674e-01 2.98376079e-03] Sparsity at: 0.0 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -4.1764853e-14 ... -9.6426047e-02 1.1891383e-01 3.2049033e-03] Sparsity at: 0.0 Epoch 222/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7880 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -7.8621895e-07 ... -9.6229345e-02 1.1911226e-01 3.2866602e-03] Sparsity at: 0.0 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9045 [ 2.1660260e-34 2.3500391e-34 4.8542637e-12 ... -9.6650369e-02 1.1909971e-01 3.2494243e-03] Sparsity at: 0.0 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7884 - val_accuracy: 0.9039 [ 2.16602604e-34 2.35003912e-34 4.30070440e-06 ... -9.64922979e-02 1.18824914e-01 3.60909826e-03] Sparsity at: 0.0 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 9.1109290e-12 ... -9.6560977e-02 1.1921288e-01 3.5429737e-03] Sparsity at: 0.0 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7869 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 1.13392452e-05 ... -9.63642821e-02 1.19124055e-01 3.35759437e-03] Sparsity at: 0.0 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7873 - val_accuracy: 0.9041 [ 2.16602604e-34 2.35003912e-34 -5.88086108e-11 ... -9.66008604e-02 1.19056754e-01 3.48671270e-03] Sparsity at: 0.0 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7882 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 -6.7843575e-05 ... -9.6594557e-02 1.1922316e-01 3.7597457e-03] Sparsity at: 0.0 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 8.5243326e-11 ... -9.6335448e-02 1.1927572e-01 3.2777374e-03] Sparsity at: 0.0 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -5.5014636e-05 ... -9.6648894e-02 1.1897836e-01 3.4741948e-03] Sparsity at: 0.0 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7882 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 -2.1246993e-10 ... -9.6568026e-02 1.1910061e-01 3.3937739e-03] Sparsity at: 0.0 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7867 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 2.4068009e-07 ... -9.6539691e-02 1.1903459e-01 3.2571326e-03] Sparsity at: 0.0 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -2.1436353e-09 ... -9.6227027e-02 1.1937067e-01 3.3909483e-03] Sparsity at: 0.0 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9038 [ 2.16602604e-34 2.35003912e-34 7.05914616e-09 ... -9.65277478e-02 1.18935116e-01 3.35481972e-03] Sparsity at: 0.0 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9021 [ 2.1660260e-34 2.3500391e-34 -4.4412976e-09 ... -9.6783206e-02 1.1925901e-01 3.4774737e-03] Sparsity at: 0.0 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7871 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -5.7329497e-10 ... -9.6331649e-02 1.1878636e-01 3.4949395e-03] Sparsity at: 0.0 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7867 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 8.7771461e-09 ... -9.6157141e-02 1.1880552e-01 3.2888025e-03] Sparsity at: 0.0 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -4.9343854e-12 ... -9.6540190e-02 1.1913497e-01 3.3184981e-03] Sparsity at: 0.0 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7880 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -2.1100488e-08 ... -9.6483000e-02 1.1908186e-01 3.0491564e-03] Sparsity at: 0.0 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7882 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 3.1995653e-14 ... -9.6581399e-02 1.1922559e-01 3.4348133e-03] Sparsity at: 0.0 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7871 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 3.0316048e-07 ... -9.6461095e-02 1.1907679e-01 3.3511606e-03] Sparsity at: 0.0 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7884 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -1.9065847e-12 ... -9.6547812e-02 1.1914340e-01 3.6021469e-03] Sparsity at: 0.0 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.7671662e-06 ... -9.6647032e-02 1.1890531e-01 3.8230347e-03] Sparsity at: 0.0 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -1.1744130e-11 ... -9.6731819e-02 1.1914517e-01 3.7350510e-03] Sparsity at: 0.0 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9018 - val_loss: 0.7884 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 4.2391039e-06 ... -9.6824832e-02 1.1919289e-01 3.8685901e-03] Sparsity at: 0.0 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 4.0742507e-11 ... -9.6519634e-02 1.1903399e-01 3.5170321e-03] Sparsity at: 0.0 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9012 - val_loss: 0.7886 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 1.5634807e-05 ... -9.6735716e-02 1.1924642e-01 3.5634537e-03] Sparsity at: 0.0 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7870 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -1.7084140e-11 ... -9.6776724e-02 1.1923430e-01 3.8675345e-03] Sparsity at: 0.0 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -2.2168499e-07 ... -9.6635878e-02 1.1918229e-01 3.5754694e-03] Sparsity at: 0.0 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9015 - val_loss: 0.7867 - val_accuracy: 0.9039 [ 2.16602604e-34 2.35003912e-34 -8.55577165e-10 ... -9.67387557e-02 1.19049884e-01 3.63536761e-03] Sparsity at: 0.0 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.035314610557317216 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.0793650176673859 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.17453197713261837 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -4.3937979e-13 ... -9.6344724e-02 1.1902300e-01 3.6545179e-03] Sparsity at: 0.0 Epoch 252/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -2.0693072e-08 ... -9.6620277e-02 1.1909591e-01 3.7679132e-03] Sparsity at: 0.0 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7880 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 -1.59345112e-13 ... -9.63765383e-02 1.19106606e-01 3.73522611e-03] Sparsity at: 0.0 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9017 - val_loss: 0.7873 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -3.1098520e-07 ... -9.6692465e-02 1.1925478e-01 3.7910873e-03] Sparsity at: 0.0 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9037 [ 2.16602604e-34 2.35003912e-34 -2.75451493e-12 ... -9.65031013e-02 1.19260825e-01 4.02578712e-03] Sparsity at: 0.0 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -1.1235820e-06 ... -9.6647441e-02 1.1908788e-01 3.7425335e-03] Sparsity at: 0.0 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 -1.8085713e-11 ... -9.6525319e-02 1.1919469e-01 3.8959847e-03] Sparsity at: 0.0 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7878 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -3.45957851e-05 ... -9.68064517e-02 1.19119704e-01 4.28887922e-03] Sparsity at: 0.0 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 2.0061147e-10 ... -9.6539557e-02 1.1902673e-01 3.5956630e-03] Sparsity at: 0.0 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 2.4618037e-05 ... -9.6303917e-02 1.1909779e-01 3.7636594e-03] Sparsity at: 0.0 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -9.1779034e-10 ... -9.6516147e-02 1.1901443e-01 3.6777228e-03] Sparsity at: 0.0 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -8.82078321e-09 ... -9.66440216e-02 1.19224764e-01 3.80826066e-03] Sparsity at: 0.0 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7867 - val_accuracy: 0.9048 [ 2.1660260e-34 2.3500391e-34 -3.0509590e-09 ... -9.6331522e-02 1.1912048e-01 3.7990536e-03] Sparsity at: 0.0 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 1.6957579e-11 ... -9.6750781e-02 1.1934923e-01 3.6006470e-03] Sparsity at: 0.0 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 2.90179258e-09 ... -9.67331752e-02 1.19020246e-01 3.88651621e-03] Sparsity at: 0.0 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 2.0668889e-13 ... -9.6745804e-02 1.1914514e-01 3.7794630e-03] Sparsity at: 0.0 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9038 [ 2.16602604e-34 2.35003912e-34 1.81970847e-08 ... -9.68090072e-02 1.19248435e-01 4.04160097e-03] Sparsity at: 0.0 Epoch 268/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7874 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -3.3846271e-14 ... -9.6479014e-02 1.1925084e-01 4.0319730e-03] Sparsity at: 0.0 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 5.6178385e-07 ... -9.6509315e-02 1.1925300e-01 4.0081032e-03] Sparsity at: 0.0 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9012 - val_loss: 0.7879 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 8.1456474e-12 ... -9.6462391e-02 1.1911639e-01 3.9660623e-03] Sparsity at: 0.0 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 -7.21448077e-06 ... -9.66551751e-02 1.19135655e-01 4.02177451e-03] Sparsity at: 0.0 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7869 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -4.1428083e-11 ... -9.6609071e-02 1.1917197e-01 4.0219193e-03] Sparsity at: 0.0 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7872 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -3.4298679e-05 ... -9.6777789e-02 1.1939617e-01 4.2797136e-03] Sparsity at: 0.0 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 2.4402824e-10 ... -9.6785866e-02 1.1927629e-01 4.2709447e-03] Sparsity at: 0.0 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 -1.01073645e-04 ... -9.66128930e-02 1.19029298e-01 4.03866824e-03] Sparsity at: 0.0 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9040 [ 2.16602604e-34 2.35003912e-34 1.06242404e-09 ... -9.69587564e-02 1.19063824e-01 4.32316307e-03] Sparsity at: 0.0 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9026 [ 2.16602604e-34 2.35003912e-34 -1.05213324e-07 ... -9.67454612e-02 1.19146951e-01 4.19961335e-03] Sparsity at: 0.0 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 1.81060367e-09 ... -9.67132971e-02 1.19107194e-01 4.42450866e-03] Sparsity at: 0.0 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7889 - val_accuracy: 0.9025 [ 2.1660260e-34 2.3500391e-34 -2.0965583e-10 ... -9.6451685e-02 1.1926340e-01 4.4435658e-03] Sparsity at: 0.0 Epoch 280/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9044 [ 2.1660260e-34 2.3500391e-34 -7.7143074e-09 ... -9.6578516e-02 1.1942077e-01 4.3690898e-03] Sparsity at: 0.0 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -7.2700353e-13 ... -9.6828096e-02 1.1902361e-01 4.2440724e-03] Sparsity at: 0.0 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -2.64753055e-08 ... -9.67255011e-02 1.19246304e-01 4.21977649e-03] Sparsity at: 0.0 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 4.7735551e-13 ... -9.6346095e-02 1.1930844e-01 4.1203029e-03] Sparsity at: 0.0 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 1.9558996e-07 ... -9.6711539e-02 1.1906434e-01 4.4111614e-03] Sparsity at: 0.0 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -4.0981736e-13 ... -9.6676365e-02 1.1907287e-01 4.5553432e-03] Sparsity at: 0.0 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9025 [ 2.1660260e-34 2.3500391e-34 -1.9513877e-06 ... -9.6382804e-02 1.1925644e-01 4.1261520e-03] Sparsity at: 0.0 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 7.99192656e-12 ... -9.65142846e-02 1.18844025e-01 4.43081558e-03] Sparsity at: 0.0 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -5.4785551e-06 ... -9.6287690e-02 1.1934008e-01 4.3406389e-03] Sparsity at: 0.0 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7874 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 3.8219351e-11 ... -9.6472383e-02 1.1912185e-01 4.6075368e-03] Sparsity at: 0.0 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9045 [ 2.1660260e-34 2.3500391e-34 7.7534685e-05 ... -9.6596919e-02 1.1946670e-01 4.3263799e-03] Sparsity at: 0.0 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 4.6739579e-10 ... -9.6622415e-02 1.1919808e-01 4.2999303e-03] Sparsity at: 0.0 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 1.32014788e-10 ... -9.70803350e-02 1.19679615e-01 4.47730999e-03] Sparsity at: 0.0 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -1.0928803e-08 ... -9.6585058e-02 1.1913126e-01 4.4846023e-03] Sparsity at: 0.0 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 8.21644185e-14 ... -9.65961143e-02 1.19230196e-01 4.52770572e-03] Sparsity at: 0.0 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 1.9438795e-07 ... -9.6789561e-02 1.1920497e-01 4.2616460e-03] Sparsity at: 0.0 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 -8.17650255e-13 ... -9.68965217e-02 1.19466454e-01 4.70501557e-03] Sparsity at: 0.0 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -6.9717753e-06 ... -9.6400127e-02 1.1943810e-01 4.3673129e-03] Sparsity at: 0.0 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7867 - val_accuracy: 0.9038 [ 2.16602604e-34 2.35003912e-34 1.84110244e-11 ... -9.63126719e-02 1.19198784e-01 4.33429051e-03] Sparsity at: 0.0 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.8145724e-05 ... -9.6655823e-02 1.1944599e-01 4.4929753e-03] Sparsity at: 0.0 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 9.0569180e-10 ... -9.6863449e-02 1.1928176e-01 4.4454457e-03] Sparsity at: 0.0 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.04354055087536146 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.09655690938234329 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.1891767531633377 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 51s 7ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 1.9813654e-12 ... -9.6758649e-02 1.1963684e-01 4.7810404e-03] Sparsity at: 0.0 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7874 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 1.4954420e-08 ... -9.7065508e-02 1.1963763e-01 4.4830227e-03] Sparsity at: 0.0 Epoch 303/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8034 - accuracy: 0.9017 - val_loss: 0.7876 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -2.2151934e-13 ... -9.6511647e-02 1.1920562e-01 4.5002829e-03] Sparsity at: 0.0 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7883 - val_accuracy: 0.9027 [ 2.16602604e-34 2.35003912e-34 3.25067958e-07 ... -9.64846984e-02 1.19379744e-01 4.47428552e-03] Sparsity at: 0.0 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7879 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 2.2351882e-12 ... -9.6837774e-02 1.1939311e-01 4.7096675e-03] Sparsity at: 0.0 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7875 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -1.92370680e-06 ... -9.66595784e-02 1.19235486e-01 4.49894508e-03] Sparsity at: 0.0 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7876 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -2.4853144e-11 ... -9.6467055e-02 1.1942970e-01 4.4892719e-03] Sparsity at: 0.0 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7873 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 -5.94379526e-06 ... -9.67059210e-02 1.19311534e-01 4.66202479e-03] Sparsity at: 0.0 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7878 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 1.3937974e-10 ... -9.6830338e-02 1.1907794e-01 4.5704502e-03] Sparsity at: 0.0 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 1.0975641e-05 ... -9.6449256e-02 1.1920325e-01 4.6798955e-03] Sparsity at: 0.0 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9016 - val_loss: 0.7874 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.6697754e-11 ... -9.6872523e-02 1.1951569e-01 4.5997868e-03] Sparsity at: 0.0 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7866 - val_accuracy: 0.9038 [ 2.16602604e-34 2.35003912e-34 -2.48979420e-10 ... -9.67431739e-02 1.18946634e-01 4.69627418e-03] Sparsity at: 0.0 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 -9.64496216e-09 ... -9.67529193e-02 1.19327635e-01 4.82939230e-03] Sparsity at: 0.0 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -1.1436792e-13 ... -9.6809402e-02 1.1935494e-01 5.0393119e-03] Sparsity at: 0.0 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 1.8283681e-07 ... -9.6587539e-02 1.1930241e-01 4.7898130e-03] Sparsity at: 0.0 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7868 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -1.3485259e-12 ... -9.6862890e-02 1.1943786e-01 4.5788363e-03] Sparsity at: 0.0 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -1.3091324e-06 ... -9.6672483e-02 1.1953855e-01 4.6290169e-03] Sparsity at: 0.0 Epoch 318/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 1.0908414e-11 ... -9.6951917e-02 1.1968986e-01 4.7119153e-03] Sparsity at: 0.0 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -7.3423880e-06 ... -9.6485481e-02 1.1951679e-01 4.6446328e-03] Sparsity at: 0.0 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 5.75806729e-11 ... -9.68058780e-02 1.19720496e-01 4.68989322e-03] Sparsity at: 0.0 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 5.65887094e-05 ... -9.67035815e-02 1.19318314e-01 4.86098789e-03] Sparsity at: 0.0 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7871 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -3.2973174e-10 ... -9.6723825e-02 1.1966369e-01 4.6176454e-03] Sparsity at: 0.0 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -2.09763493e-05 ... -9.65095162e-02 1.19338565e-01 4.88473289e-03] Sparsity at: 0.0 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -1.15962240e-09 ... -9.64239538e-02 1.19232625e-01 4.63174284e-03] Sparsity at: 0.0 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -1.13957963e-08 ... -9.60857198e-02 1.18897706e-01 4.66355728e-03] Sparsity at: 0.0 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7886 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 4.2230637e-09 ... -9.6534915e-02 1.1929124e-01 4.7262451e-03] Sparsity at: 0.0 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9027 [ 2.16602604e-34 2.35003912e-34 8.70957494e-13 ... -9.65014920e-02 1.19195096e-01 4.85195080e-03] Sparsity at: 0.0 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9018 - val_loss: 0.7872 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 3.0507934e-08 ... -9.6479036e-02 1.1901334e-01 4.9957731e-03] Sparsity at: 0.0 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -2.1914743e-13 ... -9.6440524e-02 1.1922778e-01 4.5225793e-03] Sparsity at: 0.0 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7871 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -4.9577784e-07 ... -9.6479788e-02 1.1930700e-01 4.7602993e-03] Sparsity at: 0.0 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7864 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 2.5175803e-12 ... -9.6628048e-02 1.1924239e-01 4.7345008e-03] Sparsity at: 0.0 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7886 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -5.19743207e-06 ... -9.68395472e-02 1.19826764e-01 5.04155038e-03] Sparsity at: 0.0 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -2.97040934e-11 ... -9.65827703e-02 1.19302906e-01 4.90569044e-03] Sparsity at: 0.0 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9025 [ 2.1660260e-34 2.3500391e-34 6.0509337e-05 ... -9.6771836e-02 1.1961913e-01 4.8657497e-03] Sparsity at: 0.0 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -3.66719544e-10 ... -9.67598334e-02 1.19739056e-01 5.06662717e-03] Sparsity at: 0.0 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7884 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -4.8988111e-09 ... -9.6538819e-02 1.1952799e-01 4.8865764e-03] Sparsity at: 0.0 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 3.9914392e-09 ... -9.6825629e-02 1.1969762e-01 5.1835999e-03] Sparsity at: 0.0 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9025 [ 2.16602604e-34 2.35003912e-34 -1.23081504e-12 ... -9.65617672e-02 1.19647324e-01 5.02907345e-03] Sparsity at: 0.0 Epoch 339/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 1.3323797e-08 ... -9.6829847e-02 1.1963277e-01 4.9272422e-03] Sparsity at: 0.0 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7876 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -1.6279609e-14 ... -9.6451759e-02 1.1965431e-01 5.0872872e-03] Sparsity at: 0.0 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9037 [ 2.16602604e-34 2.35003912e-34 -2.14681364e-07 ... -9.64742228e-02 1.19442746e-01 4.93557937e-03] Sparsity at: 0.0 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 5.0729711e-13 ... -9.7101122e-02 1.1981982e-01 5.0652577e-03] Sparsity at: 0.0 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7867 - val_accuracy: 0.9042 [ 2.1660260e-34 2.3500391e-34 3.0280276e-07 ... -9.6620522e-02 1.1969310e-01 4.7933762e-03] Sparsity at: 0.0 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7876 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 2.2298647e-12 ... -9.6711516e-02 1.1975234e-01 4.7575212e-03] Sparsity at: 0.0 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 1.5909659e-06 ... -9.6821472e-02 1.1988509e-01 5.0525502e-03] Sparsity at: 0.0 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7888 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 2.1747937e-11 ... -9.6737847e-02 1.2001068e-01 5.0700046e-03] Sparsity at: 0.0 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -1.3313786e-05 ... -9.6733183e-02 1.1966812e-01 5.3257551e-03] Sparsity at: 0.0 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 1.3955864e-11 ... -9.6871786e-02 1.1981682e-01 5.3792899e-03] Sparsity at: 0.0 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -2.5418412e-05 ... -9.7003378e-02 1.1982142e-01 5.3846450e-03] Sparsity at: 0.0 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -1.2926575e-10 ... -9.6693955e-02 1.1954377e-01 5.1366673e-03] Sparsity at: 0.0 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.05145336870375461 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.10824567258369111 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.2044821729245374 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7872 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 9.8307639e-05 ... -9.7003378e-02 1.1950693e-01 5.2235881e-03] Sparsity at: 0.0 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7873 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -2.7235830e-10 ... -9.6948385e-02 1.2008926e-01 5.4087066e-03] Sparsity at: 0.0 Epoch 353/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 9.02552256e-06 ... -9.68917087e-02 1.20336965e-01 5.30159194e-03] Sparsity at: 0.0 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -3.7707426e-10 ... -9.6661590e-02 1.1938381e-01 5.4519870e-03] Sparsity at: 0.0 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -7.3293333e-12 ... -9.6732311e-02 1.1958466e-01 5.6256307e-03] Sparsity at: 0.0 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7878 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -6.7991874e-09 ... -9.6412078e-02 1.1975115e-01 5.7636732e-03] Sparsity at: 0.0 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7879 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 1.5820258e-13 ... -9.7008251e-02 1.1969633e-01 5.5577708e-03] Sparsity at: 0.0 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7879 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -3.0235458e-07 ... -9.6487977e-02 1.1962325e-01 5.5306531e-03] Sparsity at: 0.0 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7880 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 5.9825289e-13 ... -9.6958421e-02 1.2016310e-01 5.3909556e-03] Sparsity at: 0.0 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -1.3786761e-05 ... -9.6753158e-02 1.1949889e-01 5.7063168e-03] Sparsity at: 0.0 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9018 - val_loss: 0.7878 - val_accuracy: 0.9045 [ 2.1660260e-34 2.3500391e-34 -7.1528880e-11 ... -9.6739851e-02 1.1973554e-01 5.7600029e-03] Sparsity at: 0.0 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -5.5535310e-08 ... -9.7103253e-02 1.1998576e-01 5.7675806e-03] Sparsity at: 0.0 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7884 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -3.2936032e-09 ... -9.7003855e-02 1.2026122e-01 5.8288397e-03] Sparsity at: 0.0 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7881 - val_accuracy: 0.9026 [ 2.16602604e-34 2.35003912e-34 -6.90607172e-14 ... -9.69852731e-02 1.20121114e-01 5.72779169e-03] Sparsity at: 0.0 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9017 - val_loss: 0.7875 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -1.0825247e-07 ... -9.6848883e-02 1.1989435e-01 5.6389375e-03] Sparsity at: 0.0 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7885 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 -2.2451767e-13 ... -9.7046338e-02 1.1985630e-01 5.9226602e-03] Sparsity at: 0.0 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 6.3173502e-08 ... -9.6872389e-02 1.1989870e-01 5.6399065e-03] Sparsity at: 0.0 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9041 [ 2.1660260e-34 2.3500391e-34 -1.0421018e-11 ... -9.6868396e-02 1.1972114e-01 5.8509554e-03] Sparsity at: 0.0 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9016 - val_loss: 0.7881 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -1.0213105e-05 ... -9.7003013e-02 1.2012153e-01 5.8855652e-03] Sparsity at: 0.0 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9015 - val_loss: 0.7881 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -1.1101617e-10 ... -9.6890718e-02 1.1999429e-01 5.8214618e-03] Sparsity at: 0.0 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 4.6373668e-07 ... -9.6690968e-02 1.2004442e-01 5.6810924e-03] Sparsity at: 0.0 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 2.41512188e-09 ... -9.66891497e-02 1.19978495e-01 5.78028103e-03] Sparsity at: 0.0 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7881 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 1.9671905e-13 ... -9.7123556e-02 1.2004318e-01 6.2524495e-03] Sparsity at: 0.0 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 -4.60832723e-08 ... -9.71458852e-02 1.20091215e-01 6.10441016e-03] Sparsity at: 0.0 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 8.4539445e-14 ... -9.6581340e-02 1.1950767e-01 6.1184750e-03] Sparsity at: 0.0 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -2.3984039e-06 ... -9.6876912e-02 1.1959945e-01 5.7548177e-03] Sparsity at: 0.0 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7865 - val_accuracy: 0.9040 [ 2.16602604e-34 2.35003912e-34 1.08241003e-11 ... -9.68324915e-02 1.19781375e-01 5.71073405e-03] Sparsity at: 0.0 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -5.75929917e-05 ... -9.66646522e-02 1.19968995e-01 5.81616024e-03] Sparsity at: 0.0 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -1.40503678e-10 ... -9.71320048e-02 1.20432705e-01 5.98466583e-03] Sparsity at: 0.0 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9031 [ 2.16602604e-34 2.35003912e-34 3.84834303e-10 ... -9.69519243e-02 1.20028965e-01 6.13523042e-03] Sparsity at: 0.0 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7880 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -8.9390895e-10 ... -9.6935637e-02 1.1984949e-01 6.1177881e-03] Sparsity at: 0.0 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7879 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 2.7136625e-14 ... -9.6559122e-02 1.1982359e-01 6.0703331e-03] Sparsity at: 0.0 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 6.2195369e-09 ... -9.6723981e-02 1.1982583e-01 6.0848384e-03] Sparsity at: 0.0 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7871 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -8.3749094e-13 ... -9.6717395e-02 1.1964802e-01 5.7577873e-03] Sparsity at: 0.0 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7876 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -3.8387143e-06 ... -9.6656099e-02 1.1975571e-01 5.9236684e-03] Sparsity at: 0.0 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -3.7445090e-12 ... -9.6875966e-02 1.1975334e-01 5.9791021e-03] Sparsity at: 0.0 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 1.6371628e-04 ... -9.6999265e-02 1.1970727e-01 5.8977683e-03] Sparsity at: 0.0 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7883 - val_accuracy: 0.9017 [ 2.1660260e-34 2.3500391e-34 2.1304680e-11 ... -9.6909545e-02 1.1974877e-01 6.0302406e-03] Sparsity at: 0.0 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 4.0213388e-12 ... -9.6977040e-02 1.1966834e-01 5.8283499e-03] Sparsity at: 0.0 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 1.9608642e-08 ... -9.6805379e-02 1.1951526e-01 5.8710417e-03] Sparsity at: 0.0 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9011 - val_loss: 0.7872 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -1.1520227e-13 ... -9.6653968e-02 1.1946594e-01 5.8363043e-03] Sparsity at: 0.0 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9037 [ 2.16602604e-34 2.35003912e-34 -5.55430006e-07 ... -9.65471342e-02 1.19637795e-01 5.66599751e-03] Sparsity at: 0.0 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7881 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 5.7021861e-12 ... -9.6932977e-02 1.1960710e-01 5.8986577e-03] Sparsity at: 0.0 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 3.7718397e-05 ... -9.6699521e-02 1.1938030e-01 5.8800378e-03] Sparsity at: 0.0 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 1.7203144e-10 ... -9.7028583e-02 1.1972045e-01 5.8591082e-03] Sparsity at: 0.0 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7881 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 2.9206672e-07 ... -9.6768357e-02 1.1972195e-01 5.9745307e-03] Sparsity at: 0.0 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9016 - val_loss: 0.7872 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 -2.49674037e-09 ... -9.69161913e-02 1.19651906e-01 5.85845439e-03] Sparsity at: 0.0 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9012 - val_loss: 0.7874 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 -1.13944696e-11 ... -9.69721898e-02 1.19592935e-01 6.19879598e-03] Sparsity at: 0.0 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7871 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -1.9942899e-08 ... -9.6477263e-02 1.1951710e-01 6.0139969e-03] Sparsity at: 0.0 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -1.25924051e-13 ... -9.66862217e-02 1.19570866e-01 6.08662562e-03] Sparsity at: 0.0 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.05604266125902191 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.1164555019028306 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.2093774694217707 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 47s 7ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7876 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 2.11803837e-08 ... -9.68910083e-02 1.19653165e-01 6.18669903e-03] Sparsity at: 0.0 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7872 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -8.2226381e-13 ... -9.6993789e-02 1.1996815e-01 6.0370034e-03] Sparsity at: 0.0 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -3.2960776e-07 ... -9.6749894e-02 1.2017904e-01 5.9685670e-03] Sparsity at: 0.0 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -1.6154834e-12 ... -9.6703604e-02 1.2003152e-01 6.1346986e-03] Sparsity at: 0.0 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7868 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -2.5189538e-06 ... -9.7272448e-02 1.2017930e-01 6.0802735e-03] Sparsity at: 0.0 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9017 - val_loss: 0.7877 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -1.0730449e-11 ... -9.7314313e-02 1.2002343e-01 6.3132341e-03] Sparsity at: 0.0 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7870 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -4.2505904e-05 ... -9.6687689e-02 1.1983128e-01 6.2026680e-03] Sparsity at: 0.0 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7882 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 1.71331338e-10 ... -9.68058631e-02 1.19859695e-01 5.86007209e-03] Sparsity at: 0.0 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7864 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 -2.0586026e-06 ... -9.6797362e-02 1.1959352e-01 6.1214152e-03] Sparsity at: 0.0 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 1.9371993e-09 ... -9.6964777e-02 1.1994149e-01 6.0078702e-03] Sparsity at: 0.0 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 2.5125373e-12 ... -9.6986793e-02 1.1979263e-01 6.2496606e-03] Sparsity at: 0.0 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 2.49314080e-08 ... -9.68761966e-02 1.19823396e-01 6.05533179e-03] Sparsity at: 0.0 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -2.2053271e-13 ... -9.6797533e-02 1.1988019e-01 6.0640452e-03] Sparsity at: 0.0 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7874 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -4.51945596e-07 ... -9.67677832e-02 1.20063774e-01 6.09964831e-03] Sparsity at: 0.0 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9017 - val_loss: 0.7877 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 2.5493010e-12 ... -9.6984856e-02 1.2006588e-01 6.0387384e-03] Sparsity at: 0.0 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9034 [ 2.16602604e-34 2.35003912e-34 -4.28598241e-06 ... -9.70633179e-02 1.20159596e-01 6.27308572e-03] Sparsity at: 0.0 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7866 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 3.1537772e-11 ... -9.6733533e-02 1.2000912e-01 6.1094481e-03] Sparsity at: 0.0 Epoch 418/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7876 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 -9.3952549e-05 ... -9.6853152e-02 1.2001436e-01 6.1458792e-03] Sparsity at: 0.0 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7882 - val_accuracy: 0.9029 [ 2.16602604e-34 2.35003912e-34 4.03115652e-10 ... -9.68397930e-02 1.19782664e-01 6.07515452e-03] Sparsity at: 0.0 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7876 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 8.2856708e-09 ... -9.7064942e-02 1.2006970e-01 6.3092215e-03] Sparsity at: 0.0 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8030 - accuracy: 0.9016 - val_loss: 0.7880 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -1.6366297e-09 ... -9.6863873e-02 1.2016517e-01 6.0936185e-03] Sparsity at: 0.0 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9017 - val_loss: 0.7879 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 3.47464006e-12 ... -9.69051123e-02 1.19845696e-01 6.19111676e-03] Sparsity at: 0.0 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7886 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 2.7047111e-08 ... -9.6784107e-02 1.2018630e-01 6.0146796e-03] Sparsity at: 0.0 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7864 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 2.5319110e-13 ... -9.6873581e-02 1.1975906e-01 6.0938802e-03] Sparsity at: 0.0 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9015 - val_loss: 0.7864 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -2.26299392e-07 ... -9.70703736e-02 1.19921975e-01 5.97802410e-03] Sparsity at: 0.0 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7874 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -1.2849633e-12 ... -9.6831903e-02 1.1997407e-01 6.1513130e-03] Sparsity at: 0.0 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7874 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -3.7824166e-06 ... -9.6877001e-02 1.1998094e-01 6.1150845e-03] Sparsity at: 0.0 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7878 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 -6.7700598e-12 ... -9.6610129e-02 1.1990828e-01 6.1241817e-03] Sparsity at: 0.0 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7870 - val_accuracy: 0.9041 [ 2.1660260e-34 2.3500391e-34 -1.1619132e-05 ... -9.6801765e-02 1.1985122e-01 6.4164218e-03] Sparsity at: 0.0 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7871 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 1.5070659e-10 ... -9.6866682e-02 1.2019607e-01 6.1873957e-03] Sparsity at: 0.0 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9017 - val_loss: 0.7882 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 -6.6356870e-07 ... -9.6852802e-02 1.2018902e-01 6.2581245e-03] Sparsity at: 0.0 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9038 [ 2.16602604e-34 2.35003912e-34 -2.40354914e-09 ... -9.68077853e-02 1.20021254e-01 6.34369720e-03] Sparsity at: 0.0 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9026 [ 2.1660260e-34 2.3500391e-34 2.5558396e-14 ... -9.6869588e-02 1.1994986e-01 6.4454237e-03] Sparsity at: 0.0 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 3.22880105e-08 ... -9.66106653e-02 1.19935594e-01 6.26665773e-03] Sparsity at: 0.0 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9015 - val_loss: 0.7877 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 3.94372848e-14 ... -9.66278687e-02 1.19856775e-01 6.34732330e-03] Sparsity at: 0.0 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9036 [ 2.1660260e-34 2.3500391e-34 3.0181363e-05 ... -9.6726850e-02 1.2010156e-01 6.0569569e-03] Sparsity at: 0.0 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 -7.58396679e-11 ... -9.66314375e-02 1.20232135e-01 6.24407222e-03] Sparsity at: 0.0 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 3.62845452e-15 ... -9.67943892e-02 1.20058715e-01 6.38965983e-03] Sparsity at: 0.0 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7879 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -1.0136169e-07 ... -9.6574388e-02 1.2011247e-01 5.9472765e-03] Sparsity at: 0.0 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7879 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -9.0069694e-13 ... -9.6664295e-02 1.2008607e-01 6.3667754e-03] Sparsity at: 0.0 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7873 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -1.8208662e-05 ... -9.6821517e-02 1.1988639e-01 6.4522256e-03] Sparsity at: 0.0 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7888 - val_accuracy: 0.9027 [ 2.1660260e-34 2.3500391e-34 -1.4914363e-10 ... -9.6652083e-02 1.1993245e-01 6.4796647e-03] Sparsity at: 0.0 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7871 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 3.6563585e-10 ... -9.6730076e-02 1.2016911e-01 6.0588238e-03] Sparsity at: 0.0 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 9.1120782e-09 ... -9.6796393e-02 1.2006035e-01 6.1519514e-03] Sparsity at: 0.0 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7873 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -8.7613077e-14 ... -9.6633457e-02 1.2002222e-01 6.2637120e-03] Sparsity at: 0.0 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 -1.7388149e-07 ... -9.6840307e-02 1.2035176e-01 6.0975980e-03] Sparsity at: 0.0 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7888 - val_accuracy: 0.9028 [ 2.1660260e-34 2.3500391e-34 -4.9876769e-13 ... -9.7027496e-02 1.2016390e-01 6.5167742e-03] Sparsity at: 0.0 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9016 - val_loss: 0.7872 - val_accuracy: 0.9042 [ 2.16602604e-34 2.35003912e-34 -3.13358237e-06 ... -9.67752635e-02 1.19794026e-01 6.25483645e-03] Sparsity at: 0.0 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9019 - val_loss: 0.7867 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 1.3018302e-12 ... -9.6899807e-02 1.1963556e-01 6.4656343e-03] Sparsity at: 0.0 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7871 - val_accuracy: 0.9041 [ 2.16602604e-34 2.35003912e-34 -4.21410587e-05 ... -9.68064517e-02 1.19539544e-01 6.40239334e-03] Sparsity at: 0.0 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 2.1450042e-10 ... -9.6703365e-02 1.1972029e-01 6.0641859e-03] Sparsity at: 0.0 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9016 - val_loss: 0.7871 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 1.5870432e-05 ... -9.6656941e-02 1.1982519e-01 5.8807763e-03] Sparsity at: 0.0 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7872 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -2.2737554e-10 ... -9.6391819e-02 1.1990812e-01 5.9275297e-03] Sparsity at: 0.0 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9017 - val_loss: 0.7871 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -5.4503957e-09 ... -9.6634492e-02 1.1957248e-01 6.3576996e-03] Sparsity at: 0.0 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 1.7715203e-09 ... -9.6448474e-02 1.1943516e-01 6.1257286e-03] Sparsity at: 0.0 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7875 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -1.2278869e-12 ... -9.6641734e-02 1.1943556e-01 6.0009961e-03] Sparsity at: 0.0 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 4.6767150e-09 ... -9.6941188e-02 1.1988121e-01 6.3115200e-03] Sparsity at: 0.0 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -2.0568784e-13 ... -9.6694328e-02 1.1969359e-01 6.1274185e-03] Sparsity at: 0.0 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9015 - val_loss: 0.7879 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 6.8728997e-07 ... -9.6564814e-02 1.1977622e-01 5.9234733e-03] Sparsity at: 0.0 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7869 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -9.6142599e-12 ... -9.6528895e-02 1.1958755e-01 6.4544128e-03] Sparsity at: 0.0 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 -3.3623728e-05 ... -9.6462905e-02 1.1981018e-01 6.5388810e-03] Sparsity at: 0.0 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7877 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -2.2112900e-10 ... -9.6497320e-02 1.1966681e-01 6.3337944e-03] Sparsity at: 0.0 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9036 [ 2.16602604e-34 2.35003912e-34 -3.43370250e-14 ... -9.64868590e-02 1.19646326e-01 6.32484723e-03] Sparsity at: 0.0 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7880 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -6.2970102e-08 ... -9.6439809e-02 1.1963749e-01 6.4403615e-03] Sparsity at: 0.0 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -6.01244098e-13 ... -9.63304564e-02 1.19518206e-01 6.16516592e-03] Sparsity at: 0.0 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 8.9890773e-07 ... -9.6772701e-02 1.1963927e-01 6.5734200e-03] Sparsity at: 0.0 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 1.0336487e-11 ... -9.6511684e-02 1.1973165e-01 6.1983364e-03] Sparsity at: 0.0 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 1.3799511e-04 ... -9.6806854e-02 1.2002606e-01 6.0931677e-03] Sparsity at: 0.0 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 3.8945291e-11 ... -9.6508339e-02 1.1983407e-01 6.4912629e-03] Sparsity at: 0.0 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9016 - val_loss: 0.7877 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 2.8059024e-12 ... -9.6576661e-02 1.1978862e-01 6.4580557e-03] Sparsity at: 0.0 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9035 [ 2.16602604e-34 2.35003912e-34 2.24938788e-08 ... -9.66281071e-02 1.19458735e-01 6.53707841e-03] Sparsity at: 0.0 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.9014 - val_loss: 0.7887 - val_accuracy: 0.9024 [ 2.1660260e-34 2.3500391e-34 4.3516406e-14 ... -9.6558474e-02 1.2012070e-01 6.3651972e-03] Sparsity at: 0.0 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7875 - val_accuracy: 0.9031 [ 2.1660260e-34 2.3500391e-34 6.3062373e-07 ... -9.6434094e-02 1.1962295e-01 6.0375966e-03] Sparsity at: 0.0 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7882 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -1.9675771e-12 ... -9.6351877e-02 1.1966482e-01 5.7850243e-03] Sparsity at: 0.0 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7865 - val_accuracy: 0.9045 [ 2.1660260e-34 2.3500391e-34 3.9097718e-05 ... -9.6481562e-02 1.1972946e-01 6.2424713e-03] Sparsity at: 0.0 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7878 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -2.3405267e-10 ... -9.6507706e-02 1.1951510e-01 6.3635283e-03] Sparsity at: 0.0 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7876 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -3.3314522e-09 ... -9.6587010e-02 1.1939046e-01 6.4207520e-03] Sparsity at: 0.0 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 5.6890537e-09 ... -9.6447788e-02 1.1964595e-01 6.4400886e-03] Sparsity at: 0.0 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9013 - val_loss: 0.7873 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 4.5537695e-13 ... -9.6305668e-02 1.1946895e-01 6.4479001e-03] Sparsity at: 0.0 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7869 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 -1.52319544e-08 ... -9.64807943e-02 1.19484685e-01 6.56530866e-03] Sparsity at: 0.0 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7879 - val_accuracy: 0.9032 [ 2.1660260e-34 2.3500391e-34 5.0520140e-13 ... -9.6256882e-02 1.1955718e-01 6.2320633e-03] Sparsity at: 0.0 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9013 - val_loss: 0.7872 - val_accuracy: 0.9039 [ 2.1660260e-34 2.3500391e-34 -3.4109598e-07 ... -9.6293196e-02 1.1936905e-01 5.8118533e-03] Sparsity at: 0.0 Epoch 483/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8035 - accuracy: 0.9015 - val_loss: 0.7878 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 2.1875045e-12 ... -9.6251570e-02 1.1964322e-01 6.3000531e-03] Sparsity at: 0.0 Epoch 484/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7872 - val_accuracy: 0.9044 [ 2.1660260e-34 2.3500391e-34 9.0741116e-07 ... -9.6350372e-02 1.1956038e-01 6.0452777e-03] Sparsity at: 0.0 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7880 - val_accuracy: 0.9028 [ 2.16602604e-34 2.35003912e-34 2.10113307e-12 ... -9.65864584e-02 1.19602114e-01 6.43824181e-03] Sparsity at: 0.0 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9011 - val_loss: 0.7877 - val_accuracy: 0.9037 [ 2.1660260e-34 2.3500391e-34 6.9361881e-06 ... -9.6337333e-02 1.1962793e-01 6.2364973e-03] Sparsity at: 0.0 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9015 - val_loss: 0.7874 - val_accuracy: 0.9035 [ 2.1660260e-34 2.3500391e-34 -1.6157064e-11 ... -9.6428081e-02 1.1945501e-01 6.1982074e-03] Sparsity at: 0.0 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9015 - val_loss: 0.7873 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 6.8449899e-06 ... -9.6295506e-02 1.1927847e-01 6.2280563e-03] Sparsity at: 0.0 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9014 - val_loss: 0.7885 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -1.0986220e-10 ... -9.6211970e-02 1.1972855e-01 6.2315315e-03] Sparsity at: 0.0 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9015 - val_loss: 0.7882 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -7.9979473e-05 ... -9.6423574e-02 1.1982947e-01 6.3107652e-03] Sparsity at: 0.0 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7877 - val_accuracy: 0.9030 [ 2.16602604e-34 2.35003912e-34 1.95383321e-10 ... -9.62549597e-02 1.19502805e-01 6.25532959e-03] Sparsity at: 0.0 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7877 - val_accuracy: 0.9040 [ 2.1660260e-34 2.3500391e-34 -7.1219179e-06 ... -9.6297696e-02 1.1940506e-01 6.5222029e-03] Sparsity at: 0.0 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9014 - val_loss: 0.7875 - val_accuracy: 0.9033 [ 2.16602604e-34 2.35003912e-34 -7.03366088e-10 ... -9.62297618e-02 1.19293556e-01 6.43690070e-03] Sparsity at: 0.0 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9034 [ 2.1660260e-34 2.3500391e-34 -5.7153233e-09 ... -9.6396729e-02 1.1946530e-01 6.3012154e-03] Sparsity at: 0.0 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9014 - val_loss: 0.7876 - val_accuracy: 0.9038 [ 2.1660260e-34 2.3500391e-34 -1.5976465e-09 ... -9.6504703e-02 1.1962247e-01 6.5393555e-03] Sparsity at: 0.0 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7883 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 -3.8030327e-13 ... -9.6533053e-02 1.1964298e-01 6.4152358e-03] Sparsity at: 0.0 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7875 - val_accuracy: 0.9029 [ 2.1660260e-34 2.3500391e-34 -4.1943888e-08 ... -9.6567661e-02 1.1965421e-01 6.4011645e-03] Sparsity at: 0.0 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9032 [ 2.16602604e-34 2.35003912e-34 2.13910703e-13 ... -9.63872373e-02 1.19854644e-01 6.28406275e-03] Sparsity at: 0.0 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7873 - val_accuracy: 0.9033 [ 2.1660260e-34 2.3500391e-34 -3.7488508e-07 ... -9.6168473e-02 1.1950279e-01 6.4220098e-03] Sparsity at: 0.0 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9016 - val_loss: 0.7881 - val_accuracy: 0.9030 [ 2.1660260e-34 2.3500391e-34 6.9685707e-13 ... -9.6381672e-02 1.1948452e-01 6.2607042e-03] Sparsity at: 0.0 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.042017221450805664 Thresholhold -0.06162944808602333 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08907948434352875 Thresholhold -0.10798515379428864 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10679344832897186 Thresholhold -0.06120911240577698 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:55 - loss: 2.3590 - accuracy: 0.1562WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_begin` time: 2.5039s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 0.4625 - accuracy: 0.8706 - val_loss: 0.2481 - val_accuracy: 0.9271 [-0.06162945 0.01141503 -0.00061712 ... -0.24551293 -0.07918725 -0.01226392] Sparsity at: 0.0 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2256 - accuracy: 0.9347 - val_loss: 0.1875 - val_accuracy: 0.9459 [-0.06162945 0.01141503 -0.00061712 ... -0.27347594 -0.08841381 -0.01025021] Sparsity at: 0.0 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1710 - accuracy: 0.9496 - val_loss: 0.1556 - val_accuracy: 0.9545 [-0.06162945 0.01141503 -0.00061712 ... -0.29839796 -0.09410758 -0.00589253] Sparsity at: 0.0 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1375 - accuracy: 0.9596 - val_loss: 0.1364 - val_accuracy: 0.9598 [-0.06162945 0.01141503 -0.00061712 ... -0.3188608 -0.0981459 -0.0017014 ] Sparsity at: 0.0 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1135 - accuracy: 0.9670 - val_loss: 0.1237 - val_accuracy: 0.9633 [-0.06162945 0.01141503 -0.00061712 ... -0.33559248 -0.10105435 0.00084789] Sparsity at: 0.0 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0954 - accuracy: 0.9725 - val_loss: 0.1148 - val_accuracy: 0.9646 [-0.06162945 0.01141503 -0.00061712 ... -0.34973148 -0.10330356 0.00239677] Sparsity at: 0.0 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0810 - accuracy: 0.9761 - val_loss: 0.1089 - val_accuracy: 0.9660 [-0.06162945 0.01141503 -0.00061712 ... -0.36135045 -0.1054047 0.00306919] Sparsity at: 0.0 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0694 - accuracy: 0.9801 - val_loss: 0.1059 - val_accuracy: 0.9682 [-0.06162945 0.01141503 -0.00061712 ... -0.37208366 -0.10731635 0.0033695 ] Sparsity at: 0.0 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.1024 - val_accuracy: 0.9692 [-0.06162945 0.01141503 -0.00061712 ... -0.38165015 -0.10870424 0.00371294] Sparsity at: 0.0 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0518 - accuracy: 0.9854 - val_loss: 0.1010 - val_accuracy: 0.9703 [-0.06162945 0.01141503 -0.00061712 ... -0.3913611 -0.10980254 0.00381318] Sparsity at: 0.0 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0450 - accuracy: 0.9873 - val_loss: 0.0996 - val_accuracy: 0.9706 [-0.06162945 0.01141503 -0.00061712 ... -0.40049657 -0.11058482 0.00409445] Sparsity at: 0.0 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0390 - accuracy: 0.9894 - val_loss: 0.1004 - val_accuracy: 0.9712 [-0.06162945 0.01141503 -0.00061712 ... -0.41025305 -0.11113498 0.00488364] Sparsity at: 0.0 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0340 - accuracy: 0.9910 - val_loss: 0.1008 - val_accuracy: 0.9714 [-0.06162945 0.01141503 -0.00061712 ... -0.42007586 -0.11131507 0.00613106] Sparsity at: 0.0 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0292 - accuracy: 0.9926 - val_loss: 0.1015 - val_accuracy: 0.9708 [-0.06162945 0.01141503 -0.00061712 ... -0.43122864 -0.1111505 0.00819975] Sparsity at: 0.0 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0250 - accuracy: 0.9939 - val_loss: 0.1031 - val_accuracy: 0.9710 [-0.06162945 0.01141503 -0.00061712 ... -0.44218895 -0.11258055 0.01155277] Sparsity at: 0.0 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0213 - accuracy: 0.9954 - val_loss: 0.1045 - val_accuracy: 0.9720 [-0.06162945 0.01141503 -0.00061712 ... -0.45352656 -0.11415644 0.01519095] Sparsity at: 0.0 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0183 - accuracy: 0.9964 - val_loss: 0.1090 - val_accuracy: 0.9705 [-0.06162945 0.01141503 -0.00061712 ... -0.46490172 -0.11548899 0.01807612] Sparsity at: 0.0 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0157 - accuracy: 0.9973 - val_loss: 0.1107 - val_accuracy: 0.9706 [-0.06162945 0.01141503 -0.00061712 ... -0.47607267 -0.11764724 0.02133511] Sparsity at: 0.0 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0135 - accuracy: 0.9978 - val_loss: 0.1123 - val_accuracy: 0.9711 [-0.06162945 0.01141503 -0.00061712 ... -0.4872291 -0.12064837 0.02294913] Sparsity at: 0.0 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0119 - accuracy: 0.9981 - val_loss: 0.1181 - val_accuracy: 0.9704 [-0.06162945 0.01141503 -0.00061712 ... -0.49602535 -0.12553558 0.02708043] Sparsity at: 0.0 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0108 - accuracy: 0.9981 - val_loss: 0.1206 - val_accuracy: 0.9713 [-0.06162945 0.01141503 -0.00061712 ... -0.50377405 -0.13176425 0.03063378] Sparsity at: 0.0 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0104 - accuracy: 0.9979 - val_loss: 0.1180 - val_accuracy: 0.9718 [-0.06162945 0.01141503 -0.00061712 ... -0.5136285 -0.13778561 0.02540568] Sparsity at: 0.0 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0106 - accuracy: 0.9975 - val_loss: 0.1241 - val_accuracy: 0.9703 [-0.06162945 0.01141503 -0.00061712 ... -0.5221462 -0.14337744 0.02686568] Sparsity at: 0.0 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0114 - accuracy: 0.9971 - val_loss: 0.1265 - val_accuracy: 0.9701 [-0.06162945 0.01141503 -0.00061712 ... -0.5349539 -0.14867003 0.02780755] Sparsity at: 0.0 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0106 - accuracy: 0.9970 - val_loss: 0.1244 - val_accuracy: 0.9719 [-0.06162945 0.01141503 -0.00061712 ... -0.5375083 -0.1524253 0.01640458] Sparsity at: 0.0 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.1402 - val_accuracy: 0.9672 [-0.06162945 0.01141503 -0.00061712 ... -0.5509785 -0.14408737 0.0099421 ] Sparsity at: 0.0 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9976 - val_loss: 0.1204 - val_accuracy: 0.9735 [-0.06162945 0.01141503 -0.00061712 ... -0.55805826 -0.14622964 0.01139615] Sparsity at: 0.0 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0068 - accuracy: 0.9985 - val_loss: 0.1179 - val_accuracy: 0.9746 [-0.06162945 0.01141503 -0.00061712 ... -0.56111133 -0.14597611 -0.00142569] Sparsity at: 0.0 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0057 - accuracy: 0.9987 - val_loss: 0.1211 - val_accuracy: 0.9750 [-0.06162945 0.01141503 -0.00061712 ... -0.5670354 -0.14752737 0.00125402] Sparsity at: 0.0 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.1270 - val_accuracy: 0.9741 [-0.06162945 0.01141503 -0.00061712 ... -0.572229 -0.15037076 0.00352814] Sparsity at: 0.0 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0040 - accuracy: 0.9994 - val_loss: 0.1307 - val_accuracy: 0.9731 [-0.06162945 0.01141503 -0.00061712 ... -0.57740575 -0.15446031 0.00388532] Sparsity at: 0.0 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9999 - val_loss: 0.1193 - val_accuracy: 0.9760 [-0.06162945 0.01141503 -0.00061712 ... -0.5823741 -0.15239309 0.00552276] Sparsity at: 0.0 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1225 - val_accuracy: 0.9758 [-0.06162945 0.01141503 -0.00061712 ... -0.5870701 -0.15869783 0.00642516] Sparsity at: 0.0 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.1264 - val_accuracy: 0.9749 [-0.06162945 0.01141503 -0.00061712 ... -0.5896417 -0.15583056 0.00965309] Sparsity at: 0.0 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1275 - val_accuracy: 0.9749 [-0.06162945 0.01141503 -0.00061712 ... -0.5919178 -0.16490872 0.01448976] Sparsity at: 0.0 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.1602 - val_accuracy: 0.9687 [-0.06162945 0.01141503 -0.00061712 ... -0.6050982 -0.16071376 0.01137671] Sparsity at: 0.0 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.1420 - val_accuracy: 0.9726 [-0.06162945 0.01141503 -0.00061712 ... -0.6028426 -0.16374318 0.00796165] Sparsity at: 0.0 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1361 - val_accuracy: 0.9740 [-0.06162945 0.01141503 -0.00061712 ... -0.604199 -0.16657922 0.02173672] Sparsity at: 0.0 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.1404 - val_accuracy: 0.9741 [-0.06162945 0.01141503 -0.00061712 ... -0.6109027 -0.15875584 0.02848339] Sparsity at: 0.0 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 0.1461 - val_accuracy: 0.9726 [-0.06162945 0.01141503 -0.00061712 ... -0.6128059 -0.17361939 0.02422939] Sparsity at: 0.0 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.1458 - val_accuracy: 0.9734 [-0.06162945 0.01141503 -0.00061712 ... -0.6146821 -0.17851873 0.0223424 ] Sparsity at: 0.0 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1442 - val_accuracy: 0.9745 [-0.06162945 0.01141503 -0.00061712 ... -0.6156024 -0.18271302 0.02176825] Sparsity at: 0.0 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.1607 - val_accuracy: 0.9736 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.18947625e-01 -1.84821114e-01 2.75941491e-02] Sparsity at: 0.0 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1521 - val_accuracy: 0.9739 [-0.06162945 0.01141503 -0.00061712 ... -0.6144523 -0.1768818 0.02819831] Sparsity at: 0.0 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 9.1685e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9751 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.20002091e-01 -1.78854421e-01 2.96269972e-02] Sparsity at: 0.0 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6178e-04 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.22364402e-01 -1.75523400e-01 2.79622562e-02] Sparsity at: 0.0 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.1655 - val_accuracy: 0.9699 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.26023233e-01 -1.90008566e-01 4.00779694e-02] Sparsity at: 0.0 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0099 - accuracy: 0.9965 - val_loss: 0.1449 - val_accuracy: 0.9732 [-0.06162945 0.01141503 -0.00061712 ... -0.6134141 -0.20012322 0.03351247] Sparsity at: 0.0 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 0.9996 - val_loss: 0.1463 - val_accuracy: 0.9754 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.30596995e-01 -1.92892626e-01 4.25914526e-02] Sparsity at: 0.0 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 8.8911e-04 - accuracy: 0.9999 - val_loss: 0.1415 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.25612915e-01 -1.93727970e-01 4.42183465e-02] Sparsity at: 0.0 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.12711033645731717 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.20644520067197902 Thresholhold -0.32372161746025085 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.42589658486443227 Thresholhold -0.12965960800647736 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 4.0956e-04 - accuracy: 1.0000 - val_loss: 0.1412 - val_accuracy: 0.9764 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.25857770e-01 -1.95057571e-01 4.21078727e-02] Sparsity at: 0.0 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 2.4635e-04 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.26612961e-01 -1.95202604e-01 4.08315845e-02] Sparsity at: 0.0 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0437e-04 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.27985895e-01 -1.95445284e-01 3.99722308e-02] Sparsity at: 0.0 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7960e-04 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.29442573e-01 -1.95800722e-01 3.92398424e-02] Sparsity at: 0.0 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6111e-04 - accuracy: 1.0000 - val_loss: 0.1429 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.30954683e-01 -1.96197331e-01 3.86395603e-02] Sparsity at: 0.0 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4591e-04 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.32520378e-01 -1.96601465e-01 3.81386764e-02] Sparsity at: 0.0 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3296e-04 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.34155512e-01 -1.97023571e-01 3.76701392e-02] Sparsity at: 0.0 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2165e-04 - accuracy: 1.0000 - val_loss: 0.1447 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.35838509e-01 -1.97454825e-01 3.72747742e-02] Sparsity at: 0.0 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1156e-04 - accuracy: 1.0000 - val_loss: 0.1453 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.37630582e-01 -1.97915986e-01 3.69126461e-02] Sparsity at: 0.0 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0234e-04 - accuracy: 1.0000 - val_loss: 0.1460 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.39488399e-01 -1.98367834e-01 3.65847126e-02] Sparsity at: 0.0 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 9.4091e-05 - accuracy: 1.0000 - val_loss: 0.1468 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.41410351e-01 -1.98837072e-01 3.62924114e-02] Sparsity at: 0.0 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 8.6385e-05 - accuracy: 1.0000 - val_loss: 0.1476 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.43404365e-01 -1.99307263e-01 3.60989273e-02] Sparsity at: 0.0 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9399e-05 - accuracy: 1.0000 - val_loss: 0.1483 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.45496964e-01 -1.99821174e-01 3.59319001e-02] Sparsity at: 0.0 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2917e-05 - accuracy: 1.0000 - val_loss: 0.1492 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.47642672e-01 -2.00294822e-01 3.57794166e-02] Sparsity at: 0.0 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6827e-05 - accuracy: 1.0000 - val_loss: 0.1501 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.49870813e-01 -2.00779572e-01 3.56225558e-02] Sparsity at: 0.0 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1336e-05 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.52199149e-01 -2.01286897e-01 3.55644450e-02] Sparsity at: 0.0 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6086e-05 - accuracy: 1.0000 - val_loss: 0.1520 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.54571235e-01 -2.01773405e-01 3.54774818e-02] Sparsity at: 0.0 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1319e-05 - accuracy: 1.0000 - val_loss: 0.1530 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.57033682e-01 -2.02323779e-01 3.54717933e-02] Sparsity at: 0.0 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6801e-05 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.59595847e-01 -2.02833235e-01 3.54407355e-02] Sparsity at: 0.0 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2671e-05 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.62196040e-01 -2.03397498e-01 3.54460850e-02] Sparsity at: 0.0 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8902e-05 - accuracy: 1.0000 - val_loss: 0.1563 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.64930940e-01 -2.03952521e-01 3.54560837e-02] Sparsity at: 0.0 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5332e-05 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.67739511e-01 -2.04535782e-01 3.54936197e-02] Sparsity at: 0.0 Epoch 73/500 235/235 [==============================] - 2s 7ms/step - loss: 3.2029e-05 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.70611739e-01 -2.05119058e-01 3.55706848e-02] Sparsity at: 0.0 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9069e-05 - accuracy: 1.0000 - val_loss: 0.1599 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.73581064e-01 -2.05759287e-01 3.56531031e-02] Sparsity at: 0.0 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6289e-05 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9772 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.76608860e-01 -2.06385598e-01 3.57374586e-02] Sparsity at: 0.0 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3732e-05 - accuracy: 1.0000 - val_loss: 0.1624 - val_accuracy: 0.9773 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.79711461e-01 -2.07035825e-01 3.58657874e-02] Sparsity at: 0.0 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1416e-05 - accuracy: 1.0000 - val_loss: 0.1638 - val_accuracy: 0.9774 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.82892621e-01 -2.07658365e-01 3.59435454e-02] Sparsity at: 0.0 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9277e-05 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9773 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.86146915e-01 -2.08323061e-01 3.61156762e-02] Sparsity at: 0.0 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7339e-05 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9771 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.89458787e-01 -2.08996817e-01 3.62223499e-02] Sparsity at: 0.0 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5588e-05 - accuracy: 1.0000 - val_loss: 0.1679 - val_accuracy: 0.9771 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.92806542e-01 -2.09692389e-01 3.63398939e-02] Sparsity at: 0.0 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3978e-05 - accuracy: 1.0000 - val_loss: 0.1693 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.96232140e-01 -2.10378379e-01 3.64778563e-02] Sparsity at: 0.0 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2512e-05 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -6.99711800e-01 -2.11038306e-01 3.65896411e-02] Sparsity at: 0.0 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1200e-05 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.03220665e-01 -2.11757019e-01 3.67024280e-02] Sparsity at: 0.0 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9987e-06 - accuracy: 1.0000 - val_loss: 0.1735 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.06746817e-01 -2.12458625e-01 3.68241109e-02] Sparsity at: 0.0 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 8.9228e-06 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.10331440e-01 -2.13185370e-01 3.69695798e-02] Sparsity at: 0.0 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9703e-06 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.13927746e-01 -2.13878572e-01 3.71495970e-02] Sparsity at: 0.0 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0984e-06 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.17498720e-01 -2.14588717e-01 3.72266546e-02] Sparsity at: 0.0 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3176e-06 - accuracy: 1.0000 - val_loss: 0.1796 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.21130133e-01 -2.15345189e-01 3.73477787e-02] Sparsity at: 0.0 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6260e-06 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9771 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.24777043e-01 -2.16045722e-01 3.74784730e-02] Sparsity at: 0.0 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9992e-06 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.28432059e-01 -2.16751575e-01 3.75722237e-02] Sparsity at: 0.0 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4432e-06 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.32082605e-01 -2.17433602e-01 3.76952626e-02] Sparsity at: 0.0 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9461e-06 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.35769510e-01 -2.18151763e-01 3.77665460e-02] Sparsity at: 0.0 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5066e-06 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9771 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.39442170e-01 -2.18845710e-01 3.78184691e-02] Sparsity at: 0.0 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1093e-06 - accuracy: 1.0000 - val_loss: 0.1888 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.43112743e-01 -2.19543487e-01 3.78916115e-02] Sparsity at: 0.0 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7563e-06 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9770 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.46812046e-01 -2.20248595e-01 3.80217545e-02] Sparsity at: 0.0 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4470e-06 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.50449181e-01 -2.20953107e-01 3.80829386e-02] Sparsity at: 0.0 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1697e-06 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.54138470e-01 -2.21677557e-01 3.81468795e-02] Sparsity at: 0.0 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9263e-06 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.57808089e-01 -2.22348854e-01 3.81894335e-02] Sparsity at: 0.0 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7083e-06 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.61428177e-01 -2.23013178e-01 3.82183827e-02] Sparsity at: 0.0 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5145e-06 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.65046656e-01 -2.23710507e-01 3.82093713e-02] Sparsity at: 0.0 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.1786200046124975 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.27929918435745904 Thresholhold -0.3375190496444702 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.6444302967839235 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 1.3421e-06 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.68685102e-01 -2.24385381e-01 3.82571928e-02] Sparsity at: 0.0 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 1.1916e-06 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.72287667e-01 -2.25008085e-01 3.82519439e-02] Sparsity at: 0.0 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0551e-06 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.75840223e-01 -2.25661933e-01 3.82906348e-02] Sparsity at: 0.0 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3854e-07 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.79343963e-01 -2.26262301e-01 3.83147486e-02] Sparsity at: 0.0 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3317e-07 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.82865644e-01 -2.26891458e-01 3.83352414e-02] Sparsity at: 0.0 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4032e-07 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.86381841e-01 -2.27530435e-01 3.84084545e-02] Sparsity at: 0.0 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5754e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.89868712e-01 -2.28118062e-01 3.84691805e-02] Sparsity at: 0.0 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8472e-07 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.93344796e-01 -2.28746548e-01 3.84795889e-02] Sparsity at: 0.0 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2085e-07 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -7.96737015e-01 -2.29373425e-01 3.84733044e-02] Sparsity at: 0.0 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6456e-07 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.00145805e-01 -2.29992673e-01 3.85238752e-02] Sparsity at: 0.0 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1363e-07 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.03495705e-01 -2.30574384e-01 3.85139883e-02] Sparsity at: 0.0 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6902e-07 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.06790948e-01 -2.31189400e-01 3.84893939e-02] Sparsity at: 0.0 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2997e-07 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.10080588e-01 -2.31762692e-01 3.85242626e-02] Sparsity at: 0.0 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9508e-07 - accuracy: 1.0000 - val_loss: 0.2192 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.13330531e-01 -2.32369795e-01 3.85279506e-02] Sparsity at: 0.0 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6406e-07 - accuracy: 1.0000 - val_loss: 0.2206 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.16471279e-01 -2.32887834e-01 3.85581776e-02] Sparsity at: 0.0 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3699e-07 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.19550395e-01 -2.33468518e-01 3.85265723e-02] Sparsity at: 0.0 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1256e-07 - accuracy: 1.0000 - val_loss: 0.2233 - val_accuracy: 0.9765 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.22639108e-01 -2.34005883e-01 3.85492742e-02] Sparsity at: 0.0 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9134e-07 - accuracy: 1.0000 - val_loss: 0.2246 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.25674713e-01 -2.34564021e-01 3.85700241e-02] Sparsity at: 0.0 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7225e-07 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.28621328e-01 -2.35114768e-01 3.85724232e-02] Sparsity at: 0.0 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5548e-07 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.31497133e-01 -2.35651076e-01 3.86031568e-02] Sparsity at: 0.0 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4068e-07 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.34326982e-01 -2.36192048e-01 3.86074074e-02] Sparsity at: 0.0 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2730e-07 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.37076187e-01 -2.36699954e-01 3.86157744e-02] Sparsity at: 0.0 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1544e-07 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.39757979e-01 -2.37209916e-01 3.86303775e-02] Sparsity at: 0.0 Epoch 124/500 235/235 [==============================] - 2s 7ms/step - loss: 1.0505e-07 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.42364609e-01 -2.37701640e-01 3.86427678e-02] Sparsity at: 0.0 Epoch 125/500 235/235 [==============================] - 2s 7ms/step - loss: 9.6011e-08 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.44921470e-01 -2.38213971e-01 3.86302695e-02] Sparsity at: 0.0 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7577e-08 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.47416461e-01 -2.38721207e-01 3.86386663e-02] Sparsity at: 0.0 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0188e-08 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.49864542e-01 -2.39186317e-01 3.86222452e-02] Sparsity at: 0.0 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3634e-08 - accuracy: 1.0000 - val_loss: 0.2361 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.52162004e-01 -2.39656612e-01 3.86305675e-02] Sparsity at: 0.0 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7633e-08 - accuracy: 1.0000 - val_loss: 0.2369 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.54442239e-01 -2.40120471e-01 3.85817252e-02] Sparsity at: 0.0 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2412e-08 - accuracy: 1.0000 - val_loss: 0.2379 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.56645644e-01 -2.40544215e-01 3.85874137e-02] Sparsity at: 0.0 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7606e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.58747959e-01 -2.40945205e-01 3.85392494e-02] Sparsity at: 0.0 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3308e-08 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.60825837e-01 -2.41367340e-01 3.85020450e-02] Sparsity at: 0.0 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9432e-08 - accuracy: 1.0000 - val_loss: 0.2404 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.62779140e-01 -2.41723835e-01 3.84941548e-02] Sparsity at: 0.0 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6128e-08 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.64676595e-01 -2.42143527e-01 3.84639129e-02] Sparsity at: 0.0 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3001e-08 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.66502225e-01 -2.42497161e-01 3.84116545e-02] Sparsity at: 0.0 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0241e-08 - accuracy: 1.0000 - val_loss: 0.2426 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.68249118e-01 -2.42863253e-01 3.83799672e-02] Sparsity at: 0.0 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7654e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.69973421e-01 -2.43236393e-01 3.83756831e-02] Sparsity at: 0.0 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5357e-08 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.71574163e-01 -2.43592024e-01 3.83719280e-02] Sparsity at: 0.0 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3249e-08 - accuracy: 1.0000 - val_loss: 0.2447 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.73110533e-01 -2.43911177e-01 3.83442529e-02] Sparsity at: 0.0 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1348e-08 - accuracy: 1.0000 - val_loss: 0.2453 - val_accuracy: 0.9765 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.74634564e-01 -2.44285271e-01 3.83453779e-02] Sparsity at: 0.0 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9616e-08 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9765 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.76099169e-01 -2.44626507e-01 3.83300520e-02] Sparsity at: 0.0 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8008e-08 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.77531469e-01 -2.44956121e-01 3.83266397e-02] Sparsity at: 0.0 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6524e-08 - accuracy: 1.0000 - val_loss: 0.2470 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.78861487e-01 -2.45257944e-01 3.82626876e-02] Sparsity at: 0.0 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5177e-08 - accuracy: 1.0000 - val_loss: 0.2475 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.80145013e-01 -2.45569840e-01 3.82449813e-02] Sparsity at: 0.0 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4031e-08 - accuracy: 1.0000 - val_loss: 0.2480 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.81384790e-01 -2.45836064e-01 3.81923653e-02] Sparsity at: 0.0 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2858e-08 - accuracy: 1.0000 - val_loss: 0.2485 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.82537663e-01 -2.46114358e-01 3.81703116e-02] Sparsity at: 0.0 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1793e-08 - accuracy: 1.0000 - val_loss: 0.2490 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.83661330e-01 -2.46371999e-01 3.81385796e-02] Sparsity at: 0.0 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0792e-08 - accuracy: 1.0000 - val_loss: 0.2493 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.84719789e-01 -2.46628493e-01 3.81027535e-02] Sparsity at: 0.0 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9936e-08 - accuracy: 1.0000 - val_loss: 0.2498 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.85771871e-01 -2.46893898e-01 3.80806401e-02] Sparsity at: 0.0 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9095e-08 - accuracy: 1.0000 - val_loss: 0.2502 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.86786222e-01 -2.47147068e-01 3.80780213e-02] Sparsity at: 0.0 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.23349668905418675 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.3637283748039408 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.8807889858131297 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 47s 7ms/step - loss: 1.8344e-08 - accuracy: 1.0000 - val_loss: 0.2506 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.87766242e-01 -2.47375876e-01 3.80660407e-02] Sparsity at: 0.0 Epoch 152/500 235/235 [==============================] - 2s 7ms/step - loss: 1.7645e-08 - accuracy: 1.0000 - val_loss: 0.2511 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.88698697e-01 -2.47616738e-01 3.80187072e-02] Sparsity at: 0.0 Epoch 153/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6956e-08 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.89619648e-01 -2.47834235e-01 3.79927456e-02] Sparsity at: 0.0 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6365e-08 - accuracy: 1.0000 - val_loss: 0.2518 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.90494585e-01 -2.48063758e-01 3.79710905e-02] Sparsity at: 0.0 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5765e-08 - accuracy: 1.0000 - val_loss: 0.2522 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.91367853e-01 -2.48332337e-01 3.79396603e-02] Sparsity at: 0.0 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5142e-08 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.92202854e-01 -2.48555094e-01 3.78836878e-02] Sparsity at: 0.0 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4647e-08 - accuracy: 1.0000 - val_loss: 0.2529 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.93022776e-01 -2.48802245e-01 3.78507935e-02] Sparsity at: 0.0 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4212e-08 - accuracy: 1.0000 - val_loss: 0.2532 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.93809974e-01 -2.49039039e-01 3.77945639e-02] Sparsity at: 0.0 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3723e-08 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.94581199e-01 -2.49277666e-01 3.77601832e-02] Sparsity at: 0.0 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3314e-08 - accuracy: 1.0000 - val_loss: 0.2539 - val_accuracy: 0.9769 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.95328820e-01 -2.49494717e-01 3.77357081e-02] Sparsity at: 0.0 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2892e-08 - accuracy: 1.0000 - val_loss: 0.2541 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.96038830e-01 -2.49718621e-01 3.77015471e-02] Sparsity at: 0.0 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2547e-08 - accuracy: 1.0000 - val_loss: 0.2544 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.96735668e-01 -2.49904469e-01 3.77042815e-02] Sparsity at: 0.0 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2157e-08 - accuracy: 1.0000 - val_loss: 0.2547 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.97418916e-01 -2.50112623e-01 3.76848988e-02] Sparsity at: 0.0 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1772e-08 - accuracy: 1.0000 - val_loss: 0.2550 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.98071826e-01 -2.50316799e-01 3.76756787e-02] Sparsity at: 0.0 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1458e-08 - accuracy: 1.0000 - val_loss: 0.2553 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.98714602e-01 -2.50518382e-01 3.76418270e-02] Sparsity at: 0.0 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1188e-08 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.99335206e-01 -2.50679493e-01 3.75848711e-02] Sparsity at: 0.0 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0933e-08 - accuracy: 1.0000 - val_loss: 0.2558 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -8.99972856e-01 -2.50870347e-01 3.75116169e-02] Sparsity at: 0.0 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0620e-08 - accuracy: 1.0000 - val_loss: 0.2561 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.00587142e-01 -2.51068354e-01 3.74415889e-02] Sparsity at: 0.0 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0463e-08 - accuracy: 1.0000 - val_loss: 0.2563 - val_accuracy: 0.9768 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.01199877e-01 -2.51278073e-01 3.73902954e-02] Sparsity at: 0.0 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0184e-08 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.01780903e-01 -2.51471400e-01 3.73461396e-02] Sparsity at: 0.0 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9778e-09 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.02333498e-01 -2.51660168e-01 3.72826569e-02] Sparsity at: 0.0 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7255e-09 - accuracy: 1.0000 - val_loss: 0.2570 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.02888894e-01 -2.51870662e-01 3.72395217e-02] Sparsity at: 0.0 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5407e-09 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.03409481e-01 -2.52093822e-01 3.71811017e-02] Sparsity at: 0.0 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 9.2924e-09 - accuracy: 1.0000 - val_loss: 0.2575 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.03915823e-01 -2.52305686e-01 3.71289700e-02] Sparsity at: 0.0 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0897e-09 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.04441357e-01 -2.52519518e-01 3.70614640e-02] Sparsity at: 0.0 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 8.8771e-09 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.04922366e-01 -2.52677858e-01 3.70196067e-02] Sparsity at: 0.0 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 8.6844e-09 - accuracy: 1.0000 - val_loss: 0.2582 - val_accuracy: 0.9767 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.05424118e-01 -2.52865523e-01 3.69873196e-02] Sparsity at: 0.0 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 8.4956e-09 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.05921340e-01 -2.53046930e-01 3.69356796e-02] Sparsity at: 0.0 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2354e-09 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9765 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.06389952e-01 -2.53208697e-01 3.69024053e-02] Sparsity at: 0.0 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 8.1420e-09 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9766 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.06857669e-01 -2.53375292e-01 3.68436314e-02] Sparsity at: 0.0 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9691e-09 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9765 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.07332718e-01 -2.53554881e-01 3.67706679e-02] Sparsity at: 0.0 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7724e-09 - accuracy: 1.0000 - val_loss: 0.2593 - val_accuracy: 0.9765 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.07797277e-01 -2.53728628e-01 3.66903357e-02] Sparsity at: 0.0 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6512e-09 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.08234000e-01 -2.53887951e-01 3.66353840e-02] Sparsity at: 0.0 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4546e-09 - accuracy: 1.0000 - val_loss: 0.2596 - val_accuracy: 0.9764 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.08669353e-01 -2.54042059e-01 3.65685709e-02] Sparsity at: 0.0 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3493e-09 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.09079969e-01 -2.54154831e-01 3.65021378e-02] Sparsity at: 0.0 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1863e-09 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9764 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.09473240e-01 -2.54305243e-01 3.64245661e-02] Sparsity at: 0.0 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0492e-09 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9764 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.09852743e-01 -2.54437536e-01 3.63791436e-02] Sparsity at: 0.0 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 6.9420e-09 - accuracy: 1.0000 - val_loss: 0.2603 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.10262227e-01 -2.54620194e-01 3.62895429e-02] Sparsity at: 0.0 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8208e-09 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.10653830e-01 -2.54777402e-01 3.62312198e-02] Sparsity at: 0.0 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7552e-09 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9764 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.11043406e-01 -2.54913747e-01 3.61530446e-02] Sparsity at: 0.0 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6121e-09 - accuracy: 1.0000 - val_loss: 0.2608 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.11433756e-01 -2.55091101e-01 3.60769965e-02] Sparsity at: 0.0 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4870e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.11791921e-01 -2.55247861e-01 3.59976143e-02] Sparsity at: 0.0 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3837e-09 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.12145734e-01 -2.55401284e-01 3.59476320e-02] Sparsity at: 0.0 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3161e-09 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.12492037e-01 -2.55557775e-01 3.58512141e-02] Sparsity at: 0.0 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1949e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.12839890e-01 -2.55712658e-01 3.57962959e-02] Sparsity at: 0.0 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0856e-09 - accuracy: 1.0000 - val_loss: 0.2615 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.13161993e-01 -2.55864739e-01 3.57208997e-02] Sparsity at: 0.0 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0320e-09 - accuracy: 1.0000 - val_loss: 0.2617 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.13472593e-01 -2.56018966e-01 3.56645025e-02] Sparsity at: 0.0 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9187e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.13775861e-01 -2.56156266e-01 3.56170535e-02] Sparsity at: 0.0 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8591e-09 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.14086223e-01 -2.56304771e-01 3.55563872e-02] Sparsity at: 0.0 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7578e-09 - accuracy: 1.0000 - val_loss: 0.2621 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.14390981e-01 -2.56449252e-01 3.55049074e-02] Sparsity at: 0.0 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.29015515959103055 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.4289406053256215 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 1.0377554696051163 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 47s 7ms/step - loss: 5.6704e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.14671004e-01 -2.56605625e-01 3.54711302e-02] Sparsity at: 0.0 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 5.5869e-09 - accuracy: 1.0000 - val_loss: 0.2625 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.14955258e-01 -2.56763905e-01 3.54210809e-02] Sparsity at: 0.0 Epoch 203/500 235/235 [==============================] - 2s 7ms/step - loss: 5.5253e-09 - accuracy: 1.0000 - val_loss: 0.2626 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.15228128e-01 -2.56920069e-01 3.53657976e-02] Sparsity at: 0.0 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 5.4359e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.15491402e-01 -2.57055789e-01 3.53299007e-02] Sparsity at: 0.0 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3267e-09 - accuracy: 1.0000 - val_loss: 0.2628 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.15749371e-01 -2.57224709e-01 3.53000872e-02] Sparsity at: 0.0 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2551e-09 - accuracy: 1.0000 - val_loss: 0.2629 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.15998399e-01 -2.57363349e-01 3.52587216e-02] Sparsity at: 0.0 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1975e-09 - accuracy: 1.0000 - val_loss: 0.2631 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.16250169e-01 -2.57522374e-01 3.51937748e-02] Sparsity at: 0.0 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1081e-09 - accuracy: 1.0000 - val_loss: 0.2632 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.16496754e-01 -2.57682413e-01 3.51673961e-02] Sparsity at: 0.0 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0048e-09 - accuracy: 1.0000 - val_loss: 0.2634 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.16747510e-01 -2.57848680e-01 3.51176858e-02] Sparsity at: 0.0 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0108e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.16990101e-01 -2.58017093e-01 3.50633748e-02] Sparsity at: 0.0 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8757e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.17223513e-01 -2.58166850e-01 3.50154191e-02] Sparsity at: 0.0 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8657e-09 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.17463958e-01 -2.58360386e-01 3.49781290e-02] Sparsity at: 0.0 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8161e-09 - accuracy: 1.0000 - val_loss: 0.2638 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.17696536e-01 -2.58528113e-01 3.49233560e-02] Sparsity at: 0.0 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7664e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.17910755e-01 -2.58705020e-01 3.48703973e-02] Sparsity at: 0.0 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6233e-09 - accuracy: 1.0000 - val_loss: 0.2640 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.18130279e-01 -2.58882880e-01 3.48255001e-02] Sparsity at: 0.0 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6313e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.18362021e-01 -2.59070545e-01 3.47708426e-02] Sparsity at: 0.0 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5876e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.18572187e-01 -2.59242654e-01 3.47032584e-02] Sparsity at: 0.0 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4942e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.18786049e-01 -2.59433955e-01 3.46616656e-02] Sparsity at: 0.0 Epoch 219/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4545e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.18996572e-01 -2.59616017e-01 3.46292928e-02] Sparsity at: 0.0 Epoch 220/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4505e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.19211626e-01 -2.59799838e-01 3.45857665e-02] Sparsity at: 0.0 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3690e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.19399023e-01 -2.59985745e-01 3.45336236e-02] Sparsity at: 0.0 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3313e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.19601619e-01 -2.60172993e-01 3.44541743e-02] Sparsity at: 0.0 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2836e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.19802368e-01 -2.60383666e-01 3.44018005e-02] Sparsity at: 0.0 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2339e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.20005023e-01 -2.60588288e-01 3.43582146e-02] Sparsity at: 0.0 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2121e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.20192719e-01 -2.60786414e-01 3.43149863e-02] Sparsity at: 0.0 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1743e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.20379937e-01 -2.60970563e-01 3.42692733e-02] Sparsity at: 0.0 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1624e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.20589268e-01 -2.61175066e-01 3.42335515e-02] Sparsity at: 0.0 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0551e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.20776010e-01 -2.61332154e-01 3.41747925e-02] Sparsity at: 0.0 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0650e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.20961797e-01 -2.61539131e-01 3.41474488e-02] Sparsity at: 0.0 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9955e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.21143115e-01 -2.61735678e-01 3.40811573e-02] Sparsity at: 0.0 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9538e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9762 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.21331465e-01 -2.61911452e-01 3.40303034e-02] Sparsity at: 0.0 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9518e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9763 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.21515286e-01 -2.62104690e-01 3.39871645e-02] Sparsity at: 0.0 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9061e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.21713591e-01 -2.62274295e-01 3.39459814e-02] Sparsity at: 0.0 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8127e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.21899974e-01 -2.62462616e-01 3.39102410e-02] Sparsity at: 0.0 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8127e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.22066033e-01 -2.62648851e-01 3.38765308e-02] Sparsity at: 0.0 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7710e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.22255576e-01 -2.62851536e-01 3.38436365e-02] Sparsity at: 0.0 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7412e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.22424376e-01 -2.63003439e-01 3.38062197e-02] Sparsity at: 0.0 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7193e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.22604740e-01 -2.63177961e-01 3.37628834e-02] Sparsity at: 0.0 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6418e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.22775328e-01 -2.63387471e-01 3.37248072e-02] Sparsity at: 0.0 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6498e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9761 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.22941923e-01 -2.63570428e-01 3.36993597e-02] Sparsity at: 0.0 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6120e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.23106253e-01 -2.63746798e-01 3.36866267e-02] Sparsity at: 0.0 Epoch 242/500 235/235 [==============================] - 2s 7ms/step - loss: 3.6061e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.23285961e-01 -2.63909280e-01 3.36660109e-02] Sparsity at: 0.0 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5524e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.23451483e-01 -2.64106601e-01 3.36506292e-02] Sparsity at: 0.0 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5147e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.23603773e-01 -2.64274925e-01 3.36309187e-02] Sparsity at: 0.0 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4928e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.23749089e-01 -2.64473379e-01 3.36190313e-02] Sparsity at: 0.0 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4591e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.23919141e-01 -2.64666617e-01 3.36114913e-02] Sparsity at: 0.0 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4432e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24050450e-01 -2.64836729e-01 3.35773490e-02] Sparsity at: 0.0 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4114e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24211681e-01 -2.65033036e-01 3.35514136e-02] Sparsity at: 0.0 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3696e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24376667e-01 -2.65216380e-01 3.35313305e-02] Sparsity at: 0.0 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3736e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24528062e-01 -2.65405357e-01 3.35179195e-02] Sparsity at: 0.0 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.3503402787513785 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.4885466332617341 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 1.1681934371301281 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 3.3458e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24670041e-01 -2.65589476e-01 3.35141756e-02] Sparsity at: 0.0 Epoch 252/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3120e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24823880e-01 -2.65767068e-01 3.34855989e-02] Sparsity at: 0.0 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2763e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.24977839e-01 -2.65944660e-01 3.34512144e-02] Sparsity at: 0.0 Epoch 254/500 235/235 [==============================] - 2s 10ms/step - loss: 3.2226e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.25129414e-01 -2.66131401e-01 3.34366970e-02] Sparsity at: 0.0 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2524e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.25261557e-01 -2.66307682e-01 3.34103853e-02] Sparsity at: 0.0 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2167e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.25418079e-01 -2.66495347e-01 3.33547890e-02] Sparsity at: 0.0 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1273e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.25549388e-01 -2.66678303e-01 3.33302841e-02] Sparsity at: 0.0 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2187e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.25707161e-01 -2.66844451e-01 3.32996286e-02] Sparsity at: 0.0 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1392e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.25861955e-01 -2.67017305e-01 3.32547203e-02] Sparsity at: 0.0 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1710e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26003277e-01 -2.67192781e-01 3.32199000e-02] Sparsity at: 0.0 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0637e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26138043e-01 -2.67359793e-01 3.31771597e-02] Sparsity at: 0.0 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0816e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26255643e-01 -2.67516524e-01 3.31262834e-02] Sparsity at: 0.0 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0716e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26396668e-01 -2.67682999e-01 3.30832936e-02] Sparsity at: 0.0 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0418e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26541388e-01 -2.67832726e-01 3.30551378e-02] Sparsity at: 0.0 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0359e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26660895e-01 -2.68008381e-01 3.30281854e-02] Sparsity at: 0.0 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9882e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26809251e-01 -2.68177360e-01 3.29838581e-02] Sparsity at: 0.0 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0080e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.26921129e-01 -2.68325478e-01 3.29505801e-02] Sparsity at: 0.0 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9782e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27057743e-01 -2.68476188e-01 3.29090133e-02] Sparsity at: 0.0 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9624e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27197754e-01 -2.68637538e-01 3.28612365e-02] Sparsity at: 0.0 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9325e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27317798e-01 -2.68785268e-01 3.28159109e-02] Sparsity at: 0.0 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27437484e-01 -2.68936992e-01 3.27634439e-02] Sparsity at: 0.0 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27565992e-01 -2.69120842e-01 3.27210948e-02] Sparsity at: 0.0 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8690e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27707613e-01 -2.69284785e-01 3.26653272e-02] Sparsity at: 0.0 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8570e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27833319e-01 -2.69439399e-01 3.26155275e-02] Sparsity at: 0.0 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.27961588e-01 -2.69596279e-01 3.25694568e-02] Sparsity at: 0.0 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8491e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28091466e-01 -2.69764632e-01 3.25070955e-02] Sparsity at: 0.0 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8014e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28217173e-01 -2.69907743e-01 3.24538723e-02] Sparsity at: 0.0 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8451e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28338230e-01 -2.70053893e-01 3.24195884e-02] Sparsity at: 0.0 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7398e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28449988e-01 -2.70204633e-01 3.23816091e-02] Sparsity at: 0.0 Epoch 280/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28579807e-01 -2.70364761e-01 3.23268808e-02] Sparsity at: 0.0 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28684056e-01 -2.70496249e-01 3.22792418e-02] Sparsity at: 0.0 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7676e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28806484e-01 -2.70665675e-01 3.22445706e-02] Sparsity at: 0.0 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.28937733e-01 -2.70820051e-01 3.21911611e-02] Sparsity at: 0.0 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7676e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29046094e-01 -2.70958185e-01 3.21371406e-02] Sparsity at: 0.0 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6862e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29148197e-01 -2.71110624e-01 3.20917107e-02] Sparsity at: 0.0 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29271698e-01 -2.71261036e-01 3.20300087e-02] Sparsity at: 0.0 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6882e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29389060e-01 -2.71424919e-01 3.19824405e-02] Sparsity at: 0.0 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6902e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29484844e-01 -2.71579772e-01 3.19306254e-02] Sparsity at: 0.0 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29609537e-01 -2.71734685e-01 3.18796709e-02] Sparsity at: 0.0 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6902e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29724455e-01 -2.71894217e-01 3.18323933e-02] Sparsity at: 0.0 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6226e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29827809e-01 -2.72025287e-01 3.17711011e-02] Sparsity at: 0.0 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6941e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.29921627e-01 -2.72164434e-01 3.17262337e-02] Sparsity at: 0.0 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6584e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30057406e-01 -2.72311896e-01 3.16710137e-02] Sparsity at: 0.0 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5690e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30153012e-01 -2.72473395e-01 3.16102467e-02] Sparsity at: 0.0 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6107e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30259347e-01 -2.72633404e-01 3.15474123e-02] Sparsity at: 0.0 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30386722e-01 -2.72813857e-01 3.14740911e-02] Sparsity at: 0.0 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6186e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30484474e-01 -2.72970378e-01 3.14183012e-02] Sparsity at: 0.0 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6286e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30597365e-01 -2.73115486e-01 3.13339047e-02] Sparsity at: 0.0 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6067e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30727839e-01 -2.73280054e-01 3.12782265e-02] Sparsity at: 0.0 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5471e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30838823e-01 -2.73430228e-01 3.12118568e-02] Sparsity at: 0.0 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.42071751571042526 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.5465529025184779 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 1.32168250607576 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.30943191e-01 -2.73569554e-01 3.11563313e-02] Sparsity at: 0.0 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 2.5888e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31062758e-01 -2.73723036e-01 3.10694650e-02] Sparsity at: 0.0 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5610e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31175709e-01 -2.73886263e-01 3.09984479e-02] Sparsity at: 0.0 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5590e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31276441e-01 -2.74011970e-01 3.09351925e-02] Sparsity at: 0.0 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5372e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31389153e-01 -2.74166137e-01 3.08516752e-02] Sparsity at: 0.0 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5431e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31466818e-01 -2.74326801e-01 3.07944044e-02] Sparsity at: 0.0 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4776e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31572795e-01 -2.74478078e-01 3.06993369e-02] Sparsity at: 0.0 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5233e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31696892e-01 -2.74643004e-01 3.06342077e-02] Sparsity at: 0.0 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5153e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31790948e-01 -2.74796277e-01 3.05601545e-02] Sparsity at: 0.0 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5372e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.31907952e-01 -2.74943173e-01 3.04721408e-02] Sparsity at: 0.0 Epoch 311/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5570e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32008624e-01 -2.75076777e-01 3.03975996e-02] Sparsity at: 0.0 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5113e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32140231e-01 -2.75252402e-01 3.02885715e-02] Sparsity at: 0.0 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4935e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32246327e-01 -2.75404513e-01 3.02186869e-02] Sparsity at: 0.0 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4498e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32355762e-01 -2.75569916e-01 3.01008262e-02] Sparsity at: 0.0 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4875e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32476521e-01 -2.75717050e-01 3.00080311e-02] Sparsity at: 0.0 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4478e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32577670e-01 -2.75901645e-01 2.98965834e-02] Sparsity at: 0.0 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4001e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32669640e-01 -2.76065528e-01 2.98235286e-02] Sparsity at: 0.0 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4994e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32773888e-01 -2.76246756e-01 2.97185611e-02] Sparsity at: 0.0 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4557e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.32896674e-01 -2.76427358e-01 2.96193399e-02] Sparsity at: 0.0 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4418e-09 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33004141e-01 -2.76580632e-01 2.95249335e-02] Sparsity at: 0.0 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33114290e-01 -2.76739955e-01 2.94403601e-02] Sparsity at: 0.0 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4696e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33220446e-01 -2.76904047e-01 2.93307547e-02] Sparsity at: 0.0 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4617e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33327258e-01 -2.77078480e-01 2.92143244e-02] Sparsity at: 0.0 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.2675 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33432817e-01 -2.77232975e-01 2.91330144e-02] Sparsity at: 0.0 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3961e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33532476e-01 -2.77407616e-01 2.90375426e-02] Sparsity at: 0.0 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4438e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33655977e-01 -2.77554125e-01 2.89466288e-02] Sparsity at: 0.0 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4219e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33762908e-01 -2.77727157e-01 2.88372748e-02] Sparsity at: 0.0 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33875382e-01 -2.77905524e-01 2.87212264e-02] Sparsity at: 0.0 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3941e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.33978021e-01 -2.78071344e-01 2.86270641e-02] Sparsity at: 0.0 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3901e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34066951e-01 -2.78246313e-01 2.85144839e-02] Sparsity at: 0.0 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3743e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34182227e-01 -2.78432906e-01 2.84102540e-02] Sparsity at: 0.0 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4100e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34286058e-01 -2.78578788e-01 2.83053778e-02] Sparsity at: 0.0 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4021e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34391439e-01 -2.78786570e-01 2.82143913e-02] Sparsity at: 0.0 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3723e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34511960e-01 -2.78952211e-01 2.80919932e-02] Sparsity at: 0.0 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3862e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34615195e-01 -2.79102743e-01 2.79988945e-02] Sparsity at: 0.0 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4080e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34739351e-01 -2.79313087e-01 2.78884340e-02] Sparsity at: 0.0 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3206e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34842765e-01 -2.79505819e-01 2.77688317e-02] Sparsity at: 0.0 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4021e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.34953094e-01 -2.79678524e-01 2.76506562e-02] Sparsity at: 0.0 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35078919e-01 -2.79857576e-01 2.75072195e-02] Sparsity at: 0.0 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35183048e-01 -2.80020237e-01 2.74019502e-02] Sparsity at: 0.0 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3444e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35287058e-01 -2.80185521e-01 2.73041949e-02] Sparsity at: 0.0 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35399055e-01 -2.80346304e-01 2.71734055e-02] Sparsity at: 0.0 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35505390e-01 -2.80540049e-01 2.70613991e-02] Sparsity at: 0.0 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3762e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35604811e-01 -2.80752480e-01 2.69550905e-02] Sparsity at: 0.0 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3464e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35702622e-01 -2.80921936e-01 2.68561095e-02] Sparsity at: 0.0 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3286e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35807824e-01 -2.81096786e-01 2.67528109e-02] Sparsity at: 0.0 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.35918272e-01 -2.81267792e-01 2.66409628e-02] Sparsity at: 0.0 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36012983e-01 -2.81458706e-01 2.65191514e-02] Sparsity at: 0.0 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36115980e-01 -2.81638324e-01 2.63936780e-02] Sparsity at: 0.0 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3564e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36217189e-01 -2.81829774e-01 2.62851212e-02] Sparsity at: 0.0 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.4900760404196802 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.5907562922668816 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 1.418425701269939 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 41s 7ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36327040e-01 -2.82030821e-01 2.61641461e-02] Sparsity at: 0.0 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 2.3663e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36432660e-01 -2.82208681e-01 2.60592867e-02] Sparsity at: 0.0 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3385e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36538637e-01 -2.82387346e-01 2.59393919e-02] Sparsity at: 0.0 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3286e-09 - accuracy: 1.0000 - val_loss: 0.2672 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36642170e-01 -2.82590419e-01 2.58339141e-02] Sparsity at: 0.0 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36771572e-01 -2.82791495e-01 2.57414468e-02] Sparsity at: 0.0 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3484e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36893165e-01 -2.82959610e-01 2.56321710e-02] Sparsity at: 0.0 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.36969459e-01 -2.83139676e-01 2.55147386e-02] Sparsity at: 0.0 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3186e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37088847e-01 -2.83327878e-01 2.53991373e-02] Sparsity at: 0.0 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3107e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37186301e-01 -2.83514231e-01 2.52768602e-02] Sparsity at: 0.0 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37287807e-01 -2.83707619e-01 2.51426268e-02] Sparsity at: 0.0 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37413037e-01 -2.83901155e-01 2.50448585e-02] Sparsity at: 0.0 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37532008e-01 -2.84112006e-01 2.49501243e-02] Sparsity at: 0.0 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3146e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37625945e-01 -2.84277260e-01 2.48101316e-02] Sparsity at: 0.0 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3186e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37716067e-01 -2.84475356e-01 2.47137081e-02] Sparsity at: 0.0 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2729e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37812865e-01 -2.84677655e-01 2.45961174e-02] Sparsity at: 0.0 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.37907994e-01 -2.84860909e-01 2.44844966e-02] Sparsity at: 0.0 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3027e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38012064e-01 -2.85059512e-01 2.43404638e-02] Sparsity at: 0.0 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38129425e-01 -2.85266370e-01 2.42407601e-02] Sparsity at: 0.0 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38231409e-01 -2.85465866e-01 2.41196100e-02] Sparsity at: 0.0 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3047e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38334584e-01 -2.85666704e-01 2.40133647e-02] Sparsity at: 0.0 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2531e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38457489e-01 -2.85873383e-01 2.38948204e-02] Sparsity at: 0.0 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38540399e-01 -2.86066264e-01 2.37517226e-02] Sparsity at: 0.0 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2668 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38648343e-01 -2.86266267e-01 2.36288980e-02] Sparsity at: 0.0 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38743472e-01 -2.86470681e-01 2.35154796e-02] Sparsity at: 0.0 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38855410e-01 -2.86665767e-01 2.33872812e-02] Sparsity at: 0.0 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2908e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.38963354e-01 -2.86864430e-01 2.32622307e-02] Sparsity at: 0.0 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39056993e-01 -2.87077785e-01 2.31701396e-02] Sparsity at: 0.0 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3186e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39167202e-01 -2.87282735e-01 2.30443683e-02] Sparsity at: 0.0 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3107e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39286888e-01 -2.87479997e-01 2.29265317e-02] Sparsity at: 0.0 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39377248e-01 -2.87684679e-01 2.27985866e-02] Sparsity at: 0.0 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39487159e-01 -2.87876308e-01 2.26705745e-02] Sparsity at: 0.0 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39607322e-01 -2.88083732e-01 2.25424264e-02] Sparsity at: 0.0 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2451e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39715862e-01 -2.88298368e-01 2.24129353e-02] Sparsity at: 0.0 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39840376e-01 -2.88499236e-01 2.22983621e-02] Sparsity at: 0.0 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.39944029e-01 -2.88727343e-01 2.21916419e-02] Sparsity at: 0.0 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2511e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40082133e-01 -2.88931608e-01 2.20815316e-02] Sparsity at: 0.0 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2666 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40145791e-01 -2.89137781e-01 2.19360068e-02] Sparsity at: 0.0 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40255702e-01 -2.89337546e-01 2.18214430e-02] Sparsity at: 0.0 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2451e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40351248e-01 -2.89548606e-01 2.16988903e-02] Sparsity at: 0.0 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40459907e-01 -2.89742559e-01 2.15865131e-02] Sparsity at: 0.0 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2590e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40573752e-01 -2.89949954e-01 2.14612093e-02] Sparsity at: 0.0 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40666318e-01 -2.90146559e-01 2.13294495e-02] Sparsity at: 0.0 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40774620e-01 -2.90373117e-01 2.12002620e-02] Sparsity at: 0.0 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2173e-09 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.40876842e-01 -2.90589243e-01 2.10563540e-02] Sparsity at: 0.0 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3107e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41013992e-01 -2.90786594e-01 2.09450405e-02] Sparsity at: 0.0 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41123128e-01 -2.91018546e-01 2.08151639e-02] Sparsity at: 0.0 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41233158e-01 -2.91220725e-01 2.06805244e-02] Sparsity at: 0.0 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41353083e-01 -2.91427165e-01 2.05487702e-02] Sparsity at: 0.0 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2372e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41453218e-01 -2.91651875e-01 2.04149354e-02] Sparsity at: 0.0 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2153e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41561043e-01 -2.91878700e-01 2.02855244e-02] Sparsity at: 0.0 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.5270422661683085 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6258507425320374 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 1.4723980643949375 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41673040e-01 -2.92106688e-01 2.01588050e-02] Sparsity at: 0.0 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 2.2809e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41771388e-01 -2.92283744e-01 2.00307872e-02] Sparsity at: 0.0 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41868007e-01 -2.92506546e-01 1.98941212e-02] Sparsity at: 0.0 Epoch 404/500 235/235 [==============================] - 2s 10ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.41951096e-01 -2.92689353e-01 1.97745040e-02] Sparsity at: 0.0 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42058742e-01 -2.92901099e-01 1.96447633e-02] Sparsity at: 0.0 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42200899e-01 -2.93141544e-01 1.95166357e-02] Sparsity at: 0.0 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2511e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42302287e-01 -2.93382436e-01 1.93952695e-02] Sparsity at: 0.0 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42418098e-01 -2.93599695e-01 1.92537494e-02] Sparsity at: 0.0 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2531e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42534983e-01 -2.93819159e-01 1.91261563e-02] Sparsity at: 0.0 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42642570e-01 -2.94038624e-01 1.89545490e-02] Sparsity at: 0.0 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42759395e-01 -2.94254690e-01 1.88633576e-02] Sparsity at: 0.0 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42874789e-01 -2.94487983e-01 1.87181011e-02] Sparsity at: 0.0 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.42967713e-01 -2.94697315e-01 1.85956415e-02] Sparsity at: 0.0 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43077803e-01 -2.94925898e-01 1.84855331e-02] Sparsity at: 0.0 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43172932e-01 -2.95119882e-01 1.83674805e-02] Sparsity at: 0.0 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2391e-09 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43295598e-01 -2.95320600e-01 1.82380956e-02] Sparsity at: 0.0 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43399549e-01 -2.95548528e-01 1.80909466e-02] Sparsity at: 0.0 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2570e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43524778e-01 -2.95752555e-01 1.79642979e-02] Sparsity at: 0.0 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43646610e-01 -2.95953482e-01 1.78360883e-02] Sparsity at: 0.0 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2471e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43744957e-01 -2.96161503e-01 1.77066568e-02] Sparsity at: 0.0 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43863928e-01 -2.96393901e-01 1.75607577e-02] Sparsity at: 0.0 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.43982422e-01 -2.96626687e-01 1.74317993e-02] Sparsity at: 0.0 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44111645e-01 -2.96865016e-01 1.72851663e-02] Sparsity at: 0.0 Epoch 424/500 235/235 [==============================] - 2s 7ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44230378e-01 -2.97109604e-01 1.71498340e-02] Sparsity at: 0.0 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2272e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44325149e-01 -2.97351837e-01 1.70032158e-02] Sparsity at: 0.0 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2729e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44439471e-01 -2.97595769e-01 1.68572851e-02] Sparsity at: 0.0 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2908e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44519639e-01 -2.97793776e-01 1.67543702e-02] Sparsity at: 0.0 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2590e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44645226e-01 -2.98026234e-01 1.66223962e-02] Sparsity at: 0.0 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44774270e-01 -2.98236072e-01 1.64820235e-02] Sparsity at: 0.0 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2630e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44893658e-01 -2.98430324e-01 1.63302589e-02] Sparsity at: 0.0 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2656 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.44996059e-01 -2.98661172e-01 1.61828361e-02] Sparsity at: 0.0 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2372e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45112824e-01 -2.98893899e-01 1.60535537e-02] Sparsity at: 0.0 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2829e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45244312e-01 -2.99134403e-01 1.59059241e-02] Sparsity at: 0.0 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45371389e-01 -2.99344569e-01 1.57548469e-02] Sparsity at: 0.0 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2074e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45480764e-01 -2.99553931e-01 1.56060671e-02] Sparsity at: 0.0 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45607901e-01 -2.99774408e-01 1.54759260e-02] Sparsity at: 0.0 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45732355e-01 -2.99996227e-01 1.53520014e-02] Sparsity at: 0.0 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2709e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45842922e-01 -3.00202817e-01 1.52285136e-02] Sparsity at: 0.0 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1954e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.45941389e-01 -3.00459027e-01 1.50872329e-02] Sparsity at: 0.0 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46087122e-01 -3.00675988e-01 1.49583369e-02] Sparsity at: 0.0 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2153e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46190476e-01 -3.00919831e-01 1.47944428e-02] Sparsity at: 0.0 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46346939e-01 -3.01154852e-01 1.46322725e-02] Sparsity at: 0.0 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9756 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46472347e-01 -3.01385343e-01 1.45136509e-02] Sparsity at: 0.0 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2272e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46603596e-01 -3.01629126e-01 1.43880397e-02] Sparsity at: 0.0 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46730793e-01 -3.01870674e-01 1.42418072e-02] Sparsity at: 0.0 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2431e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.46864784e-01 -3.02109033e-01 1.40872803e-02] Sparsity at: 0.0 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47039127e-01 -3.02335769e-01 1.39256781e-02] Sparsity at: 0.0 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2491e-09 - accuracy: 1.0000 - val_loss: 0.2653 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47155654e-01 -3.02560091e-01 1.37946298e-02] Sparsity at: 0.0 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47258055e-01 -3.02786618e-01 1.36603639e-02] Sparsity at: 0.0 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2133e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47397172e-01 -3.03030580e-01 1.35149136e-02] Sparsity at: 0.0 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47530627e-01 -3.03289115e-01 1.33642917e-02] Sparsity at: 0.0 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47655916e-01 -3.03539366e-01 1.31863924e-02] Sparsity at: 0.0 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2272e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47761178e-01 -3.03784430e-01 1.30497301e-02] Sparsity at: 0.0 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.47892785e-01 -3.04005861e-01 1.28936414e-02] Sparsity at: 0.0 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9755 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48020637e-01 -3.04228902e-01 1.27583854e-02] Sparsity at: 0.0 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2948e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48132813e-01 -3.04452837e-01 1.26176411e-02] Sparsity at: 0.0 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48262155e-01 -3.04704547e-01 1.24695813e-02] Sparsity at: 0.0 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48395550e-01 -3.04945230e-01 1.23415012e-02] Sparsity at: 0.0 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2292e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48519647e-01 -3.05169433e-01 1.21671464e-02] Sparsity at: 0.0 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48657274e-01 -3.05419743e-01 1.20440805e-02] Sparsity at: 0.0 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2451e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48762536e-01 -3.05681705e-01 1.19062131e-02] Sparsity at: 0.0 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2968e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.48866189e-01 -3.05908084e-01 1.17764063e-02] Sparsity at: 0.0 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1855e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49006617e-01 -3.06141645e-01 1.16361463e-02] Sparsity at: 0.0 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3007e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49138820e-01 -3.06387067e-01 1.14782676e-02] Sparsity at: 0.0 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2809e-09 - accuracy: 1.0000 - val_loss: 0.2650 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49247301e-01 -3.06611717e-01 1.13269957e-02] Sparsity at: 0.0 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49358165e-01 -3.06864649e-01 1.11741573e-02] Sparsity at: 0.0 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49463964e-01 -3.07107210e-01 1.10130813e-02] Sparsity at: 0.0 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2968e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49611545e-01 -3.07388693e-01 1.08463997e-02] Sparsity at: 0.0 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49755192e-01 -3.07629436e-01 1.06958346e-02] Sparsity at: 0.0 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.49876368e-01 -3.07882637e-01 1.05686951e-02] Sparsity at: 0.0 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50014651e-01 -3.08118820e-01 1.04321325e-02] Sparsity at: 0.0 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50157940e-01 -3.08348238e-01 1.02946730e-02] Sparsity at: 0.0 Epoch 473/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2332e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50274169e-01 -3.08610320e-01 1.01395873e-02] Sparsity at: 0.0 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2590e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50396001e-01 -3.08885694e-01 9.96050797e-03] Sparsity at: 0.0 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2312e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50530827e-01 -3.09125006e-01 9.81031545e-03] Sparsity at: 0.0 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2789e-09 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50670063e-01 -3.09412718e-01 9.66217462e-03] Sparsity at: 0.0 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50807810e-01 -3.09622765e-01 9.50858835e-03] Sparsity at: 0.0 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2233e-09 - accuracy: 1.0000 - val_loss: 0.2647 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.50927675e-01 -3.09862733e-01 9.35985427e-03] Sparsity at: 0.0 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51053977e-01 -3.10111880e-01 9.20893345e-03] Sparsity at: 0.0 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51170206e-01 -3.10357779e-01 9.03270021e-03] Sparsity at: 0.0 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51299012e-01 -3.10600460e-01 8.87004752e-03] Sparsity at: 0.0 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3206e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51428831e-01 -3.10844630e-01 8.71321093e-03] Sparsity at: 0.0 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51568246e-01 -3.11082661e-01 8.53290688e-03] Sparsity at: 0.0 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2491e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51724827e-01 -3.11321080e-01 8.37356132e-03] Sparsity at: 0.0 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2848e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51851189e-01 -3.11582446e-01 8.21503717e-03] Sparsity at: 0.0 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.51999962e-01 -3.11859518e-01 8.07784311e-03] Sparsity at: 0.0 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52133179e-01 -3.12110484e-01 7.90818781e-03] Sparsity at: 0.0 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52255011e-01 -3.12348932e-01 7.74430623e-03] Sparsity at: 0.0 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52368677e-01 -3.12595606e-01 7.58849783e-03] Sparsity at: 0.0 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2670e-09 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52495992e-01 -3.12877029e-01 7.42098549e-03] Sparsity at: 0.0 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52622890e-01 -3.13126296e-01 7.27442512e-03] Sparsity at: 0.0 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9759 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52781677e-01 -3.13397199e-01 7.10926624e-03] Sparsity at: 0.0 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2650e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.52908695e-01 -3.13661605e-01 6.95057306e-03] Sparsity at: 0.0 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2570e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53061283e-01 -3.13935310e-01 6.78941188e-03] Sparsity at: 0.0 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53183889e-01 -3.14222246e-01 6.63644727e-03] Sparsity at: 0.0 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2769e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53332067e-01 -3.14477414e-01 6.49842480e-03] Sparsity at: 0.0 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53476608e-01 -3.14737469e-01 6.34068158e-03] Sparsity at: 0.0 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9760 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53594744e-01 -3.15025270e-01 6.16831565e-03] Sparsity at: 0.0 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2689e-09 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9757 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53725040e-01 -3.15291137e-01 6.01910008e-03] Sparsity at: 0.0 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2928e-09 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9758 [-6.16294481e-02 1.14150345e-02 -6.17124140e-04 ... -9.53865707e-01 -3.15545589e-01 5.87014109e-03] Sparsity at: 0.0 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 0.1403 - accuracy: 0.9782 - val_loss: 0.2178 - val_accuracy: 0.9534 [-4.4356627e-34 2.9253646e-34 8.6999785e-07 ... 2.8114814e-02 3.4809522e-02 -1.7609550e-02] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9790 - val_loss: 0.2018 - val_accuracy: 0.9599 [-4.4356627e-34 2.9253646e-34 2.0628352e-06 ... 2.2707820e-02 3.8300749e-02 -1.8742241e-02] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9796 - val_loss: 0.2181 - val_accuracy: 0.9535 [-4.4356627e-34 2.9253646e-34 -2.3647873e-07 ... 2.4266619e-02 3.1056507e-02 -4.6939841e-03] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.2031 - val_accuracy: 0.9609 [-4.4356627e-34 2.9253646e-34 -1.0235022e-04 ... 2.3896152e-02 3.2281827e-02 -2.7752947e-03] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9788 - val_loss: 0.1884 - val_accuracy: 0.9639 [-4.4356627e-34 2.9253646e-34 2.4245323e-06 ... 2.5337495e-02 3.2146811e-02 -8.4898332e-03] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9794 - val_loss: 0.2097 - val_accuracy: 0.9579 [-4.4356627e-34 2.9253646e-34 1.6815411e-09 ... 2.2105722e-02 3.3353094e-02 -1.1573269e-02] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9792 - val_loss: 0.1940 - val_accuracy: 0.9621 [-4.4356627e-34 2.9253646e-34 3.2540449e-08 ... 2.7064912e-02 3.3438757e-02 -1.3257946e-02] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9798 - val_loss: 0.1836 - val_accuracy: 0.9661 [-4.4356627e-34 2.9253646e-34 -2.3241686e-05 ... 2.8341416e-02 3.2972734e-02 -8.8457661e-03] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9780 - val_loss: 0.1793 - val_accuracy: 0.9674 [-4.4356627e-34 2.9253646e-34 3.3285624e-11 ... 2.7563902e-02 4.0071361e-02 -3.4530694e-03] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1405 - accuracy: 0.9791 - val_loss: 0.1968 - val_accuracy: 0.9605 [-4.4356627e-34 2.9253646e-34 -1.4974354e-07 ... 2.4484839e-02 3.2037817e-02 -4.9615912e-03] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9795 - val_loss: 0.1985 - val_accuracy: 0.9632 [-4.4356627e-34 2.9253646e-34 -8.4715657e-10 ... 2.4635160e-02 3.2500427e-02 -8.2618659e-03] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9777 - val_loss: 0.2005 - val_accuracy: 0.9599 [-4.4356627e-34 2.9253646e-34 -3.1456713e-09 ... 2.2555325e-02 2.6626091e-02 -1.0594313e-02] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.1911 - val_accuracy: 0.9641 [-4.4356627e-34 2.9253646e-34 1.7390824e-04 ... 2.1199387e-02 3.4701910e-02 -7.0612682e-03] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9803 - val_loss: 0.2010 - val_accuracy: 0.9592 [-4.4356627e-34 2.9253646e-34 5.9900507e-10 ... 1.8665988e-02 3.5157956e-02 -1.2766198e-02] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9791 - val_loss: 0.1925 - val_accuracy: 0.9646 [-4.4356627e-34 2.9253646e-34 1.0837175e-05 ... 1.1141744e-02 3.9770748e-02 -2.7370539e-03] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9795 - val_loss: 0.2512 - val_accuracy: 0.9449 [-4.4356627e-34 2.9253646e-34 -9.1011768e-11 ... 1.6691022e-02 3.9457034e-02 -8.6125014e-03] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9781 - val_loss: 0.2324 - val_accuracy: 0.9502 [-4.4356627e-34 2.9253646e-34 7.2142129e-06 ... 2.1801472e-02 4.2004917e-02 -2.6586866e-03] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9791 - val_loss: 0.2117 - val_accuracy: 0.9582 [-4.4356627e-34 2.9253646e-34 3.2211209e-11 ... 1.6260084e-02 3.8023904e-02 -3.6363578e-03] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9794 - val_loss: 0.2253 - val_accuracy: 0.9522 [-4.4356627e-34 2.9253646e-34 2.6021853e-06 ... 1.5645349e-02 4.0428534e-02 -2.6659528e-03] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9793 - val_loss: 0.2315 - val_accuracy: 0.9531 [-4.4356627e-34 2.9253646e-34 -3.8888073e-11 ... 1.4256310e-02 3.9871428e-02 -4.1871164e-03] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9791 - val_loss: 0.1968 - val_accuracy: 0.9626 [-4.43566273e-34 2.92536463e-34 1.22077945e-05 ... 6.72502303e-03 3.83764654e-02 -2.32384587e-03] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9795 - val_loss: 0.2128 - val_accuracy: 0.9580 [-4.4356627e-34 2.9253646e-34 4.8230397e-10 ... 3.7452166e-03 3.6951277e-02 -8.5197401e-04] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9791 - val_loss: 0.1852 - val_accuracy: 0.9660 [-4.4356627e-34 2.9253646e-34 1.1352503e-07 ... 9.3110343e-03 3.6662113e-02 -3.5217057e-03] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9795 - val_loss: 0.1988 - val_accuracy: 0.9613 [-4.4356627e-34 2.9253646e-34 7.2233508e-10 ... 1.2345173e-02 3.8336936e-02 -3.7225680e-03] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9796 - val_loss: 0.2083 - val_accuracy: 0.9611 [-4.4356627e-34 2.9253646e-34 -1.5901655e-11 ... 1.0363245e-02 4.0690053e-02 -6.2533193e-03] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9789 - val_loss: 0.1918 - val_accuracy: 0.9643 [-4.4356627e-34 2.9253646e-34 -1.6603803e-08 ... 1.2871692e-02 4.5399975e-02 3.3595660e-03] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1336 - accuracy: 0.9803 - val_loss: 0.2089 - val_accuracy: 0.9603 [-4.4356627e-34 2.9253646e-34 1.1611074e-14 ... 1.2908577e-02 3.7387349e-02 4.6803150e-03] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1420 - accuracy: 0.9772 - val_loss: 0.1909 - val_accuracy: 0.9652 [-4.4356627e-34 2.9253646e-34 -1.2675883e-07 ... 1.5698574e-02 4.3851707e-02 5.9758159e-03] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9800 - val_loss: 0.2025 - val_accuracy: 0.9603 [-4.4356627e-34 2.9253646e-34 -1.6535081e-13 ... 2.0728810e-02 4.0740233e-02 5.8862166e-03] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9779 - val_loss: 0.1802 - val_accuracy: 0.9665 [-4.4356627e-34 2.9253646e-34 -2.1176586e-06 ... 1.6025430e-02 4.7430277e-02 4.8945788e-03] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9799 - val_loss: 0.2029 - val_accuracy: 0.9588 [-4.4356627e-34 2.9253646e-34 1.2703524e-11 ... 2.0578504e-02 4.0411767e-02 -6.6473940e-03] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1366 - accuracy: 0.9791 - val_loss: 0.2001 - val_accuracy: 0.9624 [-4.4356627e-34 2.9253646e-34 -4.9994997e-05 ... 1.3571465e-02 4.1463271e-02 -9.9057890e-03] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.2014 - val_accuracy: 0.9621 [-4.4356627e-34 2.9253646e-34 -2.7280800e-10 ... 1.8462239e-02 3.7958566e-02 -9.2464909e-03] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9801 - val_loss: 0.2072 - val_accuracy: 0.9556 [-4.4356627e-34 2.9253646e-34 2.1029967e-13 ... 1.8352199e-02 3.6578070e-02 -7.6208659e-03] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9785 - val_loss: 0.2332 - val_accuracy: 0.9513 [-4.4356627e-34 2.9253646e-34 -2.0386757e-08 ... 2.3226952e-02 3.5327170e-02 -7.9526985e-03] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9792 - val_loss: 0.2093 - val_accuracy: 0.9586 [-4.4356627e-34 2.9253646e-34 -2.6826092e-13 ... 1.5596361e-02 4.2395931e-02 -6.4118979e-03] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9799 - val_loss: 0.1857 - val_accuracy: 0.9655 [-4.4356627e-34 2.9253646e-34 2.0881648e-06 ... 1.4806369e-02 4.3227941e-02 2.1685108e-03] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9785 - val_loss: 0.2009 - val_accuracy: 0.9621 [-4.4356627e-34 2.9253646e-34 -1.7623753e-11 ... 5.8548148e-03 3.5814762e-02 -1.3462368e-03] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1417 - accuracy: 0.9780 - val_loss: 0.1911 - val_accuracy: 0.9656 [-4.4356627e-34 2.9253646e-34 1.7305936e-11 ... 1.0413783e-02 3.4120601e-02 9.4516268e-03] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1385 - accuracy: 0.9794 - val_loss: 0.1992 - val_accuracy: 0.9608 [-4.4356627e-34 2.9253646e-34 1.2640411e-08 ... 1.3274160e-02 3.4423355e-02 -3.4465648e-03] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9784 - val_loss: 0.2127 - val_accuracy: 0.9590 [-4.4356627e-34 2.9253646e-34 -9.4728119e-14 ... 1.2558985e-02 4.1123256e-02 2.3248577e-03] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1388 - accuracy: 0.9791 - val_loss: 0.1810 - val_accuracy: 0.9692 [-4.4356627e-34 2.9253646e-34 2.0730442e-07 ... 1.5148821e-02 4.1342162e-02 -6.5302886e-03] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9792 - val_loss: 0.2088 - val_accuracy: 0.9620 [-4.4356627e-34 2.9253646e-34 2.2067350e-12 ... 1.8062945e-02 4.1603591e-02 -5.3234766e-03] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9789 - val_loss: 0.1822 - val_accuracy: 0.9654 [-4.4356627e-34 2.9253646e-34 4.1821153e-05 ... 2.1196369e-02 3.3439983e-02 -5.2966471e-03] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1394 - accuracy: 0.9777 - val_loss: 0.2045 - val_accuracy: 0.9628 [-4.4356627e-34 2.9253646e-34 1.3934112e-10 ... 1.3416549e-02 3.7804525e-02 6.3323870e-04] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9799 - val_loss: 0.1983 - val_accuracy: 0.9612 [-4.4356627e-34 2.9253646e-34 1.8034328e-07 ... 1.7215360e-02 3.8162611e-02 -3.5554436e-03] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9798 - val_loss: 0.1847 - val_accuracy: 0.9644 [-4.4356627e-34 2.9253646e-34 -2.8809382e-09 ... 2.2185195e-02 3.2359198e-02 -1.4449451e-02] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.1859 - val_accuracy: 0.9656 [-4.4356627e-34 2.9253646e-34 6.0652590e-13 ... 1.9747971e-02 4.4964917e-02 -6.0511660e-03] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1380 - accuracy: 0.9791 - val_loss: 0.2077 - val_accuracy: 0.9611 [-4.4356627e-34 2.9253646e-34 3.5328782e-08 ... 2.0591386e-02 3.8017310e-02 -2.8537875e-03] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9793 - val_loss: 0.1845 - val_accuracy: 0.9654 [-4.4356627e-34 2.9253646e-34 2.0064465e-13 ... 1.8177766e-02 3.7906237e-02 -8.3224906e-05] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9798 - val_loss: 0.1738 - val_accuracy: 0.9686 [-4.4356627e-34 0.0000000e+00 7.5561047e-08 ... 0.0000000e+00 3.8337916e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9806 - val_loss: 0.1939 - val_accuracy: 0.9642 [-4.4356627e-34 0.0000000e+00 7.4753129e-12 ... -0.0000000e+00 4.0081557e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9793 - val_loss: 0.2033 - val_accuracy: 0.9584 [-4.4356627e-34 0.0000000e+00 4.0164996e-05 ... 0.0000000e+00 3.8575668e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1352 - accuracy: 0.9801 - val_loss: 0.1854 - val_accuracy: 0.9662 [-4.4356627e-34 0.0000000e+00 1.7040164e-10 ... -0.0000000e+00 4.1046672e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9805 - val_loss: 0.1780 - val_accuracy: 0.9694 [-4.4356627e-34 0.0000000e+00 -8.9459604e-07 ... 0.0000000e+00 4.2443641e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9790 - val_loss: 0.1859 - val_accuracy: 0.9655 [-4.4356627e-34 0.0000000e+00 -2.1861919e-09 ... 0.0000000e+00 4.6921499e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.2140 - val_accuracy: 0.9577 [-4.4356627e-34 0.0000000e+00 -1.0070818e-11 ... 0.0000000e+00 4.6017870e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.1978 - val_accuracy: 0.9620 [-4.4356627e-34 0.0000000e+00 -2.0438502e-08 ... 0.0000000e+00 4.5299400e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9785 - val_loss: 0.2135 - val_accuracy: 0.9593 [-4.4356627e-34 0.0000000e+00 -2.4871468e-13 ... -0.0000000e+00 4.6698000e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9801 - val_loss: 0.1977 - val_accuracy: 0.9600 [-4.4356627e-34 0.0000000e+00 1.7329231e-07 ... 0.0000000e+00 4.6961948e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9793 - val_loss: 0.1726 - val_accuracy: 0.9687 [-4.4356627e-34 0.0000000e+00 -1.0099716e-12 ... 0.0000000e+00 4.4800472e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9791 - val_loss: 0.2034 - val_accuracy: 0.9615 [-4.4356627e-34 0.0000000e+00 -7.9016473e-07 ... -0.0000000e+00 4.6416786e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2069 - val_accuracy: 0.9575 [-4.4356627e-34 0.0000000e+00 4.2106444e-12 ... 0.0000000e+00 4.8943881e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1390 - accuracy: 0.9787 - val_loss: 0.1848 - val_accuracy: 0.9658 [-4.4356627e-34 0.0000000e+00 -3.9353017e-06 ... 0.0000000e+00 4.9843300e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.2101 - val_accuracy: 0.9592 [-4.4356627e-34 0.0000000e+00 2.9262030e-11 ... 0.0000000e+00 4.7083952e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9799 - val_loss: 0.1976 - val_accuracy: 0.9631 [-4.4356627e-34 0.0000000e+00 -8.1391161e-05 ... 0.0000000e+00 4.9882617e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9792 - val_loss: 0.2004 - val_accuracy: 0.9617 [-4.4356627e-34 0.0000000e+00 5.1441817e-10 ... -0.0000000e+00 4.8349380e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9793 - val_loss: 0.1986 - val_accuracy: 0.9629 [-4.4356627e-34 0.0000000e+00 -7.1694979e-09 ... 0.0000000e+00 5.1423583e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.1996 - val_accuracy: 0.9610 [-4.4356627e-34 0.0000000e+00 -7.1149597e-10 ... 0.0000000e+00 4.5033433e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.1829 - val_accuracy: 0.9655 [-4.4356627e-34 0.0000000e+00 -1.0871173e-13 ... 0.0000000e+00 4.5161065e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9787 - val_loss: 0.1884 - val_accuracy: 0.9642 [-4.4356627e-34 0.0000000e+00 -6.9089694e-08 ... 0.0000000e+00 4.3527700e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9789 - val_loss: 0.1795 - val_accuracy: 0.9673 [-4.4356627e-34 0.0000000e+00 -4.5490548e-13 ... 0.0000000e+00 3.9080158e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9791 - val_loss: 0.1872 - val_accuracy: 0.9638 [-4.4356627e-34 0.0000000e+00 -1.7250043e-06 ... 0.0000000e+00 3.5831150e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9788 - val_loss: 0.1904 - val_accuracy: 0.9661 [-4.4356627e-34 0.0000000e+00 9.6639094e-12 ... 0.0000000e+00 3.5006415e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9785 - val_loss: 0.1984 - val_accuracy: 0.9609 [-4.4356627e-34 0.0000000e+00 8.5559222e-06 ... 0.0000000e+00 3.7686534e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9798 - val_loss: 0.2012 - val_accuracy: 0.9601 [-4.4356627e-34 0.0000000e+00 -1.7441773e-10 ... 0.0000000e+00 4.5726351e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1352 - accuracy: 0.9789 - val_loss: 0.1930 - val_accuracy: 0.9623 [-4.4356627e-34 0.0000000e+00 1.4823360e-08 ... 0.0000000e+00 4.3249365e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9789 - val_loss: 0.2054 - val_accuracy: 0.9614 [-4.4356627e-34 0.0000000e+00 4.3116963e-09 ... 0.0000000e+00 5.0600823e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9795 - val_loss: 0.1828 - val_accuracy: 0.9677 [-4.4356627e-34 0.0000000e+00 -8.6048454e-13 ... 0.0000000e+00 3.9024297e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.1780 - val_accuracy: 0.9666 [-4.4356627e-34 0.0000000e+00 -8.3123908e-10 ... -0.0000000e+00 3.9982121e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9803 - val_loss: 0.1833 - val_accuracy: 0.9639 [-4.4356627e-34 0.0000000e+00 -3.3148874e-13 ... 0.0000000e+00 4.0137947e-02 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1354 - accuracy: 0.9796 - val_loss: 0.1972 - val_accuracy: 0.9598 [-4.4356627e-34 0.0000000e+00 -2.8858494e-07 ... 0.0000000e+00 3.7828699e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.1937 - val_accuracy: 0.9625 [-4.4356627e-34 0.0000000e+00 -2.0175914e-12 ... -0.0000000e+00 3.3317301e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.1951 - val_accuracy: 0.9650 [-4.4356627e-34 0.0000000e+00 1.6610065e-06 ... -0.0000000e+00 2.8387634e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9654 [-4.4356627e-34 0.0000000e+00 -6.1096527e-12 ... 0.0000000e+00 2.9365592e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9785 - val_loss: 0.2060 - val_accuracy: 0.9577 [-4.4356627e-34 0.0000000e+00 -3.4340264e-06 ... 0.0000000e+00 3.0280661e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.1979 - val_accuracy: 0.9624 [-4.4356627e-34 0.0000000e+00 8.6926702e-11 ... 0.0000000e+00 3.0917553e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.1758 - val_accuracy: 0.9703 [-4.4356627e-34 0.0000000e+00 -7.4176787e-05 ... -0.0000000e+00 2.3183962e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9799 - val_loss: 0.1857 - val_accuracy: 0.9653 [-4.4356627e-34 0.0000000e+00 -4.8237903e-10 ... 0.0000000e+00 2.7763518e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1338 - accuracy: 0.9797 - val_loss: 0.1771 - val_accuracy: 0.9694 [-4.4356627e-34 0.0000000e+00 -4.9796534e-10 ... 0.0000000e+00 2.7011510e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1349 - accuracy: 0.9796 - val_loss: 0.2320 - val_accuracy: 0.9546 [-4.4356627e-34 0.0000000e+00 -2.3421070e-09 ... 0.0000000e+00 2.5811791e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.1817 - val_accuracy: 0.9675 [-4.4356627e-34 0.0000000e+00 -1.0969568e-12 ... -0.0000000e+00 2.9459586e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9797 - val_loss: 0.2190 - val_accuracy: 0.9590 [-4.4356627e-34 0.0000000e+00 -1.3856273e-08 ... 0.0000000e+00 3.4079906e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9790 - val_loss: 0.1913 - val_accuracy: 0.9645 [-4.4356627e-34 0.0000000e+00 1.1063107e-13 ... -0.0000000e+00 3.2372128e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9787 - val_loss: 0.1736 - val_accuracy: 0.9681 [-4.4356627e-34 0.0000000e+00 -2.0720938e-07 ... 0.0000000e+00 3.4817919e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9802 - val_loss: 0.1973 - val_accuracy: 0.9607 [-4.4356627e-34 0.0000000e+00 1.3105747e-12 ... 0.0000000e+00 3.2587487e-02 0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9802 - val_loss: 0.1841 - val_accuracy: 0.9653 [-4.4356627e-34 0.0000000e+00 8.1787135e-07 ... 0.0000000e+00 2.7937170e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1323 - accuracy: 0.9803 - val_loss: 0.2062 - val_accuracy: 0.9608 [-4.4356627e-34 0.0000000e+00 6.2087054e-13 ... -0.0000000e+00 2.8586470e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9788 - val_loss: 0.1928 - val_accuracy: 0.9638 [-4.4356627e-34 0.0000000e+00 -3.2267062e-06 ... 0.0000000e+00 2.6585540e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9793 - val_loss: 0.1819 - val_accuracy: 0.9684 [-4.4356627e-34 0.0000000e+00 4.8087419e-12 ... 0.0000000e+00 2.4012934e-02 -0.0000000e+00] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1382 - accuracy: 0.9784 - val_loss: 0.1906 - val_accuracy: 0.9630 [ 0.00000000e+00 0.00000000e+00 -1.10901765e-05 ... 0.00000000e+00 0.00000000e+00 -0.00000000e+00] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9790 - val_loss: 0.1971 - val_accuracy: 0.9619 [ 0.0000000e+00 0.0000000e+00 9.3825336e-11 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9797 - val_loss: 0.1929 - val_accuracy: 0.9639 [ 0.0000000e+00 0.0000000e+00 6.0431394e-06 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9797 - val_loss: 0.1930 - val_accuracy: 0.9622 [ 0.000000e+00 0.000000e+00 2.420294e-10 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9778 - val_loss: 0.1866 - val_accuracy: 0.9640 [ 0.0000000e+00 0.0000000e+00 3.1896767e-13 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1307 - accuracy: 0.9806 - val_loss: 0.1744 - val_accuracy: 0.9669 [ 0.0000000e+00 0.0000000e+00 3.0714983e-08 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1345 - accuracy: 0.9791 - val_loss: 0.1897 - val_accuracy: 0.9635 [ 0.0000000e+00 0.0000000e+00 3.0954333e-13 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1369 - accuracy: 0.9794 - val_loss: 0.1845 - val_accuracy: 0.9672 [ 0.0000000e+00 0.0000000e+00 -3.1310702e-07 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.1953 - val_accuracy: 0.9643 [ 0.000000e+00 0.000000e+00 -8.974476e-12 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1357 - accuracy: 0.9791 - val_loss: 0.1918 - val_accuracy: 0.9637 [ 0.000000e+00 0.000000e+00 2.656984e-05 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9796 - val_loss: 0.1949 - val_accuracy: 0.9620 [ 0.0000000e+00 0.0000000e+00 -1.3925776e-10 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9792 - val_loss: 0.1838 - val_accuracy: 0.9648 [ 0.000000e+00 0.000000e+00 -1.450744e-05 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9795 - val_loss: 0.1837 - val_accuracy: 0.9643 [ 0.0000000e+00 0.0000000e+00 1.4368234e-09 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1341 - accuracy: 0.9793 - val_loss: 0.1950 - val_accuracy: 0.9625 [ 0.0000000e+00 0.0000000e+00 -6.5859407e-10 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9796 - val_loss: 0.1944 - val_accuracy: 0.9628 [ 0.000000e+00 0.000000e+00 7.662484e-09 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9796 - val_loss: 0.1838 - val_accuracy: 0.9640 [ 0.0000000e+00 0.0000000e+00 -4.1895174e-13 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9802 - val_loss: 0.2107 - val_accuracy: 0.9574 [ 0.000000e+00 0.000000e+00 -3.666252e-08 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9800 - val_loss: 0.1824 - val_accuracy: 0.9651 [ 0.000000e+00 0.000000e+00 -7.948408e-14 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9791 - val_loss: 0.1905 - val_accuracy: 0.9650 [ 0.0000000e+00 0.0000000e+00 -1.8969446e-07 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9793 - val_loss: 0.1830 - val_accuracy: 0.9659 [ 0.0000000e+00 0.0000000e+00 2.7408983e-13 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1342 - accuracy: 0.9793 - val_loss: 0.1950 - val_accuracy: 0.9617 [ 0.0000000e+00 0.0000000e+00 -2.5255802e-06 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9803 - val_loss: 0.2352 - val_accuracy: 0.9516 [ 0.0000000e+00 0.0000000e+00 2.5782834e-11 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9796 - val_loss: 0.1807 - val_accuracy: 0.9667 [ 0.000000e+00 0.000000e+00 -4.067086e-05 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9795 - val_loss: 0.1920 - val_accuracy: 0.9640 [ 0.0000000e+00 0.0000000e+00 -3.2448738e-11 ... -0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1337 - accuracy: 0.9797 - val_loss: 0.2015 - val_accuracy: 0.9641 [ 0.000000e+00 0.000000e+00 -4.041456e-05 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9796 - val_loss: 0.1783 - val_accuracy: 0.9661 [ 0.0000000e+00 0.0000000e+00 -1.1982384e-09 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9794 - val_loss: 0.1990 - val_accuracy: 0.9599 [ 0.0000000e+00 0.0000000e+00 -2.0103468e-10 ... -0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9801 - val_loss: 0.2071 - val_accuracy: 0.9599 [ 0.00000000e+00 0.00000000e+00 -1.05678115e-08 ... 0.00000000e+00 -0.00000000e+00 -0.00000000e+00] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9791 - val_loss: 0.2264 - val_accuracy: 0.9522 [ 0.0000000e+00 0.0000000e+00 5.0367284e-13 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.1842 - val_accuracy: 0.9654 [ 0.0000000e+00 0.0000000e+00 -5.7416223e-08 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9798 - val_loss: 0.2186 - val_accuracy: 0.9575 [ 0.000000e+00 0.000000e+00 -4.046129e-13 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9796 - val_loss: 0.2148 - val_accuracy: 0.9543 [ 0.0000000e+00 0.0000000e+00 1.5337136e-07 ... -0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1308 - accuracy: 0.9804 - val_loss: 0.1888 - val_accuracy: 0.9644 [ 0.0000000e+00 0.0000000e+00 -1.3391382e-12 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1405 - accuracy: 0.9777 - val_loss: 0.1984 - val_accuracy: 0.9617 [ 0.0000000e+00 0.0000000e+00 1.3954218e-07 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9794 - val_loss: 0.1900 - val_accuracy: 0.9645 [ 0.000000e+00 0.000000e+00 -9.914687e-12 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9789 - val_loss: 0.1847 - val_accuracy: 0.9659 [ 0.00000e+00 0.00000e+00 -4.68079e-06 ... -0.00000e+00 -0.00000e+00 -0.00000e+00] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9798 - val_loss: 0.2027 - val_accuracy: 0.9616 [ 0.0000000e+00 0.0000000e+00 4.7218417e-11 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9793 - val_loss: 0.2030 - val_accuracy: 0.9571 [ 0.0000000e+00 0.0000000e+00 2.6743915e-05 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9796 - val_loss: 0.1848 - val_accuracy: 0.9664 [ 0.00000000e+00 0.00000000e+00 1.12650125e-10 ... 0.00000000e+00 0.00000000e+00 -0.00000000e+00] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9785 - val_loss: 0.2062 - val_accuracy: 0.9614 [ 0.000000e+00 0.000000e+00 -6.849979e-05 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9791 - val_loss: 0.2039 - val_accuracy: 0.9595 [ 0.0000000e+00 0.0000000e+00 -3.3240766e-10 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1367 - accuracy: 0.9785 - val_loss: 0.1897 - val_accuracy: 0.9634 [ 0.000000e+00 0.000000e+00 -2.473981e-07 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9803 - val_loss: 0.1956 - val_accuracy: 0.9631 [ 0.000000e+00 0.000000e+00 -2.167746e-09 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9796 - val_loss: 0.2088 - val_accuracy: 0.9585 [ 0.0000000e+00 0.0000000e+00 -2.6023326e-11 ... -0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2211 - val_accuracy: 0.9530 [ 0.000000e+00 0.000000e+00 5.179608e-09 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1365 - accuracy: 0.9790 - val_loss: 0.1793 - val_accuracy: 0.9674 [ 0.0000000e+00 0.0000000e+00 1.1345071e-13 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9789 - val_loss: 0.1976 - val_accuracy: 0.9592 [ 0.0000000e+00 0.0000000e+00 -7.4160187e-07 ... 0.0000000e+00 0.0000000e+00 0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9793 - val_loss: 0.2069 - val_accuracy: 0.9595 [ 0.0000000e+00 0.0000000e+00 -1.6349986e-14 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9790 - val_loss: 0.2184 - val_accuracy: 0.9554 [ 0.0000000e+00 0.0000000e+00 -1.3019195e-05 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1321 - accuracy: 0.9807 - val_loss: 0.2058 - val_accuracy: 0.9586 [ 0.0000000e+00 0.0000000e+00 -8.9659134e-11 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1368 - accuracy: 0.9779 - val_loss: 0.1793 - val_accuracy: 0.9653 [ 0.000000e+00 0.000000e+00 9.122719e-05 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9786 - val_loss: 0.2079 - val_accuracy: 0.9588 [ 0.0000000e+00 0.0000000e+00 1.1002177e-09 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1340 - accuracy: 0.9785 - val_loss: 0.1938 - val_accuracy: 0.9623 [ 0.0000000e+00 0.0000000e+00 -1.9955628e-09 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1369 - accuracy: 0.9785 - val_loss: 0.2126 - val_accuracy: 0.9591 [ 0.0000000e+00 0.0000000e+00 -1.7042057e-10 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9793 - val_loss: 0.2014 - val_accuracy: 0.9613 [ 0.0000000e+00 0.0000000e+00 -3.3773284e-12 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9785 - val_loss: 0.2231 - val_accuracy: 0.9530 [ 0.0000000e+00 0.0000000e+00 2.6485694e-08 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9786 - val_loss: 0.1938 - val_accuracy: 0.9638 [ 0.0000000e+00 0.0000000e+00 -5.0965195e-13 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1362 - accuracy: 0.9787 - val_loss: 0.1957 - val_accuracy: 0.9639 [ 0.000000e+00 0.000000e+00 -3.639437e-08 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9781 - val_loss: 0.1945 - val_accuracy: 0.9643 [ 0.000000e+00 0.000000e+00 6.132617e-13 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9793 - val_loss: 0.2332 - val_accuracy: 0.9516 [ 0.0000000e+00 0.0000000e+00 4.7822334e-07 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9790 - val_loss: 0.1917 - val_accuracy: 0.9632 [ 0.000000e+00 0.000000e+00 -1.966705e-12 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2156 - val_accuracy: 0.9570 [ 0.000000e+00 0.000000e+00 -1.616819e-05 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9789 - val_loss: 0.2114 - val_accuracy: 0.9561 [ 0.00000e+00 0.00000e+00 5.03321e-11 ... -0.00000e+00 0.00000e+00 0.00000e+00] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9790 - val_loss: 0.2597 - val_accuracy: 0.9450 [ 0.000000e+00 0.000000e+00 -8.558493e-07 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9777 - val_loss: 0.2103 - val_accuracy: 0.9586 [ 0.000000e+00 0.000000e+00 -6.353012e-10 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.2185 - val_accuracy: 0.9569 [ 0.000000e+00 0.000000e+00 3.145237e-13 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9788 - val_loss: 0.1964 - val_accuracy: 0.9628 [ 0.00000e+00 0.00000e+00 3.56048e-08 ... 0.00000e+00 0.00000e+00 -0.00000e+00] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1371 - accuracy: 0.9783 - val_loss: 0.1938 - val_accuracy: 0.9648 [ 0.0000000e+00 0.0000000e+00 3.1849425e-13 ... -0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9784 - val_loss: 0.1942 - val_accuracy: 0.9617 [ 0.0000000e+00 0.0000000e+00 4.9574453e-08 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1357 - accuracy: 0.9790 - val_loss: 0.2294 - val_accuracy: 0.9517 [ 0.00000e+00 0.00000e+00 -2.91406e-13 ... 0.00000e+00 -0.00000e+00 -0.00000e+00] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9776 - val_loss: 0.1734 - val_accuracy: 0.9704 [ 0.0000000e+00 0.0000000e+00 1.7178622e-06 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.1958 - val_accuracy: 0.9610 [ 0.0000000e+00 0.0000000e+00 4.0700582e-11 ... 0.0000000e+00 -0.0000000e+00 0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9789 - val_loss: 0.2025 - val_accuracy: 0.9615 [ 0.0000000e+00 0.0000000e+00 -8.1325794e-05 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9795 - val_loss: 0.2253 - val_accuracy: 0.9536 [ 0.0000000e+00 0.0000000e+00 -4.2789614e-10 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9786 - val_loss: 0.2314 - val_accuracy: 0.9528 [ 0.000000e+00 0.000000e+00 6.446089e-10 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1364 - accuracy: 0.9786 - val_loss: 0.1862 - val_accuracy: 0.9652 [ 0.00000e+00 0.00000e+00 2.41013e-10 ... -0.00000e+00 -0.00000e+00 -0.00000e+00] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9790 - val_loss: 0.2028 - val_accuracy: 0.9605 [ 0.0000000e+00 0.0000000e+00 3.7481926e-14 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9784 - val_loss: 0.2282 - val_accuracy: 0.9540 [ 0.000000e+00 0.000000e+00 -9.490899e-08 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2031 - val_accuracy: 0.9592 [ 0.0000000e+00 0.0000000e+00 -7.0986484e-13 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1391 - accuracy: 0.9778 - val_loss: 0.2167 - val_accuracy: 0.9587 [ 0.000000e+00 0.000000e+00 -3.865753e-06 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1332 - accuracy: 0.9799 - val_loss: 0.1905 - val_accuracy: 0.9637 [ 0.000000e+00 0.000000e+00 -2.171804e-11 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1348 - accuracy: 0.9787 - val_loss: 0.2044 - val_accuracy: 0.9609 [ 0.0000000e+00 0.0000000e+00 -2.4548866e-05 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9785 - val_loss: 0.1906 - val_accuracy: 0.9654 [ 0.0000000e+00 0.0000000e+00 -3.1039984e-10 ... -0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9798 - val_loss: 0.1802 - val_accuracy: 0.9683 [ 0.0000000e+00 0.0000000e+00 -1.8686228e-07 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.1834 - val_accuracy: 0.9693 [ 0.0000000e+00 0.0000000e+00 1.3447674e-09 ... 0.0000000e+00 -0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9791 - val_loss: 0.1898 - val_accuracy: 0.9630 [ 0.0000000e+00 0.0000000e+00 1.4321574e-11 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9788 - val_loss: 0.2183 - val_accuracy: 0.9529 [ 0.000000e+00 0.000000e+00 1.781405e-08 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9791 - val_loss: 0.2004 - val_accuracy: 0.9584 [ 0.000000e+00 0.000000e+00 1.799275e-13 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9782 - val_loss: 0.2263 - val_accuracy: 0.9522 [ 0.000000e+00 0.000000e+00 6.602303e-08 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9789 - val_loss: 0.1933 - val_accuracy: 0.9645 [ 0.000000e+00 0.000000e+00 1.078543e-12 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9797 - val_loss: 0.1967 - val_accuracy: 0.9625 [ 0.0000000e+00 0.0000000e+00 -7.2546334e-07 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2067 - val_accuracy: 0.9586 [ 0.00000e+00 0.00000e+00 -4.84798e-12 ... 0.00000e+00 0.00000e+00 -0.00000e+00] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9800 - val_loss: 0.2137 - val_accuracy: 0.9586 [ 0.0000000e+00 0.0000000e+00 8.7397086e-07 ... -0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9784 - val_loss: 0.2025 - val_accuracy: 0.9618 [ 0.000000e+00 0.000000e+00 9.598258e-12 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9791 - val_loss: 0.1935 - val_accuracy: 0.9627 [ 0.000000e+00 0.000000e+00 6.188666e-07 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9803 - val_loss: 0.1917 - val_accuracy: 0.9631 [ 0.000000e+00 0.000000e+00 -4.729945e-11 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2092 - val_accuracy: 0.9591 [ 0.00000000e+00 0.00000000e+00 -1.10468845e-05 ... -0.00000000e+00 0.00000000e+00 -0.00000000e+00] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9787 - val_loss: 0.2098 - val_accuracy: 0.9566 [ 0.0000000e+00 0.0000000e+00 -1.6847905e-11 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9808 - val_loss: 0.1943 - val_accuracy: 0.9615 [ 0.0000000e+00 0.0000000e+00 3.5817135e-05 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2142 - val_accuracy: 0.9580 [ 0.0000000e+00 0.0000000e+00 -1.5523263e-10 ... 0.0000000e+00 0.0000000e+00 -0.0000000e+00] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9777 - val_loss: 0.1769 - val_accuracy: 0.9683 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1386 - accuracy: 0.9778 - val_loss: 0.2163 - val_accuracy: 0.9570 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9777 - val_loss: 0.2232 - val_accuracy: 0.9517 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9787 - val_loss: 0.1947 - val_accuracy: 0.9622 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9779 - val_loss: 0.1877 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9789 - val_loss: 0.1892 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9784 - val_loss: 0.2305 - val_accuracy: 0.9521 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9779 - val_loss: 0.2202 - val_accuracy: 0.9561 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9782 - val_loss: 0.2181 - val_accuracy: 0.9582 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9781 - val_loss: 0.2053 - val_accuracy: 0.9611 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1382 - accuracy: 0.9786 - val_loss: 0.2529 - val_accuracy: 0.9473 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2279 - val_accuracy: 0.9524 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9776 - val_loss: 0.2178 - val_accuracy: 0.9567 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9777 - val_loss: 0.1997 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9794 - val_loss: 0.2038 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1351 - accuracy: 0.9787 - val_loss: 0.1945 - val_accuracy: 0.9659 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9786 - val_loss: 0.2124 - val_accuracy: 0.9578 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9777 - val_loss: 0.2140 - val_accuracy: 0.9591 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9783 - val_loss: 0.1876 - val_accuracy: 0.9660 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9792 - val_loss: 0.1946 - val_accuracy: 0.9629 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9797 - val_loss: 0.1980 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9767 - val_loss: 0.2060 - val_accuracy: 0.9605 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9794 - val_loss: 0.1948 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9791 - val_loss: 0.1953 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9783 - val_loss: 0.2118 - val_accuracy: 0.9587 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9778 - val_loss: 0.1980 - val_accuracy: 0.9650 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9795 - val_loss: 0.2045 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9787 - val_loss: 0.2029 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1344 - accuracy: 0.9787 - val_loss: 0.1963 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1360 - accuracy: 0.9787 - val_loss: 0.1879 - val_accuracy: 0.9645 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9784 - val_loss: 0.2016 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1369 - accuracy: 0.9783 - val_loss: 0.1990 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9767 - val_loss: 0.2106 - val_accuracy: 0.9599 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9794 - val_loss: 0.1917 - val_accuracy: 0.9626 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9779 - val_loss: 0.2095 - val_accuracy: 0.9582 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9798 - val_loss: 0.1843 - val_accuracy: 0.9669 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9786 - val_loss: 0.2177 - val_accuracy: 0.9590 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9790 - val_loss: 0.1882 - val_accuracy: 0.9665 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.2071 - val_accuracy: 0.9602 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9774 - val_loss: 0.2195 - val_accuracy: 0.9550 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9790 - val_loss: 0.1886 - val_accuracy: 0.9648 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9789 - val_loss: 0.1970 - val_accuracy: 0.9600 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9787 - val_loss: 0.2310 - val_accuracy: 0.9538 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9779 - val_loss: 0.2156 - val_accuracy: 0.9568 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9789 - val_loss: 0.2121 - val_accuracy: 0.9580 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1343 - accuracy: 0.9792 - val_loss: 0.2014 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1352 - accuracy: 0.9787 - val_loss: 0.2240 - val_accuracy: 0.9528 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.1896 - val_accuracy: 0.9655 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9780 - val_loss: 0.1980 - val_accuracy: 0.9620 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9794 - val_loss: 0.2352 - val_accuracy: 0.9502 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9776 - val_loss: 0.1893 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9791 - val_loss: 0.1886 - val_accuracy: 0.9631 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9798 - val_loss: 0.1824 - val_accuracy: 0.9649 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9790 - val_loss: 0.2002 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1264 - accuracy: 0.9790 - val_loss: 0.2162 - val_accuracy: 0.9572 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9791 - val_loss: 0.1990 - val_accuracy: 0.9582 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9808 - val_loss: 0.1774 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1206 - accuracy: 0.9803 - val_loss: 0.1787 - val_accuracy: 0.9643 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9804 - val_loss: 0.1979 - val_accuracy: 0.9594 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9815 - val_loss: 0.1787 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9804 - val_loss: 0.2051 - val_accuracy: 0.9565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9803 - val_loss: 0.1998 - val_accuracy: 0.9598 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1217 - accuracy: 0.9808 - val_loss: 0.1757 - val_accuracy: 0.9659 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9803 - val_loss: 0.1738 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9802 - val_loss: 0.1955 - val_accuracy: 0.9612 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9811 - val_loss: 0.2095 - val_accuracy: 0.9565 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1196 - accuracy: 0.9803 - val_loss: 0.1839 - val_accuracy: 0.9621 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9801 - val_loss: 0.1799 - val_accuracy: 0.9645 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9807 - val_loss: 0.1903 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9814 - val_loss: 0.1787 - val_accuracy: 0.9667 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9812 - val_loss: 0.2090 - val_accuracy: 0.9572 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1214 - accuracy: 0.9801 - val_loss: 0.1951 - val_accuracy: 0.9612 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9817 - val_loss: 0.2143 - val_accuracy: 0.9545 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9792 - val_loss: 0.2043 - val_accuracy: 0.9586 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9802 - val_loss: 0.2010 - val_accuracy: 0.9566 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9808 - val_loss: 0.2186 - val_accuracy: 0.9541 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9802 - val_loss: 0.2045 - val_accuracy: 0.9573 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9802 - val_loss: 0.1858 - val_accuracy: 0.9634 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9810 - val_loss: 0.1798 - val_accuracy: 0.9658 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9804 - val_loss: 0.1822 - val_accuracy: 0.9653 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9804 - val_loss: 0.1958 - val_accuracy: 0.9607 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9809 - val_loss: 0.1917 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.2145 - val_accuracy: 0.9551 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9800 - val_loss: 0.2253 - val_accuracy: 0.9524 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9815 - val_loss: 0.2032 - val_accuracy: 0.9578 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1223 - accuracy: 0.9800 - val_loss: 0.1794 - val_accuracy: 0.9663 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9811 - val_loss: 0.1808 - val_accuracy: 0.9638 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1194 - accuracy: 0.9805 - val_loss: 0.1850 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9813 - val_loss: 0.1935 - val_accuracy: 0.9608 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1201 - accuracy: 0.9800 - val_loss: 0.2114 - val_accuracy: 0.9561 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1170 - accuracy: 0.9811 - val_loss: 0.1717 - val_accuracy: 0.9675 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1172 - accuracy: 0.9810 - val_loss: 0.1753 - val_accuracy: 0.9662 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9811 - val_loss: 0.2093 - val_accuracy: 0.9544 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9816 - val_loss: 0.1864 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9809 - val_loss: 0.2057 - val_accuracy: 0.9559 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9809 - val_loss: 0.1917 - val_accuracy: 0.9600 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9807 - val_loss: 0.2053 - val_accuracy: 0.9568 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1187 - accuracy: 0.9806 - val_loss: 0.1970 - val_accuracy: 0.9581 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1173 - accuracy: 0.9807 - val_loss: 0.1945 - val_accuracy: 0.9605 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9806 - val_loss: 0.1918 - val_accuracy: 0.9624 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9755 - val_loss: 0.1819 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9786 - val_loss: 0.1700 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9791 - val_loss: 0.1811 - val_accuracy: 0.9643 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1146 - accuracy: 0.9792 - val_loss: 0.1944 - val_accuracy: 0.9595 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1126 - accuracy: 0.9803 - val_loss: 0.1998 - val_accuracy: 0.9562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9807 - val_loss: 0.1729 - val_accuracy: 0.9652 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1091 - accuracy: 0.9804 - val_loss: 0.1837 - val_accuracy: 0.9619 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1116 - accuracy: 0.9795 - val_loss: 0.1897 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9809 - val_loss: 0.1729 - val_accuracy: 0.9654 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1064 - accuracy: 0.9816 - val_loss: 0.2077 - val_accuracy: 0.9535 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1059 - accuracy: 0.9813 - val_loss: 0.2058 - val_accuracy: 0.9539 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1062 - accuracy: 0.9811 - val_loss: 0.1853 - val_accuracy: 0.9612 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1055 - accuracy: 0.9810 - val_loss: 0.1774 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1070 - accuracy: 0.9806 - val_loss: 0.1835 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1067 - accuracy: 0.9806 - val_loss: 0.1801 - val_accuracy: 0.9609 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9813 - val_loss: 0.1668 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - accuracy: 0.9809 - val_loss: 0.2037 - val_accuracy: 0.9579 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9819 - val_loss: 0.1843 - val_accuracy: 0.9604 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9809 - val_loss: 0.2075 - val_accuracy: 0.9555 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9813 - val_loss: 0.1894 - val_accuracy: 0.9589 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1056 - accuracy: 0.9815 - val_loss: 0.1792 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1039 - accuracy: 0.9814 - val_loss: 0.1878 - val_accuracy: 0.9608 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1071 - accuracy: 0.9804 - val_loss: 0.2092 - val_accuracy: 0.9531 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9800 - val_loss: 0.1893 - val_accuracy: 0.9593 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1040 - accuracy: 0.9818 - val_loss: 0.1878 - val_accuracy: 0.9612 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1023 - accuracy: 0.9818 - val_loss: 0.1865 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1070 - accuracy: 0.9804 - val_loss: 0.1940 - val_accuracy: 0.9593 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9815 - val_loss: 0.1768 - val_accuracy: 0.9627 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1051 - accuracy: 0.9811 - val_loss: 0.1891 - val_accuracy: 0.9577 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9812 - val_loss: 0.1974 - val_accuracy: 0.9585 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9808 - val_loss: 0.1863 - val_accuracy: 0.9593 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9813 - val_loss: 0.1955 - val_accuracy: 0.9579 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1059 - accuracy: 0.9809 - val_loss: 0.1798 - val_accuracy: 0.9623 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9809 - val_loss: 0.1956 - val_accuracy: 0.9585 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1046 - accuracy: 0.9816 - val_loss: 0.1895 - val_accuracy: 0.9589 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1056 - accuracy: 0.9807 - val_loss: 0.1810 - val_accuracy: 0.9602 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1066 - accuracy: 0.9809 - val_loss: 0.1769 - val_accuracy: 0.9643 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9811 - val_loss: 0.1800 - val_accuracy: 0.9625 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9814 - val_loss: 0.1833 - val_accuracy: 0.9604 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1053 - accuracy: 0.9811 - val_loss: 0.1816 - val_accuracy: 0.9623 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1040 - accuracy: 0.9819 - val_loss: 0.1707 - val_accuracy: 0.9654 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9808 - val_loss: 0.2062 - val_accuracy: 0.9552 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1038 - accuracy: 0.9818 - val_loss: 0.1794 - val_accuracy: 0.9614 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9812 - val_loss: 0.1744 - val_accuracy: 0.9660 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1055 - accuracy: 0.9807 - val_loss: 0.1863 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1055 - accuracy: 0.9807 - val_loss: 0.1822 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9816 - val_loss: 0.1714 - val_accuracy: 0.9650 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9814 - val_loss: 0.1795 - val_accuracy: 0.9598 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9814 - val_loss: 0.1780 - val_accuracy: 0.9625 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1027 - accuracy: 0.9817 - val_loss: 0.2028 - val_accuracy: 0.9579 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2009 - accuracy: 0.9611 - val_loss: 0.2082 - val_accuracy: 0.9567 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1457 - accuracy: 0.9724 - val_loss: 0.1913 - val_accuracy: 0.9602 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9743 - val_loss: 0.1906 - val_accuracy: 0.9607 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9748 - val_loss: 0.1865 - val_accuracy: 0.9624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9743 - val_loss: 0.1823 - val_accuracy: 0.9634 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1251 - accuracy: 0.9758 - val_loss: 0.1819 - val_accuracy: 0.9643 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9755 - val_loss: 0.1834 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9767 - val_loss: 0.1729 - val_accuracy: 0.9654 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9764 - val_loss: 0.1724 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1225 - accuracy: 0.9758 - val_loss: 0.1789 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9772 - val_loss: 0.1907 - val_accuracy: 0.9594 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9761 - val_loss: 0.1817 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9761 - val_loss: 0.1792 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9771 - val_loss: 0.1830 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9775 - val_loss: 0.1830 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9762 - val_loss: 0.1721 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1160 - accuracy: 0.9774 - val_loss: 0.1760 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1159 - accuracy: 0.9774 - val_loss: 0.1752 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9766 - val_loss: 0.1695 - val_accuracy: 0.9660 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1155 - accuracy: 0.9776 - val_loss: 0.1730 - val_accuracy: 0.9643 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9765 - val_loss: 0.1782 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1168 - accuracy: 0.9770 - val_loss: 0.1784 - val_accuracy: 0.9645 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9764 - val_loss: 0.1869 - val_accuracy: 0.9619 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9773 - val_loss: 0.1792 - val_accuracy: 0.9626 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9772 - val_loss: 0.1794 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1153 - accuracy: 0.9779 - val_loss: 0.1855 - val_accuracy: 0.9598 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9772 - val_loss: 0.1832 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1150 - accuracy: 0.9776 - val_loss: 0.1719 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9766 - val_loss: 0.1692 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1158 - accuracy: 0.9782 - val_loss: 0.1690 - val_accuracy: 0.9656 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9769 - val_loss: 0.1756 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1150 - accuracy: 0.9775 - val_loss: 0.1868 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1156 - accuracy: 0.9778 - val_loss: 0.1783 - val_accuracy: 0.9618 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1149 - accuracy: 0.9774 - val_loss: 0.1822 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9769 - val_loss: 0.1727 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9772 - val_loss: 0.1744 - val_accuracy: 0.9656 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9772 - val_loss: 0.1791 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1137 - accuracy: 0.9780 - val_loss: 0.1823 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1168 - accuracy: 0.9765 - val_loss: 0.1726 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1164 - accuracy: 0.9770 - val_loss: 0.1847 - val_accuracy: 0.9614 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1150 - accuracy: 0.9779 - val_loss: 0.1756 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9770 - val_loss: 0.1755 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1155 - accuracy: 0.9771 - val_loss: 0.1810 - val_accuracy: 0.9634 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9779 - val_loss: 0.1736 - val_accuracy: 0.9647 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1137 - accuracy: 0.9784 - val_loss: 0.1819 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1156 - accuracy: 0.9776 - val_loss: 0.1742 - val_accuracy: 0.9626 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1141 - accuracy: 0.9778 - val_loss: 0.1705 - val_accuracy: 0.9649 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1154 - accuracy: 0.9773 - val_loss: 0.1712 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9762 - val_loss: 0.1878 - val_accuracy: 0.9619 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9773 - val_loss: 0.1732 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9736 - val_loss: 0.1699 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9769 - val_loss: 0.1803 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1148 - accuracy: 0.9767 - val_loss: 0.1741 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1152 - accuracy: 0.9763 - val_loss: 0.1744 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1137 - accuracy: 0.9765 - val_loss: 0.1802 - val_accuracy: 0.9616 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1127 - accuracy: 0.9768 - val_loss: 0.1721 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1136 - accuracy: 0.9765 - val_loss: 0.1726 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1131 - accuracy: 0.9771 - val_loss: 0.1826 - val_accuracy: 0.9607 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1120 - accuracy: 0.9771 - val_loss: 0.1743 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1129 - accuracy: 0.9764 - val_loss: 0.1632 - val_accuracy: 0.9656 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1122 - accuracy: 0.9769 - val_loss: 0.1715 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1122 - accuracy: 0.9772 - val_loss: 0.1849 - val_accuracy: 0.9603 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1125 - accuracy: 0.9770 - val_loss: 0.1674 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1123 - accuracy: 0.9765 - val_loss: 0.1756 - val_accuracy: 0.9626 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1109 - accuracy: 0.9770 - val_loss: 0.1766 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1114 - accuracy: 0.9770 - val_loss: 0.1801 - val_accuracy: 0.9611 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9779 - val_loss: 0.1776 - val_accuracy: 0.9614 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1106 - accuracy: 0.9777 - val_loss: 0.1658 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1108 - accuracy: 0.9767 - val_loss: 0.1748 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1102 - accuracy: 0.9776 - val_loss: 0.1671 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1102 - accuracy: 0.9776 - val_loss: 0.1678 - val_accuracy: 0.9661 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1097 - accuracy: 0.9773 - val_loss: 0.1683 - val_accuracy: 0.9643 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1104 - accuracy: 0.9769 - val_loss: 0.1786 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9777 - val_loss: 0.1863 - val_accuracy: 0.9586 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1100 - accuracy: 0.9771 - val_loss: 0.1667 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1099 - accuracy: 0.9768 - val_loss: 0.1692 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1108 - accuracy: 0.9767 - val_loss: 0.1776 - val_accuracy: 0.9612 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1094 - accuracy: 0.9770 - val_loss: 0.1758 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9774 - val_loss: 0.1717 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1096 - accuracy: 0.9772 - val_loss: 0.1671 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1101 - accuracy: 0.9769 - val_loss: 0.1686 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1093 - accuracy: 0.9773 - val_loss: 0.1765 - val_accuracy: 0.9634 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1100 - accuracy: 0.9772 - val_loss: 0.1667 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1090 - accuracy: 0.9774 - val_loss: 0.1720 - val_accuracy: 0.9645 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1091 - accuracy: 0.9776 - val_loss: 0.1760 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1095 - accuracy: 0.9772 - val_loss: 0.1662 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1095 - accuracy: 0.9770 - val_loss: 0.1749 - val_accuracy: 0.9625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1097 - accuracy: 0.9775 - val_loss: 0.1704 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9768 - val_loss: 0.1750 - val_accuracy: 0.9633 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1093 - accuracy: 0.9772 - val_loss: 0.1748 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1092 - accuracy: 0.9771 - val_loss: 0.1829 - val_accuracy: 0.9603 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1090 - accuracy: 0.9777 - val_loss: 0.1802 - val_accuracy: 0.9609 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9775 - val_loss: 0.1720 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1092 - accuracy: 0.9771 - val_loss: 0.1682 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1098 - accuracy: 0.9773 - val_loss: 0.1703 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9776 - val_loss: 0.1673 - val_accuracy: 0.9640 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1085 - accuracy: 0.9776 - val_loss: 0.1645 - val_accuracy: 0.9655 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1098 - accuracy: 0.9765 - val_loss: 0.1669 - val_accuracy: 0.9634 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1091 - accuracy: 0.9770 - val_loss: 0.1730 - val_accuracy: 0.9633 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1083 - accuracy: 0.9774 - val_loss: 0.1729 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1097 - accuracy: 0.9768 - val_loss: 0.1700 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1085 - accuracy: 0.9777 - val_loss: 0.1708 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9768 - val_loss: 0.1714 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9779 - val_loss: 0.1717 - val_accuracy: 0.9623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9774 - val_loss: 0.1802 - val_accuracy: 0.9624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9771 - val_loss: 0.1754 - val_accuracy: 0.9625 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1103 - accuracy: 0.9768 - val_loss: 0.1724 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1106 - accuracy: 0.9763 - val_loss: 0.1655 - val_accuracy: 0.9645 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1097 - accuracy: 0.9775 - val_loss: 0.1621 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1088 - accuracy: 0.9769 - val_loss: 0.1770 - val_accuracy: 0.9615 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1094 - accuracy: 0.9779 - val_loss: 0.1598 - val_accuracy: 0.9656 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1093 - accuracy: 0.9775 - val_loss: 0.1592 - val_accuracy: 0.9659 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1089 - accuracy: 0.9773 - val_loss: 0.1668 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1087 - accuracy: 0.9773 - val_loss: 0.1791 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1094 - accuracy: 0.9770 - val_loss: 0.1834 - val_accuracy: 0.9604 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1087 - accuracy: 0.9772 - val_loss: 0.1736 - val_accuracy: 0.9622 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1086 - accuracy: 0.9772 - val_loss: 0.1776 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1089 - accuracy: 0.9775 - val_loss: 0.1708 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1084 - accuracy: 0.9772 - val_loss: 0.1722 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1087 - accuracy: 0.9775 - val_loss: 0.1714 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1099 - accuracy: 0.9770 - val_loss: 0.1685 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1095 - accuracy: 0.9765 - val_loss: 0.1749 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1086 - accuracy: 0.9772 - val_loss: 0.1639 - val_accuracy: 0.9654 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1084 - accuracy: 0.9774 - val_loss: 0.1727 - val_accuracy: 0.9619 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1082 - accuracy: 0.9773 - val_loss: 0.1760 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1087 - accuracy: 0.9772 - val_loss: 0.1704 - val_accuracy: 0.9640 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1086 - accuracy: 0.9779 - val_loss: 0.1692 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9777 - val_loss: 0.1685 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9770 - val_loss: 0.1702 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1103 - accuracy: 0.9768 - val_loss: 0.1688 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1079 - accuracy: 0.9779 - val_loss: 0.1677 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1078 - accuracy: 0.9771 - val_loss: 0.1607 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1088 - accuracy: 0.9769 - val_loss: 0.1693 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1077 - accuracy: 0.9777 - val_loss: 0.1655 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1091 - accuracy: 0.9769 - val_loss: 0.1654 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1078 - accuracy: 0.9774 - val_loss: 0.1662 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1084 - accuracy: 0.9775 - val_loss: 0.1661 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1089 - accuracy: 0.9773 - val_loss: 0.1613 - val_accuracy: 0.9661 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9768 - val_loss: 0.1665 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1079 - accuracy: 0.9774 - val_loss: 0.1668 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9777 - val_loss: 0.1657 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1078 - accuracy: 0.9772 - val_loss: 0.1693 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1085 - accuracy: 0.9776 - val_loss: 0.1690 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1086 - accuracy: 0.9770 - val_loss: 0.1662 - val_accuracy: 0.9654 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1080 - accuracy: 0.9778 - val_loss: 0.1663 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1072 - accuracy: 0.9781 - val_loss: 0.1678 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1094 - accuracy: 0.9764 - val_loss: 0.1645 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1069 - accuracy: 0.9774 - val_loss: 0.1633 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1071 - accuracy: 0.9772 - val_loss: 0.1630 - val_accuracy: 0.9661 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1083 - accuracy: 0.9772 - val_loss: 0.1695 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 9.3331e-04 - accuracy: 0.9997 - val_loss: 0.0963 - val_accuracy: 0.9812 [-0. 0. -0. ... 0.5728107 -0.6194763 -0.34995005] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5382e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.5811358 -0.62145585 -0.34729576] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1270e-04 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.5845508 -0.6308096 -0.3416493] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1033e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.58650583 -0.6309471 -0.34129578] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9506e-05 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.58817655 -0.63367796 -0.35052896] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3892e-05 - accuracy: 1.0000 - val_loss: 0.0931 - val_accuracy: 0.9830 [-0. 0. -0. ... 0.5890956 -0.63567436 -0.3518097 ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6770e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9834 [-0. 0. -0. ... 0.59242564 -0.63557327 -0.35123745] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1067e-05 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.5933231 -0.64107764 -0.3517232 ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7829e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9835 [-0. 0. -0. ... 0.5950826 -0.6455001 -0.35310227] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9340e-05 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9836 [-0. 0. -0. ... 0.5969851 -0.64611626 -0.35453779] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1802e-04 - accuracy: 0.9999 - val_loss: 0.1043 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.59762824 -0.6596632 -0.35390502] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6295e-04 - accuracy: 0.9999 - val_loss: 0.1089 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.60045296 -0.69052464 -0.32379308] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6196e-04 - accuracy: 0.9999 - val_loss: 0.1081 - val_accuracy: 0.9828 [-0. 0. -0. ... 0.63626266 -0.6899871 -0.34524125] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2674e-04 - accuracy: 0.9999 - val_loss: 0.1055 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.63799417 -0.68720376 -0.34359086] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3679e-04 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.64237183 -0.68737996 -0.3410983 ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6625e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9834 [-0. 0. -0. ... 0.6461495 -0.68922085 -0.3433997 ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3966e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9832 [-0. 0. -0. ... 0.6486699 -0.69084466 -0.3469219 ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8556e-04 - accuracy: 0.9999 - val_loss: 0.1090 - val_accuracy: 0.9820 [-0. 0. -0. ... 0.6470774 -0.69216007 -0.33084062] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5657e-04 - accuracy: 0.9998 - val_loss: 0.1073 - val_accuracy: 0.9830 [-0. 0. -0. ... 0.64972854 -0.6898631 -0.32484606] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2469e-04 - accuracy: 0.9999 - val_loss: 0.1132 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.62412965 -0.69792885 -0.30385524] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4485e-04 - accuracy: 0.9998 - val_loss: 0.1153 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.6229628 -0.6654983 -0.31234008] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8763e-04 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9822 [-0. 0. -0. ... 0.6175275 -0.6579139 -0.31474936] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2513e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.6234867 -0.63990015 -0.3232531 ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3872e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9822 [-0. 0. -0. ... 0.6274907 -0.64659023 -0.32716078] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3270e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.62874603 -0.6493433 -0.32712048] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3714e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9812 [-0. 0. -0. ... 0.63365585 -0.65055966 -0.32116568] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7069e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9813 [-0. 0. -0. ... 0.6328648 -0.65292984 -0.3230161 ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1656e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9814 [-0. 0. -0. ... 0.6346627 -0.6539204 -0.32331395] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0874e-06 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9815 [-0. 0. -0. ... 0.6369615 -0.65541726 -0.3234316 ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9655e-06 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9816 [-0. 0. -0. ... 0.6373124 -0.65784353 -0.32275504] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9628e-06 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9815 [-0. 0. -0. ... 0.6411442 -0.65870833 -0.32401726] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 6.6336e-06 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.6473526 -0.66045934 -0.32407165] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3290e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9819 [-0. 0. -0. ... 0.6489468 -0.66235393 -0.3240567 ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7615e-06 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9819 [-0. 0. -0. ... 0.64948905 -0.6630712 -0.32466218] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4540e-06 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9815 [-0. 0. -0. ... 0.65234184 -0.663413 -0.32477307] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1417e-06 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9820 [-0. 0. -0. ... 0.6529727 -0.66734636 -0.32475352] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0414e-06 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9821 [-0. 0. -0. ... 0.6502373 -0.6688133 -0.3254238] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9960e-06 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9822 [-0. 0. -0. ... 0.65443945 -0.6701251 -0.32661685] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6559e-06 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9823 [-0. 0. -0. ... 0.65405536 -0.67231345 -0.32769272] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1687e-06 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9823 [-0. 0. -0. ... 0.6547837 -0.675713 -0.32839793] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2624e-06 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.6563494 -0.6817796 -0.32847393] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2849e-06 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9828 [-0. 0. -0. ... 0.6584201 -0.6855066 -0.32805812] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3630e-06 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9832 [-0. 0. -0. ... 0.66052485 -0.686105 -0.32879087] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2724e-06 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.65127695 -0.6895687 -0.3293585 ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0209e-06 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.6537033 -0.69137114 -0.3300522 ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7297e-06 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.6557985 -0.6974854 -0.33050433] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5521e-06 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.6577344 -0.7002139 -0.3307969] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6236e-06 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.66088337 -0.6997571 -0.33187795] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3420e-06 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.6629594 -0.7033369 -0.33326378] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3051e-06 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9835 [-0. 0. -0. ... 0.66472346 -0.70540833 -0.33328238] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9987 - val_loss: 0.1142 - val_accuracy: 0.9809 [-0. 0. -0. ... 0.67267823 -0.6694673 0. ] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2343e-04 - accuracy: 0.9999 - val_loss: 0.1156 - val_accuracy: 0.9814 [-0. 0. -0. ... 0.6750742 -0.6694366 -0. ] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5477e-04 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9815 [-0. 0. -0. ... 0.67589384 -0.6586552 -0. ] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8033e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9813 [-0. 0. -0. ... 0.67959446 -0.664264 -0. ] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0351e-05 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9814 [-0. 0. -0. ... 0.68111336 -0.6574598 -0. ] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4314e-05 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9814 [-0. 0. -0. ... 0.6823232 -0.65832376 -0. ] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3096e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9813 [-0. 0. -0. ... 0.6837406 -0.65799636 -0. ] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0015e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9812 [-0. 0. -0. ... 0.6841942 -0.65819967 0. ] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0865e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9816 [-0. 0. -0. ... 0.6842039 -0.65945446 -0. ] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5436e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.6856578 -0.65961194 -0. ] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9722e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9819 [-0. 0. -0. ... 0.6870233 -0.6593791 -0. ] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7636e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9820 [-0. 0. -0. ... 0.68911314 -0.65971917 -0. ] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2463e-05 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9816 [-0. 0. -0. ... 0.6898468 -0.65928704 -0. ] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0311e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.6919321 -0.6597065 -0. ] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5163e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.69464403 -0.6592978 -0. ] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5711e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.6958347 -0.6609568 -0. ] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2783e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9821 [-0. 0. -0. ... 0.6976866 -0.6617891 0. ] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0779e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9820 [-0. 0. -0. ... 0.7008152 -0.66302764 -0. ] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1982e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.7011633 -0.6635848 -0. ] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5149e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9816 [-0. 0. -0. ... 0.7069478 -0.66461915 0. ] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0693e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.70389295 -0.66573805 -0. ] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0642e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9816 [-0. 0. -0. ... 0.7057545 -0.6640868 0. ] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5360e-06 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.7102162 -0.66545075 0. ] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7784e-06 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.7119753 -0.665771 -0. ] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7769e-06 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.7098874 -0.66466635 -0. ] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2641e-06 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9828 [-0. 0. -0. ... 0.71202105 -0.6655856 -0. ] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4936e-06 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9825 [-0. 0. -0. ... 0.71641064 -0.66721 -0. ] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6450e-06 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.7192422 -0.66830343 -0. ] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2055e-06 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.7219333 -0.67022395 -0. ] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9112e-06 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9823 [-0. 0. -0. ... 0.72621113 -0.67057467 -0. ] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7637e-06 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.727075 -0.6721101 -0. ] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5869e-06 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.72855395 -0.6740664 0. ] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7235e-06 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.7290966 -0.6759711 -0. ] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3406e-06 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.7085638 -0.67781353 -0. ] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0892e-04 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.70539594 -0.6756918 -0. ] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0508e-04 - accuracy: 0.9998 - val_loss: 0.1336 - val_accuracy: 0.9806 [-0. 0. -0. ... 0.7289927 -0.7011832 -0. ] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 9.1676e-04 - accuracy: 0.9997 - val_loss: 0.1304 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.69481236 -0.68962526 -0. ] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2219e-04 - accuracy: 0.9998 - val_loss: 0.1232 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.7086477 -0.6901053 0. ] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7720e-05 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.71027124 -0.68777096 -0. ] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7224e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9822 [-0. 0. -0. ... 0.7110911 -0.6880556 -0. ] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1619e-05 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9821 [-0. 0. -0. ... 0.71161515 -0.68817335 0. ] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5976e-05 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.7118119 -0.6930628 -0. ] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3374e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9828 [-0. 0. -0. ... 0.71268284 -0.6952215 -0. ] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4212e-06 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.7130383 -0.6965197 -0. ] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0812e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.7131864 -0.6953783 -0. ] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6503e-04 - accuracy: 0.9999 - val_loss: 0.1279 - val_accuracy: 0.9828 [-0. 0. -0. ... 0.7134659 -0.69130635 -0. ] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1213e-05 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9831 [-0. 0. -0. ... 0.711992 -0.69202596 -0. ] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1584e-06 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9832 [-0. 0. -0. ... 0.71246517 -0.69141304 -0. ] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5545e-06 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9834 [-0. 0. -0. ... 0.7131963 -0.69123703 -0. ] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1739e-06 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9836 [-0. 0. -0. ... 0.71325 -0.69116396 -0. ] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9967 - val_loss: 0.1108 - val_accuracy: 0.9806 [-0. 0. -0. ... 0.6110119 -0.6858295 0. ] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1105 - val_accuracy: 0.9808 [-0. 0. -0. ... 0.63798857 -0.6979264 -0. ] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1219e-04 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9812 [-0. 0. -0. ... 0.6407899 -0.6906702 0. ] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6631e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9813 [-0. 0. -0. ... 0.6435331 -0.6927668 -0. ] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9895e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9821 [-0. 0. -0. ... 0.64305925 -0.69544995 0. ] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7461e-04 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9812 [-0. 0. -0. ... 0.640739 -0.6956936 0. ] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7881e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.64870965 -0.6990817 0. ] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3145e-04 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.65189624 -0.6992605 0. ] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5564e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9813 [-0. 0. -0. ... 0.65277624 -0.7000053 0. ] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2340e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9819 [-0. 0. -0. ... 0.65494007 -0.6991881 0. ] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2995e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9819 [-0. 0. -0. ... 0.65609974 -0.6998431 0. ] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 4s 15ms/step - loss: 8.2249e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.65814567 -0.69843817 -0. ] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2097e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9820 [-0. 0. -0. ... 0.6591217 -0.70076704 -0. ] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5924e-05 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.6605608 -0.7008054 -0. ] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8858e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9821 [-0. 0. -0. ... 0.663057 -0.7001511 0. ] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2744e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.6676221 -0.70617723 0. ] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 4s 17ms/step - loss: 4.6379e-05 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9818 [-0. 0. -0. ... 0.67018235 -0.7089968 -0. ] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7506e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.67002004 -0.70976394 -0. ] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7285e-05 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9820 [-0. 0. -0. ... 0.6723043 -0.71164054 -0. ] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2465e-05 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.675022 -0.71514684 -0. ] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5052e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9819 [-0. 0. -0. ... 0.6752753 -0.72023225 -0. ] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0463e-05 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9817 [-0. 0. -0. ... 0.67813927 -0.7205321 -0. ] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4964e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9825 [-0. 0. -0. ... 0.6758883 -0.7230654 0. ] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0061e-05 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.6784352 -0.7260054 0. ] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7300e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9825 [-0. 0. -0. ... 0.68057126 -0.7288986 0. ] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 6.6649e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9822 [-0. 0. -0. ... 0.68047976 -0.73600656 -0. ] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9053e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.6839424 -0.74509305 -0. ] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9285e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.68527794 -0.7477193 0. ] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6842e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.6905687 -0.74836195 0. ] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8267e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.6902968 -0.7543926 0. ] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9052e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9838 [-0. 0. -0. ... 0.6981326 -0.75526774 0. ] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2835e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9837 [-0. 0. -0. ... 0.6737219 -0.7577034 0. ] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2843e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9839 [-0. 0. -0. ... 0.6510969 -0.7578433 0. ] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6188e-04 - accuracy: 0.9999 - val_loss: 0.1189 - val_accuracy: 0.9835 [-0. 0. -0. ... 0.6972773 -0.7576598 0. ] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7531e-04 - accuracy: 0.9999 - val_loss: 0.1187 - val_accuracy: 0.9821 [-0. 0. -0. ... 0.6902221 -0.76433235 -0. ] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5578e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.6930773 -0.77579236 -0. ] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6565e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9825 [-0. 0. -0. ... 0.6931349 -0.7786544 0. ] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1491e-05 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.69264024 -0.76636523 -0. ] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8180e-06 - accuracy: 1.0000 - val_loss: 0.1191 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.6935656 -0.77079767 0. ] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5495e-05 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.6879964 -0.7699719 -0. ] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1827e-04 - accuracy: 0.9999 - val_loss: 0.1248 - val_accuracy: 0.9824 [-0. 0. -0. ... 0.7046541 -0.81172335 -0. ] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7369e-04 - accuracy: 1.0000 - val_loss: 0.1251 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.70606863 -0.83346945 -0. ] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6858e-05 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.7075756 -0.8355491 -0. ] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2029e-05 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9829 [-0. 0. -0. ... 0.70732874 -0.831987 0. ] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5877e-06 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9830 [-0. 0. -0. ... 0.7074176 -0.83183855 -0. ] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4195e-06 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.7074956 -0.83091474 0. ] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5892e-06 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.7075618 -0.8297385 -0. ] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2573e-06 - accuracy: 1.0000 - val_loss: 0.1269 - val_accuracy: 0.9827 [-0. 0. -0. ... 0.7080372 -0.8293254 -0. ] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5151e-06 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9826 [-0. 0. -0. ... 0.7085081 -0.8294451 -0. ] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1128e-06 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9828 [-0. 0. -0. ... 0.7086039 -0.83034563 -0. ] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0252 - accuracy: 0.9921 - val_loss: 0.1123 - val_accuracy: 0.9785 [-0. 0. -0. ... -0. -0.7440849 -0. ] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 0.1080 - val_accuracy: 0.9790 [-0. 0. -0. ... 0. -0.7526541 -0. ] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.1076 - val_accuracy: 0.9789 [-0. 0. -0. ... 0. -0.75774723 0. ] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1078 - val_accuracy: 0.9798 [-0. 0. -0. ... 0. -0.75869465 -0. ] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8900e-04 - accuracy: 0.9999 - val_loss: 0.1062 - val_accuracy: 0.9806 [-0. 0. -0. ... 0. -0.7609396 -0. ] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5973e-04 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9806 [-0. 0. -0. ... -0. -0.7665332 0. ] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4631e-04 - accuracy: 0.9999 - val_loss: 0.1062 - val_accuracy: 0.9805 [-0. 0. -0. ... 0. -0.7592011 0. ] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1691e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9814 [-0. 0. -0. ... 0. -0.76015586 -0. ] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1296e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9804 [-0. 0. -0. ... 0. -0.7656703 -0. ] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2932e-04 - accuracy: 0.9999 - val_loss: 0.1090 - val_accuracy: 0.9803 [-0. 0. -0. ... -0. -0.7622859 -0. ] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3926e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9808 [-0. 0. -0. ... 0. -0.77538466 -0. ] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1746e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9810 [-0. 0. -0. ... 0. -0.7781318 -0. ] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6398e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9808 [-0. 0. -0. ... 0. -0.78141093 -0. ] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3711e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9810 [-0. 0. -0. ... 0. -0.79104614 -0. ] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7049e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9806 [-0. 0. -0. ... 0. -0.79307246 -0. ] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1104e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9808 [-0. 0. -0. ... 0. -0.793305 -0. ] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 4s 16ms/step - loss: 1.6608e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9816 [-0. 0. -0. ... 0. -0.79998714 -0. ] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 4s 16ms/step - loss: 1.4595e-04 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9815 [-0. 0. -0. ... 0. -0.80366963 0. ] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4174e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9816 [-0. 0. -0. ... 0. -0.8007117 -0. ] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2445e-04 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9813 [-0. 0. -0. ... 0. -0.8061623 -0. ] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8029e-04 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9816 [-0. 0. -0. ... 0. -0.8125732 -0. ] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2205e-04 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9812 [-0. 0. -0. ... 0. -0.80843836 -0. ] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2125e-04 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9821 [-0. 0. -0. ... 0. -0.811196 -0. ] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5808e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9822 [-0. 0. -0. ... -0. -0.80934453 -0. ] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9900e-05 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9821 [-0. 0. -0. ... 0. -0.8203639 -0. ] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2783e-05 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9828 [-0. 0. -0. ... -0. -0.8211397 0. ] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9989e-05 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9825 [-0. 0. -0. ... -0. -0.8268195 -0. ] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 15ms/step - loss: 5.5061e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9825 [-0. 0. -0. ... 0. -0.8345771 -0. ] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7776e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9824 [-0. 0. -0. ... -0. -0.8287348 -0. ] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0984e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9827 [-0. 0. -0. ... -0. -0.8321112 0. ] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4910e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9822 [-0. 0. -0. ... -0. -0.83348036 0. ] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4088e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9820 [-0. 0. -0. ... 0. -0.83803266 -0. ] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8119e-05 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9816 [-0. 0. -0. ... -0. -0.8416033 -0. ] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 8.3554e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9814 [-0. 0. -0. ... 0. -0.8419356 -0. ] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0285e-05 - accuracy: 1.0000 - val_loss: 0.1234 - val_accuracy: 0.9826 [-0. 0. -0. ... 0. -0.8441631 0. ] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3261e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9817 [-0. 0. -0. ... -0. -0.8416663 -0. ] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7940e-05 - accuracy: 1.0000 - val_loss: 0.1254 - val_accuracy: 0.9816 [-0. 0. -0. ... 0. -0.8414333 0. ] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6141e-04 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9821 [-0. 0. -0. ... -0. -0.8852608 -0. ] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8587e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9822 [-0. 0. -0. ... 0. -0.88370085 -0. ] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1298e-04 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9818 [-0. 0. -0. ... -0. -0.8710609 -0. ] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8512e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9816 [-0. 0. -0. ... -0. -0.88074225 -0. ] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7639e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9814 [-0. 0. -0. ... -0. -0.8799711 -0. ] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0734e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9821 [-0. 0. -0. ... 0. -0.8837086 -0. ] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4109e-05 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9811 [-0. 0. -0. ... -0. -0.8808844 -0. ] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2703e-05 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9815 [-0. 0. -0. ... -0. -0.87856007 -0. ] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8782e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9817 [-0. 0. -0. ... 0. -0.88340646 -0. ] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8361e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9818 [-0. 0. -0. ... 0. -0.88370496 -0. ] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5962e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9815 [-0. 0. -0. ... 0. -0.8791857 -0. ] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1569e-05 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9814 [-0. 0. -0. ... 0. -0.88159984 -0. ] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7513e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9816 [-0. 0. -0. ... -0. -0.8855052 0. ] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0740 - accuracy: 0.9809 - val_loss: 0.1357 - val_accuracy: 0.9722 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0228 - accuracy: 0.9925 - val_loss: 0.1260 - val_accuracy: 0.9753 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0141 - accuracy: 0.9957 - val_loss: 0.1226 - val_accuracy: 0.9756 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0103 - accuracy: 0.9969 - val_loss: 0.1192 - val_accuracy: 0.9761 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9979 - val_loss: 0.1180 - val_accuracy: 0.9767 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9982 - val_loss: 0.1174 - val_accuracy: 0.9769 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9988 - val_loss: 0.1172 - val_accuracy: 0.9772 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.1161 - val_accuracy: 0.9767 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.1173 - val_accuracy: 0.9771 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.1178 - val_accuracy: 0.9774 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1178 - val_accuracy: 0.9778 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.1184 - val_accuracy: 0.9779 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.1179 - val_accuracy: 0.9782 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.1189 - val_accuracy: 0.9782 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1196 - val_accuracy: 0.9787 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1218 - val_accuracy: 0.9783 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1218 - val_accuracy: 0.9785 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1240 - val_accuracy: 0.9792 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 9.6738e-04 - accuracy: 1.0000 - val_loss: 0.1251 - val_accuracy: 0.9784 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5441e-04 - accuracy: 0.9999 - val_loss: 0.1244 - val_accuracy: 0.9787 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9439e-04 - accuracy: 1.0000 - val_loss: 0.1274 - val_accuracy: 0.9788 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7263e-04 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9784 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1095e-04 - accuracy: 1.0000 - val_loss: 0.1282 - val_accuracy: 0.9784 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7242e-04 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9781 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5615e-04 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9791 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0292e-04 - accuracy: 1.0000 - val_loss: 0.1291 - val_accuracy: 0.9783 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8570e-04 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9790 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6339e-04 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9781 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4515e-04 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9793 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8250e-04 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9795 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2899e-04 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9787 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2006e-04 - accuracy: 0.9999 - val_loss: 0.1339 - val_accuracy: 0.9788 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8067e-04 - accuracy: 1.0000 - val_loss: 0.1350 - val_accuracy: 0.9785 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8182e-04 - accuracy: 0.9999 - val_loss: 0.1373 - val_accuracy: 0.9789 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2219e-04 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9788 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1484e-04 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9787 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6711e-04 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9790 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2025e-04 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9788 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1301e-04 - accuracy: 0.9999 - val_loss: 0.1430 - val_accuracy: 0.9781 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7922e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9787 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7816e-04 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9790 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5838e-04 - accuracy: 1.0000 - val_loss: 0.1417 - val_accuracy: 0.9789 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3195e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9788 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3744e-04 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9789 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8176e-04 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9789 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3314e-04 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9789 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0997e-04 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9782 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5966e-04 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 0.9786 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0773e-04 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9788 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6043e-04 - accuracy: 0.9999 - val_loss: 0.1515 - val_accuracy: 0.9785 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1821 - accuracy: 0.9564 - val_loss: 0.1846 - val_accuracy: 0.9614 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0854 - accuracy: 0.9748 - val_loss: 0.1615 - val_accuracy: 0.9659 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0674 - accuracy: 0.9790 - val_loss: 0.1510 - val_accuracy: 0.9657 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0576 - accuracy: 0.9818 - val_loss: 0.1450 - val_accuracy: 0.9678 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0524 - accuracy: 0.9834 - val_loss: 0.1405 - val_accuracy: 0.9683 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0467 - accuracy: 0.9848 - val_loss: 0.1373 - val_accuracy: 0.9687 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0428 - accuracy: 0.9864 - val_loss: 0.1355 - val_accuracy: 0.9696 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0397 - accuracy: 0.9872 - val_loss: 0.1337 - val_accuracy: 0.9697 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0368 - accuracy: 0.9880 - val_loss: 0.1328 - val_accuracy: 0.9702 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0345 - accuracy: 0.9886 - val_loss: 0.1313 - val_accuracy: 0.9712 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0320 - accuracy: 0.9898 - val_loss: 0.1299 - val_accuracy: 0.9708 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0306 - accuracy: 0.9899 - val_loss: 0.1291 - val_accuracy: 0.9711 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0286 - accuracy: 0.9907 - val_loss: 0.1292 - val_accuracy: 0.9710 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0265 - accuracy: 0.9916 - val_loss: 0.1286 - val_accuracy: 0.9713 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0254 - accuracy: 0.9918 - val_loss: 0.1284 - val_accuracy: 0.9709 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0240 - accuracy: 0.9927 - val_loss: 0.1288 - val_accuracy: 0.9714 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0222 - accuracy: 0.9932 - val_loss: 0.1284 - val_accuracy: 0.9718 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0216 - accuracy: 0.9936 - val_loss: 0.1290 - val_accuracy: 0.9726 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0203 - accuracy: 0.9940 - val_loss: 0.1298 - val_accuracy: 0.9721 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0196 - accuracy: 0.9944 - val_loss: 0.1305 - val_accuracy: 0.9725 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0182 - accuracy: 0.9948 - val_loss: 0.1308 - val_accuracy: 0.9723 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0170 - accuracy: 0.9952 - val_loss: 0.1315 - val_accuracy: 0.9728 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0166 - accuracy: 0.9954 - val_loss: 0.1316 - val_accuracy: 0.9725 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0156 - accuracy: 0.9955 - val_loss: 0.1340 - val_accuracy: 0.9724 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.1333 - val_accuracy: 0.9725 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.1342 - val_accuracy: 0.9728 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0133 - accuracy: 0.9966 - val_loss: 0.1350 - val_accuracy: 0.9722 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0128 - accuracy: 0.9966 - val_loss: 0.1365 - val_accuracy: 0.9721 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9974 - val_loss: 0.1374 - val_accuracy: 0.9725 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0113 - accuracy: 0.9973 - val_loss: 0.1391 - val_accuracy: 0.9719 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0109 - accuracy: 0.9974 - val_loss: 0.1380 - val_accuracy: 0.9726 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0106 - accuracy: 0.9973 - val_loss: 0.1403 - val_accuracy: 0.9731 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.1422 - val_accuracy: 0.9728 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0096 - accuracy: 0.9978 - val_loss: 0.1422 - val_accuracy: 0.9734 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0087 - accuracy: 0.9981 - val_loss: 0.1446 - val_accuracy: 0.9724 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0086 - accuracy: 0.9982 - val_loss: 0.1462 - val_accuracy: 0.9726 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0081 - accuracy: 0.9984 - val_loss: 0.1463 - val_accuracy: 0.9724 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9981 - val_loss: 0.1470 - val_accuracy: 0.9729 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.1487 - val_accuracy: 0.9730 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0071 - accuracy: 0.9985 - val_loss: 0.1502 - val_accuracy: 0.9732 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9988 - val_loss: 0.1505 - val_accuracy: 0.9730 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0064 - accuracy: 0.9988 - val_loss: 0.1528 - val_accuracy: 0.9728 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0059 - accuracy: 0.9990 - val_loss: 0.1550 - val_accuracy: 0.9728 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9989 - val_loss: 0.1547 - val_accuracy: 0.9724 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0057 - accuracy: 0.9990 - val_loss: 0.1572 - val_accuracy: 0.9714 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9989 - val_loss: 0.1588 - val_accuracy: 0.9716 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9993 - val_loss: 0.1611 - val_accuracy: 0.9720 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0049 - accuracy: 0.9992 - val_loss: 0.1613 - val_accuracy: 0.9720 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0045 - accuracy: 0.9992 - val_loss: 0.1648 - val_accuracy: 0.9719 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.1653 - val_accuracy: 0.9720 [-0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.6125 - accuracy: 0.8541 - val_loss: 0.4022 - val_accuracy: 0.8906 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.3103 - accuracy: 0.9083 - val_loss: 0.3055 - val_accuracy: 0.9151 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2572 - accuracy: 0.9220 - val_loss: 0.2715 - val_accuracy: 0.9238 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2317 - accuracy: 0.9297 - val_loss: 0.2528 - val_accuracy: 0.9277 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2174 - accuracy: 0.9332 - val_loss: 0.2404 - val_accuracy: 0.9310 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2076 - accuracy: 0.9363 - val_loss: 0.2311 - val_accuracy: 0.9343 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1987 - accuracy: 0.9388 - val_loss: 0.2242 - val_accuracy: 0.9372 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1918 - accuracy: 0.9411 - val_loss: 0.2181 - val_accuracy: 0.9395 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1864 - accuracy: 0.9425 - val_loss: 0.2130 - val_accuracy: 0.9407 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1806 - accuracy: 0.9444 - val_loss: 0.2088 - val_accuracy: 0.9424 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1758 - accuracy: 0.9459 - val_loss: 0.2056 - val_accuracy: 0.9429 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1719 - accuracy: 0.9467 - val_loss: 0.2030 - val_accuracy: 0.9441 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1684 - accuracy: 0.9481 - val_loss: 0.2001 - val_accuracy: 0.9450 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1641 - accuracy: 0.9492 - val_loss: 0.1979 - val_accuracy: 0.9455 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1608 - accuracy: 0.9499 - val_loss: 0.1960 - val_accuracy: 0.9458 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1593 - accuracy: 0.9511 - val_loss: 0.1941 - val_accuracy: 0.9469 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1563 - accuracy: 0.9520 - val_loss: 0.1923 - val_accuracy: 0.9473 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1545 - accuracy: 0.9525 - val_loss: 0.1911 - val_accuracy: 0.9472 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1520 - accuracy: 0.9528 - val_loss: 0.1896 - val_accuracy: 0.9474 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1495 - accuracy: 0.9538 - val_loss: 0.1881 - val_accuracy: 0.9486 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1482 - accuracy: 0.9544 - val_loss: 0.1872 - val_accuracy: 0.9494 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1469 - accuracy: 0.9545 - val_loss: 0.1866 - val_accuracy: 0.9498 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1449 - accuracy: 0.9560 - val_loss: 0.1853 - val_accuracy: 0.9500 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1432 - accuracy: 0.9561 - val_loss: 0.1842 - val_accuracy: 0.9503 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1419 - accuracy: 0.9565 - val_loss: 0.1833 - val_accuracy: 0.9509 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1405 - accuracy: 0.9570 - val_loss: 0.1824 - val_accuracy: 0.9504 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1391 - accuracy: 0.9572 - val_loss: 0.1822 - val_accuracy: 0.9506 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1376 - accuracy: 0.9576 - val_loss: 0.1817 - val_accuracy: 0.9510 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1378 - accuracy: 0.9578 - val_loss: 0.1811 - val_accuracy: 0.9514 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1358 - accuracy: 0.9584 - val_loss: 0.1806 - val_accuracy: 0.9514 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1348 - accuracy: 0.9585 - val_loss: 0.1803 - val_accuracy: 0.9516 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1336 - accuracy: 0.9586 - val_loss: 0.1800 - val_accuracy: 0.9511 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9590 - val_loss: 0.1800 - val_accuracy: 0.9513 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9599 - val_loss: 0.1792 - val_accuracy: 0.9512 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9596 - val_loss: 0.1795 - val_accuracy: 0.9511 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9598 - val_loss: 0.1791 - val_accuracy: 0.9508 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9597 - val_loss: 0.1790 - val_accuracy: 0.9514 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1282 - accuracy: 0.9604 - val_loss: 0.1788 - val_accuracy: 0.9508 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9607 - val_loss: 0.1785 - val_accuracy: 0.9508 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1263 - accuracy: 0.9614 - val_loss: 0.1781 - val_accuracy: 0.9510 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1259 - accuracy: 0.9614 - val_loss: 0.1781 - val_accuracy: 0.9506 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1244 - accuracy: 0.9617 - val_loss: 0.1776 - val_accuracy: 0.9502 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9623 - val_loss: 0.1773 - val_accuracy: 0.9502 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9621 - val_loss: 0.1773 - val_accuracy: 0.9505 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9625 - val_loss: 0.1771 - val_accuracy: 0.9510 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1212 - accuracy: 0.9630 - val_loss: 0.1772 - val_accuracy: 0.9512 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1206 - accuracy: 0.9627 - val_loss: 0.1770 - val_accuracy: 0.9511 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1197 - accuracy: 0.9634 - val_loss: 0.1770 - val_accuracy: 0.9513 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1189 - accuracy: 0.9635 - val_loss: 0.1769 - val_accuracy: 0.9517 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9630 - val_loss: 0.1769 - val_accuracy: 0.9513 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 4s 16ms/step - loss: 0.7342 - accuracy: 0.7664 - val_loss: 0.6443 - val_accuracy: 0.8018 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 15ms/step - loss: 0.6177 - accuracy: 0.8041 - val_loss: 0.6057 - val_accuracy: 0.8130 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 4s 16ms/step - loss: 0.5922 - accuracy: 0.8116 - val_loss: 0.5868 - val_accuracy: 0.8170 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 15ms/step - loss: 0.5757 - accuracy: 0.8165 - val_loss: 0.5735 - val_accuracy: 0.8259 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 13ms/step - loss: 0.5635 - accuracy: 0.8221 - val_loss: 0.5641 - val_accuracy: 0.8295 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5555 - accuracy: 0.8238 - val_loss: 0.5571 - val_accuracy: 0.8325 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5478 - accuracy: 0.8267 - val_loss: 0.5513 - val_accuracy: 0.8341 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 15ms/step - loss: 0.5417 - accuracy: 0.8286 - val_loss: 0.5465 - val_accuracy: 0.8359 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5360 - accuracy: 0.8305 - val_loss: 0.5420 - val_accuracy: 0.8377 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5314 - accuracy: 0.8321 - val_loss: 0.5376 - val_accuracy: 0.8400 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 4s 15ms/step - loss: 0.5269 - accuracy: 0.8334 - val_loss: 0.5341 - val_accuracy: 0.8410 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 15ms/step - loss: 0.5218 - accuracy: 0.8356 - val_loss: 0.5308 - val_accuracy: 0.8423 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 15ms/step - loss: 0.5201 - accuracy: 0.8366 - val_loss: 0.5279 - val_accuracy: 0.8424 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 15ms/step - loss: 0.5139 - accuracy: 0.8376 - val_loss: 0.5248 - val_accuracy: 0.8423 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5115 - accuracy: 0.8393 - val_loss: 0.5211 - val_accuracy: 0.8434 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 4s 16ms/step - loss: 0.5066 - accuracy: 0.8412 - val_loss: 0.5172 - val_accuracy: 0.8466 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5031 - accuracy: 0.8426 - val_loss: 0.5140 - val_accuracy: 0.8471 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 4s 15ms/step - loss: 0.5001 - accuracy: 0.8430 - val_loss: 0.5114 - val_accuracy: 0.8497 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4969 - accuracy: 0.8446 - val_loss: 0.5087 - val_accuracy: 0.8496 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4946 - accuracy: 0.8445 - val_loss: 0.5056 - val_accuracy: 0.8517 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4906 - accuracy: 0.8462 - val_loss: 0.5017 - val_accuracy: 0.8519 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4863 - accuracy: 0.8486 - val_loss: 0.4976 - val_accuracy: 0.8524 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4805 - accuracy: 0.8505 - val_loss: 0.4931 - val_accuracy: 0.8538 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4749 - accuracy: 0.8519 - val_loss: 0.4873 - val_accuracy: 0.8571 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4710 - accuracy: 0.8535 - val_loss: 0.4837 - val_accuracy: 0.8580 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4666 - accuracy: 0.8547 - val_loss: 0.4811 - val_accuracy: 0.8582 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4650 - accuracy: 0.8559 - val_loss: 0.4801 - val_accuracy: 0.8586 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4643 - accuracy: 0.8552 - val_loss: 0.4788 - val_accuracy: 0.8582 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4624 - accuracy: 0.8553 - val_loss: 0.4777 - val_accuracy: 0.8580 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4611 - accuracy: 0.8569 - val_loss: 0.4780 - val_accuracy: 0.8589 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4597 - accuracy: 0.8570 - val_loss: 0.4753 - val_accuracy: 0.8584 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4583 - accuracy: 0.8573 - val_loss: 0.4746 - val_accuracy: 0.8596 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4578 - accuracy: 0.8568 - val_loss: 0.4739 - val_accuracy: 0.8592 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4573 - accuracy: 0.8570 - val_loss: 0.4732 - val_accuracy: 0.8599 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 13ms/step - loss: 0.4566 - accuracy: 0.8574 - val_loss: 0.4721 - val_accuracy: 0.8601 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4562 - accuracy: 0.8579 - val_loss: 0.4711 - val_accuracy: 0.8602 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4550 - accuracy: 0.8573 - val_loss: 0.4722 - val_accuracy: 0.8606 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4543 - accuracy: 0.8579 - val_loss: 0.4711 - val_accuracy: 0.8612 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4539 - accuracy: 0.8585 - val_loss: 0.4710 - val_accuracy: 0.8611 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4531 - accuracy: 0.8587 - val_loss: 0.4702 - val_accuracy: 0.8600 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4522 - accuracy: 0.8590 - val_loss: 0.4689 - val_accuracy: 0.8618 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4519 - accuracy: 0.8590 - val_loss: 0.4680 - val_accuracy: 0.8613 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4509 - accuracy: 0.8587 - val_loss: 0.4685 - val_accuracy: 0.8614 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4506 - accuracy: 0.8599 - val_loss: 0.4676 - val_accuracy: 0.8608 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4501 - accuracy: 0.8594 - val_loss: 0.4672 - val_accuracy: 0.8612 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4491 - accuracy: 0.8601 - val_loss: 0.4659 - val_accuracy: 0.8594 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4494 - accuracy: 0.8600 - val_loss: 0.4676 - val_accuracy: 0.8612 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 4s 17ms/step - loss: 0.4495 - accuracy: 0.8595 - val_loss: 0.4684 - val_accuracy: 0.8616 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4487 - accuracy: 0.8607 - val_loss: 0.4661 - val_accuracy: 0.8607 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4492 - accuracy: 0.8593 - val_loss: 0.4654 - val_accuracy: 0.8612 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0034 - accuracy: 0.6751 - val_loss: 0.9745 - val_accuracy: 0.6446 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9290 - accuracy: 0.6858 - val_loss: 0.9438 - val_accuracy: 0.6497 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 4s 16ms/step - loss: 0.9165 - accuracy: 0.6895 - val_loss: 0.9324 - val_accuracy: 0.6510 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9125 - accuracy: 0.6906 - val_loss: 0.9235 - val_accuracy: 0.6933 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9091 - accuracy: 0.6931 - val_loss: 0.9208 - val_accuracy: 0.6942 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9070 - accuracy: 0.6932 - val_loss: 0.9181 - val_accuracy: 0.6949 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9045 - accuracy: 0.6935 - val_loss: 0.9146 - val_accuracy: 0.6962 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9037 - accuracy: 0.6952 - val_loss: 0.9116 - val_accuracy: 0.6939 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9008 - accuracy: 0.6957 - val_loss: 0.9096 - val_accuracy: 0.6952 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8992 - accuracy: 0.6963 - val_loss: 0.9081 - val_accuracy: 0.6953 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8975 - accuracy: 0.6958 - val_loss: 0.9078 - val_accuracy: 0.6974 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8964 - accuracy: 0.6976 - val_loss: 0.9054 - val_accuracy: 0.6986 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8949 - accuracy: 0.6982 - val_loss: 0.9047 - val_accuracy: 0.6981 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8946 - accuracy: 0.6991 - val_loss: 0.9038 - val_accuracy: 0.6989 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8934 - accuracy: 0.6981 - val_loss: 0.9019 - val_accuracy: 0.6994 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8927 - accuracy: 0.6992 - val_loss: 0.9018 - val_accuracy: 0.6994 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8923 - accuracy: 0.6996 - val_loss: 0.9011 - val_accuracy: 0.7003 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8907 - accuracy: 0.7000 - val_loss: 0.9003 - val_accuracy: 0.7009 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8898 - accuracy: 0.7004 - val_loss: 0.8995 - val_accuracy: 0.7015 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8892 - accuracy: 0.7008 - val_loss: 0.8981 - val_accuracy: 0.7024 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8884 - accuracy: 0.7005 - val_loss: 0.9000 - val_accuracy: 0.7018 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8879 - accuracy: 0.7013 - val_loss: 0.8975 - val_accuracy: 0.7024 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8864 - accuracy: 0.7016 - val_loss: 0.8976 - val_accuracy: 0.7017 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8869 - accuracy: 0.7013 - val_loss: 0.8964 - val_accuracy: 0.7027 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8871 - accuracy: 0.7020 - val_loss: 0.8963 - val_accuracy: 0.7024 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8842 - accuracy: 0.7010 - val_loss: 0.8947 - val_accuracy: 0.7039 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8841 - accuracy: 0.7017 - val_loss: 0.8930 - val_accuracy: 0.7045 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8837 - accuracy: 0.7019 - val_loss: 0.8932 - val_accuracy: 0.7042 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8823 - accuracy: 0.7029 - val_loss: 0.8930 - val_accuracy: 0.7045 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8819 - accuracy: 0.7022 - val_loss: 0.8930 - val_accuracy: 0.7049 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8818 - accuracy: 0.7024 - val_loss: 0.8927 - val_accuracy: 0.7054 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8811 - accuracy: 0.7025 - val_loss: 0.8917 - val_accuracy: 0.7050 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8812 - accuracy: 0.7016 - val_loss: 0.8922 - val_accuracy: 0.7050 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8805 - accuracy: 0.7022 - val_loss: 0.8928 - val_accuracy: 0.7046 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8798 - accuracy: 0.7036 - val_loss: 0.8910 - val_accuracy: 0.7057 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8806 - accuracy: 0.7029 - val_loss: 0.8912 - val_accuracy: 0.7053 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8800 - accuracy: 0.7018 - val_loss: 0.8905 - val_accuracy: 0.7031 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8806 - accuracy: 0.7025 - val_loss: 0.8900 - val_accuracy: 0.7031 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8788 - accuracy: 0.7031 - val_loss: 0.8904 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8794 - accuracy: 0.7013 - val_loss: 0.8905 - val_accuracy: 0.7063 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8785 - accuracy: 0.7034 - val_loss: 0.8901 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8792 - accuracy: 0.7028 - val_loss: 0.8901 - val_accuracy: 0.7065 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8781 - accuracy: 0.7027 - val_loss: 0.8898 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8787 - accuracy: 0.7016 - val_loss: 0.8896 - val_accuracy: 0.7064 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8777 - accuracy: 0.7028 - val_loss: 0.8893 - val_accuracy: 0.7062 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8790 - accuracy: 0.7030 - val_loss: 0.8888 - val_accuracy: 0.7066 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8786 - accuracy: 0.7028 - val_loss: 0.8898 - val_accuracy: 0.7066 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8779 - accuracy: 0.7033 - val_loss: 0.8899 - val_accuracy: 0.7069 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8781 - accuracy: 0.7014 - val_loss: 0.8890 - val_accuracy: 0.7041 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 4s 17ms/step - loss: 0.8779 - accuracy: 0.7032 - val_loss: 0.8890 - val_accuracy: 0.7057 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8773 - accuracy: 0.7027 - val_loss: 0.8896 - val_accuracy: 0.7064 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8769 - accuracy: 0.7031 - val_loss: 0.8888 - val_accuracy: 0.7066 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8772 - accuracy: 0.7034 - val_loss: 0.8887 - val_accuracy: 0.7068 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8768 - accuracy: 0.7028 - val_loss: 0.8895 - val_accuracy: 0.7063 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8767 - accuracy: 0.7030 - val_loss: 0.8888 - val_accuracy: 0.7064 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8776 - accuracy: 0.7034 - val_loss: 0.8883 - val_accuracy: 0.7067 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8768 - accuracy: 0.7030 - val_loss: 0.8892 - val_accuracy: 0.7058 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8772 - accuracy: 0.7021 - val_loss: 0.8880 - val_accuracy: 0.7035 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8771 - accuracy: 0.7036 - val_loss: 0.8892 - val_accuracy: 0.7059 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8770 - accuracy: 0.7035 - val_loss: 0.8885 - val_accuracy: 0.7060 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8758 - accuracy: 0.7034 - val_loss: 0.8885 - val_accuracy: 0.7059 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8762 - accuracy: 0.7034 - val_loss: 0.8882 - val_accuracy: 0.7056 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8765 - accuracy: 0.7030 - val_loss: 0.8884 - val_accuracy: 0.7063 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8751 - accuracy: 0.7036 - val_loss: 0.8880 - val_accuracy: 0.7059 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 4s 17ms/step - loss: 0.8763 - accuracy: 0.7033 - val_loss: 0.8896 - val_accuracy: 0.7055 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8756 - accuracy: 0.7025 - val_loss: 0.8870 - val_accuracy: 0.7033 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8757 - accuracy: 0.7037 - val_loss: 0.8877 - val_accuracy: 0.7057 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 4s 16ms/step - loss: 0.8763 - accuracy: 0.7035 - val_loss: 0.8875 - val_accuracy: 0.7033 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8766 - accuracy: 0.7041 - val_loss: 0.8881 - val_accuracy: 0.7055 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8753 - accuracy: 0.7039 - val_loss: 0.8885 - val_accuracy: 0.7052 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8758 - accuracy: 0.7029 - val_loss: 0.8885 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8747 - accuracy: 0.7042 - val_loss: 0.8887 - val_accuracy: 0.7058 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8756 - accuracy: 0.7025 - val_loss: 0.8893 - val_accuracy: 0.7049 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8758 - accuracy: 0.7033 - val_loss: 0.8882 - val_accuracy: 0.7048 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8757 - accuracy: 0.7034 - val_loss: 0.8881 - val_accuracy: 0.7053 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8762 - accuracy: 0.7027 - val_loss: 0.8872 - val_accuracy: 0.7054 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8751 - accuracy: 0.7030 - val_loss: 0.8884 - val_accuracy: 0.7058 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8762 - accuracy: 0.7028 - val_loss: 0.8866 - val_accuracy: 0.7025 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8741 - accuracy: 0.7032 - val_loss: 0.8883 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8749 - accuracy: 0.7039 - val_loss: 0.8872 - val_accuracy: 0.7059 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8741 - accuracy: 0.7042 - val_loss: 0.8868 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8746 - accuracy: 0.7035 - val_loss: 0.8884 - val_accuracy: 0.7060 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8753 - accuracy: 0.7035 - val_loss: 0.8874 - val_accuracy: 0.7058 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8739 - accuracy: 0.7035 - val_loss: 0.8873 - val_accuracy: 0.7060 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8748 - accuracy: 0.7036 - val_loss: 0.8878 - val_accuracy: 0.7056 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8754 - accuracy: 0.7028 - val_loss: 0.8875 - val_accuracy: 0.7061 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7032 - val_loss: 0.8870 - val_accuracy: 0.7063 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8754 - accuracy: 0.7028 - val_loss: 0.8873 - val_accuracy: 0.7056 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8745 - accuracy: 0.7040 - val_loss: 0.8866 - val_accuracy: 0.7063 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8745 - accuracy: 0.7043 - val_loss: 0.8863 - val_accuracy: 0.7032 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8742 - accuracy: 0.7048 - val_loss: 0.8876 - val_accuracy: 0.7057 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8746 - accuracy: 0.7032 - val_loss: 0.8864 - val_accuracy: 0.7057 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7033 - val_loss: 0.8863 - val_accuracy: 0.7060 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8749 - accuracy: 0.7032 - val_loss: 0.8854 - val_accuracy: 0.7031 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8738 - accuracy: 0.7043 - val_loss: 0.8868 - val_accuracy: 0.7064 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8739 - accuracy: 0.7040 - val_loss: 0.8856 - val_accuracy: 0.7034 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7042 - val_loss: 0.8862 - val_accuracy: 0.7064 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8743 - accuracy: 0.7041 - val_loss: 0.8864 - val_accuracy: 0.7058 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8744 - accuracy: 0.7046 - val_loss: 0.8851 - val_accuracy: 0.7064 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8740 - accuracy: 0.7038 - val_loss: 0.8855 - val_accuracy: 0.7058 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 3s 9ms/step - loss: 0.8520 - accuracy: 0.9004 - val_loss: 0.8256 - val_accuracy: 0.9053 [ 0. 0. 0. ... -0.11999476 0.09713747 -0. ] Sparsity at: 0.4999497049356223 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8428 - accuracy: 0.9017 - val_loss: 0.8238 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0.11510681 0.09732954 -0. ] Sparsity at: 0.4999497049356223 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8417 - accuracy: 0.9017 - val_loss: 0.8237 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0.11250631 0.09858902 -0. ] Sparsity at: 0.4999497049356223 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.9016 - val_loss: 0.8230 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0.11115648 0.09971751 -0. ] Sparsity at: 0.4999497049356223 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.9015 - val_loss: 0.8226 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0.11069585 0.10087128 -0. ] Sparsity at: 0.4999497049356223 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9013 - val_loss: 0.8225 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0.11003719 0.10171769 -0. ] Sparsity at: 0.4999497049356223 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9016 - val_loss: 0.8231 - val_accuracy: 0.9056 [ 0. 0. 0. ... -0.11021721 0.10235994 -0. ] Sparsity at: 0.4999497049356223 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8402 - accuracy: 0.9018 - val_loss: 0.8223 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0.11026673 0.10327499 -0. ] Sparsity at: 0.4999497049356223 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8403 - accuracy: 0.9016 - val_loss: 0.8222 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0.11059546 0.10384838 0. ] Sparsity at: 0.4999497049356223 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8402 - accuracy: 0.9015 - val_loss: 0.8220 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0.11047424 0.10421267 -0. ] Sparsity at: 0.4999497049356223 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9016 - val_loss: 0.8221 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0.11097842 0.10494617 -0. ] Sparsity at: 0.4999497049356223 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8399 - accuracy: 0.9017 - val_loss: 0.8219 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0.11090565 0.10508011 -0. ] Sparsity at: 0.4999497049356223 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0.11091372 0.10521565 -0. ] Sparsity at: 0.4999497049356223 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9016 - val_loss: 0.8217 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0.11115445 0.10520121 -0. ] Sparsity at: 0.4999497049356223 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9015 - val_loss: 0.8218 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0.11103234 0.10562972 -0. ] Sparsity at: 0.4999497049356223 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8400 - accuracy: 0.9017 - val_loss: 0.8217 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0.11115702 0.10545632 -0. ] Sparsity at: 0.4999497049356223 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9018 - val_loss: 0.8220 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0.11081094 0.10569409 -0. ] Sparsity at: 0.4999497049356223 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9016 - val_loss: 0.8221 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0.11115512 0.10595326 -0. ] Sparsity at: 0.4999497049356223 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9013 - val_loss: 0.8216 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0.1115019 0.10596164 -0. ] Sparsity at: 0.4999497049356223 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9020 - val_loss: 0.8219 - val_accuracy: 0.9059 [ 0. 0. 0. ... -0.11132853 0.10617508 -0. ] Sparsity at: 0.4999497049356223 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9018 - val_loss: 0.8222 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0.11114341 0.1062773 -0. ] Sparsity at: 0.4999497049356223 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8399 - accuracy: 0.9014 - val_loss: 0.8222 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0.11112142 0.10616571 -0. ] Sparsity at: 0.4999497049356223 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9018 - val_loss: 0.8217 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0.111249 0.10646705 -0. ] Sparsity at: 0.4999497049356223 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9016 - val_loss: 0.8218 - val_accuracy: 0.9058 [ 0. 0. 0. ... -0.1113296 0.1065885 0. ] Sparsity at: 0.4999497049356223 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9013 - val_loss: 0.8211 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0.11155467 0.1064709 0. ] Sparsity at: 0.4999497049356223 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9017 - val_loss: 0.8215 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0.11126692 0.10668615 -0. ] Sparsity at: 0.4999497049356223 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8398 - accuracy: 0.9014 - val_loss: 0.8215 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0.11128236 0.10695814 -0. ] Sparsity at: 0.4999497049356223 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9014 - val_loss: 0.8215 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0.11132324 0.10694671 0. ] Sparsity at: 0.4999497049356223 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8397 - accuracy: 0.9015 - val_loss: 0.8213 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0.11114836 0.10676926 -0. ] Sparsity at: 0.4999497049356223 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9018 - val_loss: 0.8214 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0.11114299 0.10683499 -0. ] Sparsity at: 0.4999497049356223 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8395 - accuracy: 0.9014 - val_loss: 0.8217 - val_accuracy: 0.9059 [ 0. 0. 0. ... -0.11142986 0.10724993 -0. ] Sparsity at: 0.4999497049356223 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9015 - val_loss: 0.8217 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0.11111596 0.1068156 -0. ] Sparsity at: 0.4999497049356223 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9018 - val_loss: 0.8212 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0.111177 0.10685202 -0. ] Sparsity at: 0.4999497049356223 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9018 - val_loss: 0.8213 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0.1112776 0.10690131 -0. ] Sparsity at: 0.4999497049356223 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9016 - val_loss: 0.8217 - val_accuracy: 0.9057 [ 0. 0. 0. ... -0.11080883 0.10680526 -0. ] Sparsity at: 0.4999497049356223 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9017 - val_loss: 0.8220 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0.11056922 0.10705034 -0. ] Sparsity at: 0.4999497049356223 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9015 - val_loss: 0.8218 - val_accuracy: 0.9060 [ 0. 0. 0. ... -0.11078779 0.10706759 -0. ] Sparsity at: 0.4999497049356223 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9015 - val_loss: 0.8216 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0.11128186 0.1073451 -0. ] Sparsity at: 0.4999497049356223 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8392 - accuracy: 0.9018 - val_loss: 0.8218 - val_accuracy: 0.9061 [ 0. 0. 0. ... -0.11110184 0.1074997 -0. ] Sparsity at: 0.4999497049356223 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8397 - accuracy: 0.9014 - val_loss: 0.8217 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0.11089122 0.10716727 -0. ] Sparsity at: 0.4999497049356223 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9016 - val_loss: 0.8215 - val_accuracy: 0.9062 [ 0. 0. 0. ... -0.11071569 0.10693131 -0. ] Sparsity at: 0.4999497049356223 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9018 - val_loss: 0.8215 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0.11094666 0.10705218 -0. ] Sparsity at: 0.4999497049356223 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9014 - val_loss: 0.8216 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0.11079706 0.1070861 -0. ] Sparsity at: 0.4999497049356223 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8217 - val_accuracy: 0.9063 [ 0. 0. 0. ... -0.11087454 0.10747892 -0. ] Sparsity at: 0.4999497049356223 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8395 - accuracy: 0.9014 - val_loss: 0.8219 - val_accuracy: 0.9060 [ 0. 0. 0. ... -0.1107095 0.10741054 -0. ] Sparsity at: 0.4999497049356223 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9018 - val_loss: 0.8217 - val_accuracy: 0.9060 [ 0. 0. 0. ... -0.11062576 0.1072721 -0. ] Sparsity at: 0.4999497049356223 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8393 - accuracy: 0.9018 - val_loss: 0.8217 - val_accuracy: 0.9059 [ 0. 0. 0. ... -0.11075323 0.10719405 -0. ] Sparsity at: 0.4999497049356223 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9014 - val_loss: 0.8212 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0.11079308 0.10741761 -0. ] Sparsity at: 0.4999497049356223 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8394 - accuracy: 0.9016 - val_loss: 0.8212 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0.11067689 0.10727011 -0. ] Sparsity at: 0.4999497049356223 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8396 - accuracy: 0.9014 - val_loss: 0.8214 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0.11076469 0.10725372 -0. ] Sparsity at: 0.4999497049356223 Epoch 51/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8702 - accuracy: 0.9013 - val_loss: 0.8455 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 52/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8630 - accuracy: 0.9021 - val_loss: 0.8438 - val_accuracy: 0.9073 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8620 - accuracy: 0.9018 - val_loss: 0.8431 - val_accuracy: 0.9081 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.9021 - val_loss: 0.8432 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.9019 - val_loss: 0.8429 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8612 - accuracy: 0.9020 - val_loss: 0.8426 - val_accuracy: 0.9076 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9020 - val_loss: 0.8427 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8612 - accuracy: 0.9019 - val_loss: 0.8426 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9021 - val_loss: 0.8428 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9016 - val_loss: 0.8424 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8610 - accuracy: 0.9019 - val_loss: 0.8424 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9020 - val_loss: 0.8425 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8610 - accuracy: 0.9017 - val_loss: 0.8427 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8609 - accuracy: 0.9019 - val_loss: 0.8425 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9020 - val_loss: 0.8422 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9019 - val_loss: 0.8424 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9019 - val_loss: 0.8425 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9018 - val_loss: 0.8424 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9018 - val_loss: 0.8422 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9020 - val_loss: 0.8425 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9020 - val_loss: 0.8422 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8607 - accuracy: 0.9022 - val_loss: 0.8423 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9021 - val_loss: 0.8422 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9023 - val_loss: 0.8424 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8608 - accuracy: 0.9020 - val_loss: 0.8423 - val_accuracy: 0.9072 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8422 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9019 - val_loss: 0.8421 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9022 - val_loss: 0.8421 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.9021 - val_loss: 0.8423 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8421 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9074 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9018 - val_loss: 0.8425 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8608 - accuracy: 0.9020 - val_loss: 0.8419 - val_accuracy: 0.9071 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9021 - val_loss: 0.8422 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9021 - val_loss: 0.8423 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9019 - val_loss: 0.8422 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8427 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9018 - val_loss: 0.8420 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9020 - val_loss: 0.8420 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9021 - val_loss: 0.8421 - val_accuracy: 0.9067 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.9019 - val_loss: 0.8421 - val_accuracy: 0.9069 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8605 - accuracy: 0.9018 - val_loss: 0.8422 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.9018 - val_loss: 0.8423 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.6458221566523605 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9021 - val_loss: 0.8424 - val_accuracy: 0.9068 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8604 - accuracy: 0.9019 - val_loss: 0.8420 - val_accuracy: 0.9066 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8606 - accuracy: 0.9020 - val_loss: 0.8422 - val_accuracy: 0.9065 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8605 - accuracy: 0.9019 - val_loss: 0.8419 - val_accuracy: 0.9070 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.9018 - val_loss: 0.8423 - val_accuracy: 0.9064 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.6458221566523605 Epoch 101/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8955 - accuracy: 0.9003 - val_loss: 0.8750 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 102/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8894 - accuracy: 0.9017 - val_loss: 0.8738 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8889 - accuracy: 0.9019 - val_loss: 0.8739 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8885 - accuracy: 0.9018 - val_loss: 0.8734 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8884 - accuracy: 0.9020 - val_loss: 0.8733 - val_accuracy: 0.9034 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8882 - accuracy: 0.9017 - val_loss: 0.8734 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8881 - accuracy: 0.9019 - val_loss: 0.8732 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8881 - accuracy: 0.9014 - val_loss: 0.8730 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8880 - accuracy: 0.9016 - val_loss: 0.8729 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9015 - val_loss: 0.8728 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9016 - val_loss: 0.8730 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9015 - val_loss: 0.8731 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9014 - val_loss: 0.8729 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8879 - accuracy: 0.9017 - val_loss: 0.8729 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9014 - val_loss: 0.8728 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8878 - accuracy: 0.9017 - val_loss: 0.8728 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8730 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8729 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8877 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8878 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8730 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8725 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8729 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9016 - val_loss: 0.8726 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9016 - val_loss: 0.8727 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9016 - val_loss: 0.8728 - val_accuracy: 0.9034 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9014 - val_loss: 0.8727 - val_accuracy: 0.9036 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8727 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8877 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8728 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9020 - val_loss: 0.8727 - val_accuracy: 0.9035 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9019 - val_loss: 0.8726 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8874 - accuracy: 0.9016 - val_loss: 0.8724 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9035 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8725 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9018 - val_loss: 0.8724 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8876 - accuracy: 0.9017 - val_loss: 0.8728 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9018 - val_loss: 0.8726 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9015 - val_loss: 0.8724 - val_accuracy: 0.9033 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9017 - val_loss: 0.8721 - val_accuracy: 0.9032 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9018 - val_loss: 0.8725 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8875 - accuracy: 0.9016 - val_loss: 0.8726 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.7594219420600858 Epoch 151/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9558 - accuracy: 0.8981 - val_loss: 0.9275 - val_accuracy: 0.9014 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9429 - accuracy: 0.9000 - val_loss: 0.9254 - val_accuracy: 0.9020 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9412 - accuracy: 0.9004 - val_loss: 0.9244 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9403 - accuracy: 0.9003 - val_loss: 0.9238 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9398 - accuracy: 0.9007 - val_loss: 0.9233 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9393 - accuracy: 0.9007 - val_loss: 0.9230 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9389 - accuracy: 0.9007 - val_loss: 0.9227 - val_accuracy: 0.9027 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9387 - accuracy: 0.9008 - val_loss: 0.9227 - val_accuracy: 0.9027 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9385 - accuracy: 0.9006 - val_loss: 0.9223 - val_accuracy: 0.9026 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9383 - accuracy: 0.9006 - val_loss: 0.9221 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9382 - accuracy: 0.9006 - val_loss: 0.9222 - val_accuracy: 0.9031 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9381 - accuracy: 0.9005 - val_loss: 0.9219 - val_accuracy: 0.9030 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9380 - accuracy: 0.9007 - val_loss: 0.9220 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9379 - accuracy: 0.9006 - val_loss: 0.9219 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9379 - accuracy: 0.9005 - val_loss: 0.9217 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9378 - accuracy: 0.9004 - val_loss: 0.9217 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9378 - accuracy: 0.9005 - val_loss: 0.9217 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9217 - val_accuracy: 0.9027 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9006 - val_loss: 0.9216 - val_accuracy: 0.9026 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9027 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9006 - val_loss: 0.9216 - val_accuracy: 0.9028 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9006 - val_loss: 0.9215 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9008 - val_loss: 0.9216 - val_accuracy: 0.9022 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9027 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9216 - val_accuracy: 0.9024 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9009 - val_loss: 0.9215 - val_accuracy: 0.9026 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9377 - accuracy: 0.9007 - val_loss: 0.9215 - val_accuracy: 0.9023 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9216 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9215 - val_accuracy: 0.9024 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9215 - val_accuracy: 0.9024 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9022 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9022 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9214 - val_accuracy: 0.9025 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9215 - val_accuracy: 0.9021 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8447726663090128 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9214 - val_accuracy: 0.9023 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9215 - val_accuracy: 0.9023 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9006 - val_loss: 0.9215 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9214 - val_accuracy: 0.9021 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9376 - accuracy: 0.9008 - val_loss: 0.9214 - val_accuracy: 0.9023 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9214 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9376 - accuracy: 0.9007 - val_loss: 0.9213 - val_accuracy: 0.9025 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9375 - accuracy: 0.9008 - val_loss: 0.9213 - val_accuracy: 0.9024 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8447726663090128 Epoch 201/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1231 - accuracy: 0.8529 - val_loss: 1.0860 - val_accuracy: 0.8721 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 202/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0939 - accuracy: 0.8738 - val_loss: 1.0787 - val_accuracy: 0.8735 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0901 - accuracy: 0.8760 - val_loss: 1.0759 - val_accuracy: 0.8759 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0883 - accuracy: 0.8767 - val_loss: 1.0745 - val_accuracy: 0.8771 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0874 - accuracy: 0.8773 - val_loss: 1.0737 - val_accuracy: 0.8778 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0868 - accuracy: 0.8776 - val_loss: 1.0731 - val_accuracy: 0.8780 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0863 - accuracy: 0.8777 - val_loss: 1.0726 - val_accuracy: 0.8783 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0860 - accuracy: 0.8779 - val_loss: 1.0723 - val_accuracy: 0.8780 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0857 - accuracy: 0.8783 - val_loss: 1.0721 - val_accuracy: 0.8787 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0854 - accuracy: 0.8783 - val_loss: 1.0717 - val_accuracy: 0.8787 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0852 - accuracy: 0.8783 - val_loss: 1.0715 - val_accuracy: 0.8790 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0851 - accuracy: 0.8785 - val_loss: 1.0713 - val_accuracy: 0.8793 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0849 - accuracy: 0.8784 - val_loss: 1.0713 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0847 - accuracy: 0.8788 - val_loss: 1.0710 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0846 - accuracy: 0.8786 - val_loss: 1.0709 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0846 - accuracy: 0.8785 - val_loss: 1.0708 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0845 - accuracy: 0.8785 - val_loss: 1.0706 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0843 - accuracy: 0.8786 - val_loss: 1.0706 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0843 - accuracy: 0.8787 - val_loss: 1.0705 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0843 - accuracy: 0.8785 - val_loss: 1.0703 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0842 - accuracy: 0.8787 - val_loss: 1.0702 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0842 - accuracy: 0.8787 - val_loss: 1.0704 - val_accuracy: 0.8799 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0842 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8797 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0841 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8798 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8797 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0841 - accuracy: 0.8788 - val_loss: 1.0703 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8787 - val_loss: 1.0703 - val_accuracy: 0.8798 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8788 - val_loss: 1.0701 - val_accuracy: 0.8791 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8788 - val_loss: 1.0702 - val_accuracy: 0.8794 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0840 - accuracy: 0.8787 - val_loss: 1.0701 - val_accuracy: 0.8797 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 231/500 235/235 [==============================] - 2s 10ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0702 - val_accuracy: 0.8791 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 232/500 235/235 [==============================] - 2s 10ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0701 - val_accuracy: 0.8797 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0702 - val_accuracy: 0.8797 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0701 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0701 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0700 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0699 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0701 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0701 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0700 - val_accuracy: 0.8798 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8786 - val_loss: 1.0701 - val_accuracy: 0.8798 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8790 - val_loss: 1.0700 - val_accuracy: 0.8794 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0838 - accuracy: 0.8786 - val_loss: 1.0700 - val_accuracy: 0.8797 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0700 - val_accuracy: 0.8796 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0701 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0838 - accuracy: 0.8789 - val_loss: 1.0700 - val_accuracy: 0.8793 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8789 - val_loss: 1.0700 - val_accuracy: 0.8791 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8787 - val_loss: 1.0700 - val_accuracy: 0.8799 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0838 - accuracy: 0.8786 - val_loss: 1.0700 - val_accuracy: 0.8792 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0839 - accuracy: 0.8788 - val_loss: 1.0700 - val_accuracy: 0.8795 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059482296137339 Epoch 251/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2216 - accuracy: 0.8592 - val_loss: 1.1620 - val_accuracy: 0.8758 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 252/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1800 - accuracy: 0.8737 - val_loss: 1.1565 - val_accuracy: 0.8787 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1766 - accuracy: 0.8749 - val_loss: 1.1546 - val_accuracy: 0.8801 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1752 - accuracy: 0.8752 - val_loss: 1.1535 - val_accuracy: 0.8817 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1744 - accuracy: 0.8752 - val_loss: 1.1526 - val_accuracy: 0.8819 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1736 - accuracy: 0.8752 - val_loss: 1.1517 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1729 - accuracy: 0.8753 - val_loss: 1.1510 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1724 - accuracy: 0.8754 - val_loss: 1.1505 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1722 - accuracy: 0.8753 - val_loss: 1.1503 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1720 - accuracy: 0.8754 - val_loss: 1.1500 - val_accuracy: 0.8829 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1718 - accuracy: 0.8753 - val_loss: 1.1499 - val_accuracy: 0.8828 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1717 - accuracy: 0.8752 - val_loss: 1.1498 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1717 - accuracy: 0.8750 - val_loss: 1.1497 - val_accuracy: 0.8828 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1716 - accuracy: 0.8751 - val_loss: 1.1496 - val_accuracy: 0.8827 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1715 - accuracy: 0.8752 - val_loss: 1.1496 - val_accuracy: 0.8829 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1715 - accuracy: 0.8751 - val_loss: 1.1495 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8752 - val_loss: 1.1495 - val_accuracy: 0.8825 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8751 - val_loss: 1.1494 - val_accuracy: 0.8825 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8751 - val_loss: 1.1494 - val_accuracy: 0.8827 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1714 - accuracy: 0.8751 - val_loss: 1.1494 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8752 - val_loss: 1.1493 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8827 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1494 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8822 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8753 - val_loss: 1.1493 - val_accuracy: 0.8825 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1493 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 282/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8824 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469722371244635 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8825 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1713 - accuracy: 0.8751 - val_loss: 1.1493 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1713 - accuracy: 0.8750 - val_loss: 1.1492 - val_accuracy: 0.8824 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469722371244635 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8749 - val_loss: 1.1492 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8749 - val_loss: 1.1492 - val_accuracy: 0.8824 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469722371244635 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8825 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469722371244635 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8752 - val_loss: 1.1492 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8751 - val_loss: 1.1492 - val_accuracy: 0.8827 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1492 - val_accuracy: 0.8825 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469722371244635 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1491 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1712 - accuracy: 0.8750 - val_loss: 1.1491 - val_accuracy: 0.8824 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1711 - accuracy: 0.8750 - val_loss: 1.1490 - val_accuracy: 0.8826 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1710 - accuracy: 0.8750 - val_loss: 1.1489 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1710 - accuracy: 0.8748 - val_loss: 1.1489 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1709 - accuracy: 0.8749 - val_loss: 1.1488 - val_accuracy: 0.8820 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1709 - accuracy: 0.8750 - val_loss: 1.1487 - val_accuracy: 0.8823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469722371244635 Epoch 301/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5324 - accuracy: 0.6888 - val_loss: 1.4737 - val_accuracy: 0.6974 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 302/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4811 - accuracy: 0.6967 - val_loss: 1.4623 - val_accuracy: 0.6987 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4755 - accuracy: 0.6970 - val_loss: 1.4595 - val_accuracy: 0.6988 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4737 - accuracy: 0.6950 - val_loss: 1.4583 - val_accuracy: 0.6989 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4729 - accuracy: 0.6945 - val_loss: 1.4577 - val_accuracy: 0.6985 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4726 - accuracy: 0.6953 - val_loss: 1.4574 - val_accuracy: 0.6991 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4723 - accuracy: 0.6948 - val_loss: 1.4571 - val_accuracy: 0.6991 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4721 - accuracy: 0.6949 - val_loss: 1.4570 - val_accuracy: 0.6989 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4720 - accuracy: 0.6949 - val_loss: 1.4568 - val_accuracy: 0.6990 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4719 - accuracy: 0.6950 - val_loss: 1.4567 - val_accuracy: 0.6989 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4718 - accuracy: 0.6953 - val_loss: 1.4566 - val_accuracy: 0.6990 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4718 - accuracy: 0.6955 - val_loss: 1.4566 - val_accuracy: 0.6989 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4717 - accuracy: 0.6949 - val_loss: 1.4565 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4717 - accuracy: 0.6951 - val_loss: 1.4564 - val_accuracy: 0.6989 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 315/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4716 - accuracy: 0.6951 - val_loss: 1.4564 - val_accuracy: 0.6989 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4716 - accuracy: 0.6950 - val_loss: 1.4563 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4716 - accuracy: 0.6952 - val_loss: 1.4563 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4716 - accuracy: 0.6950 - val_loss: 1.4563 - val_accuracy: 0.6995 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6952 - val_loss: 1.4563 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6957 - val_loss: 1.4562 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6956 - val_loss: 1.4562 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6953 - val_loss: 1.4562 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6953 - val_loss: 1.4562 - val_accuracy: 0.6995 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6950 - val_loss: 1.4562 - val_accuracy: 0.6995 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6951 - val_loss: 1.4562 - val_accuracy: 0.6996 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6949 - val_loss: 1.4562 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6952 - val_loss: 1.4561 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6951 - val_loss: 1.4561 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6953 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6955 - val_loss: 1.4561 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6953 - val_loss: 1.4561 - val_accuracy: 0.6995 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4714 - accuracy: 0.6954 - val_loss: 1.4561 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6956 - val_loss: 1.4561 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4560 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6952 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4714 - accuracy: 0.6951 - val_loss: 1.4561 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6992 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4715 - accuracy: 0.6951 - val_loss: 1.4560 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4561 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6948 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6947 - val_loss: 1.4561 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6950 - val_loss: 1.4561 - val_accuracy: 0.6994 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4714 - accuracy: 0.6949 - val_loss: 1.4561 - val_accuracy: 0.6993 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718347639484979 Epoch 351/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7748 - accuracy: 0.5517 - val_loss: 1.7227 - val_accuracy: 0.5577 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 352/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7197 - accuracy: 0.5532 - val_loss: 1.7108 - val_accuracy: 0.5407 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.5449 - val_loss: 1.7082 - val_accuracy: 0.5410 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7124 - accuracy: 0.5429 - val_loss: 1.7072 - val_accuracy: 0.5412 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.5431 - val_loss: 1.7068 - val_accuracy: 0.5417 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.5433 - val_loss: 1.7065 - val_accuracy: 0.5421 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7112 - accuracy: 0.5433 - val_loss: 1.7063 - val_accuracy: 0.5422 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7110 - accuracy: 0.5435 - val_loss: 1.7062 - val_accuracy: 0.5425 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7110 - accuracy: 0.5435 - val_loss: 1.7061 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.5436 - val_loss: 1.7060 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.5437 - val_loss: 1.7060 - val_accuracy: 0.5425 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.5437 - val_loss: 1.7059 - val_accuracy: 0.5427 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5436 - val_loss: 1.7059 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5436 - val_loss: 1.7058 - val_accuracy: 0.5427 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7107 - accuracy: 0.5437 - val_loss: 1.7058 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5427 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5431 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5431 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5431 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5440 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5440 - val_loss: 1.7058 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5440 - val_loss: 1.7058 - val_accuracy: 0.5431 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5437 - val_loss: 1.7057 - val_accuracy: 0.5430 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5439 - val_loss: 1.7058 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7057 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7106 - accuracy: 0.5438 - val_loss: 1.7058 - val_accuracy: 0.5429 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9843414699570815 Epoch 401/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8909 - accuracy: 0.4116 - val_loss: 1.8557 - val_accuracy: 0.4074 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9891362660944206 Epoch 402/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8576 - accuracy: 0.4144 - val_loss: 1.8469 - val_accuracy: 0.4565 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9891362660944206 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8537 - accuracy: 0.4536 - val_loss: 1.8450 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8529 - accuracy: 0.4533 - val_loss: 1.8443 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8526 - accuracy: 0.4534 - val_loss: 1.8441 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8524 - accuracy: 0.4533 - val_loss: 1.8439 - val_accuracy: 0.4563 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8523 - accuracy: 0.4533 - val_loss: 1.8438 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8523 - accuracy: 0.4534 - val_loss: 1.8437 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8522 - accuracy: 0.4533 - val_loss: 1.8437 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8522 - accuracy: 0.4532 - val_loss: 1.8436 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8436 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8436 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8521 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4563 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4533 - val_loss: 1.8435 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4559 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4563 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4563 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4533 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4559 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 465/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4563 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 479/500 235/235 [==============================] - 2s 10ms/step - loss: 1.8520 - accuracy: 0.4531 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4533 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4560 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4559 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4561 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8520 - accuracy: 0.4532 - val_loss: 1.8435 - val_accuracy: 0.4562 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9891362660944206 Epoch 1/500 235/235 [==============================] - 4s 9ms/step - loss: 0.0028 - accuracy: 0.9990 - val_loss: 0.2508 - val_accuracy: 0.9731 [-0. 0. -0. ... -0.880246 0. -0. ] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5839e-04 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9748 [-0. 0. -0. ... -0.88338655 0. -0. ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6287e-05 - accuracy: 1.0000 - val_loss: 0.2439 - val_accuracy: 0.9750 [-0. 0. -0. ... -0.88420767 -0. -0. ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0862e-05 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.88462055 -0. -0. ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6462e-05 - accuracy: 1.0000 - val_loss: 0.2430 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.88488364 -0. -0. ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3971e-05 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.88519484 -0. -0. ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2152e-05 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9751 [-0. 0. -0. ... -0.8855181 -0. -0. ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0740e-05 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9752 [-0. 0. -0. ... -0.8858649 -0. -0. ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 9.6042e-06 - accuracy: 1.0000 - val_loss: 0.2421 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.8862164 -0. -0. ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 8.6602e-06 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.88663924 -0. -0. ] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8596e-06 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8870965 -0. -0. ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1681e-06 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.88758665 -0. -0. ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5653e-06 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.8880845 -0. -0. ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0327e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8886213 -0. -0. ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5587e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8892315 -0. -0. ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1293e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8898362 -0. -0. ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7440e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8904798 -0. -0. ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3919e-06 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.891201 -0. -0. ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0704e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8919127 -0. -0. ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7760e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8927047 0. -0. ] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5044e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.8934853 -0. -0. ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2548e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.89430565 0. -0. ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0236e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8951802 0. -0. ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8083e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.8961327 0. -0. ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6101e-06 - accuracy: 1.0000 - val_loss: 0.2408 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.8970646 0. -0. ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4256e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.8980432 0. -0. ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2550e-06 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.8991141 0. -0. ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0949e-06 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9757 [-0. 0. -0. ... -0.900172 0. -0. ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9475e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9757 [-0. 0. -0. ... -0.9013244 0. -0. ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8090e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9757 [-0. 0. -0. ... -0.90249956 0. -0. ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6800e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9758 [-0. 0. -0. ... -0.90368205 0. -0. ] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5605e-06 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.9049617 0. -0. ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4485e-06 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.90627617 0. -0. ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3443e-06 - accuracy: 1.0000 - val_loss: 0.2420 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.9076231 0. -0. ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2475e-06 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.9090028 0. -0. ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1572e-06 - accuracy: 1.0000 - val_loss: 0.2426 - val_accuracy: 0.9757 [-0. 0. -0. ... -0.9104391 0. -0. ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0728e-06 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9758 [-0. 0. -0. ... -0.91196615 0. -0. ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9442e-07 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9763 [-0. 0. -0. ... -0.9135096 0. -0. ] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 9.2161e-07 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9762 [-0. 0. -0. ... -0.9151253 0. -0. ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5330e-07 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9168575 0. -0. ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8998e-07 - accuracy: 1.0000 - val_loss: 0.2444 - val_accuracy: 0.9760 [-0. 0. -0. ... -0.91864526 0. -0. ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3102e-07 - accuracy: 1.0000 - val_loss: 0.2449 - val_accuracy: 0.9760 [-0. 0. -0. ... -0.9204894 0. -0. ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7575e-07 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9223955 0. -0. ] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2511e-07 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.92433524 0. 0. ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7775e-07 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9263523 0. 0. ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3357e-07 - accuracy: 1.0000 - val_loss: 0.2470 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9284324 0. 0. ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9279e-07 - accuracy: 1.0000 - val_loss: 0.2476 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9305882 0. -0. ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5467e-07 - accuracy: 1.0000 - val_loss: 0.2481 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9328444 0. 0. ] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1975e-07 - accuracy: 1.0000 - val_loss: 0.2488 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.9351129 0. 0. ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8673e-07 - accuracy: 1.0000 - val_loss: 0.2494 - val_accuracy: 0.9761 [-0. 0. -0. ... -0.93748915 0. 0. ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0220 - accuracy: 0.9937 - val_loss: 0.2050 - val_accuracy: 0.9721 [-0. 0. -0. ... -0.9197833 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 52/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 0.1972 - val_accuracy: 0.9746 [-0. 0. -0. ... -0.92911404 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4271e-04 - accuracy: 0.9999 - val_loss: 0.1923 - val_accuracy: 0.9743 [-0. 0. -0. ... -0.93192124 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7807e-04 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9742 [-0. 0. -0. ... -0.9324415 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9726e-04 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9743 [-0. 0. -0. ... -0.93300086 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7012e-04 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9744 [-0. 0. -0. ... -0.93357474 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5169e-04 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9744 [-0. 0. -0. ... -0.93418986 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3714e-04 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9744 [-0. 0. -0. ... -0.9348854 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2514e-04 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9747 [-0. 0. -0. ... -0.93563855 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1471e-04 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9746 [-0. 0. -0. ... -0.9364736 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0559e-04 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9746 [-0. 0. -0. ... -0.9373931 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7388e-05 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9747 [-0. 0. -0. ... -0.9383922 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0047e-05 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9747 [-0. 0. -0. ... -0.9394359 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3303e-05 - accuracy: 1.0000 - val_loss: 0.1951 - val_accuracy: 0.9750 [-0. 0. -0. ... -0.94056064 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7102e-05 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9751 [-0. 0. -0. ... -0.94178057 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1415e-05 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9752 [-0. 0. -0. ... -0.9430806 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6151e-05 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9752 [-0. 0. -0. ... -0.94446164 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1270e-05 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9751 [-0. 0. -0. ... -0.9459038 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6698e-05 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.94743764 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2444e-05 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.94902414 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8513e-05 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.95073 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4804e-05 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.9524606 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1370e-05 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9753 [-0. 0. -0. ... -0.95430547 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8158e-05 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.9562299 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5170e-05 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9754 [-0. 0. -0. ... -0.95825326 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2361e-05 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9755 [-0. 0. -0. ... -0.96038073 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9773e-05 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9752 [-0. 0. -0. ... -0.96263814 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7366e-05 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9752 [-0. 0. -0. ... -0.9649859 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5115e-05 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9751 [-0. 0. -0. ... -0.96739435 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3035e-05 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9750 [-0. 0. -0. ... -0.9699126 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1112e-05 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9749 [-0. 0. -0. ... -0.97255224 0. 0. ] Sparsity at: 0.6458724517167382 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9329e-05 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9750 [-0. 0. -0. ... -0.9753028 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7664e-05 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9751 [-0. 0. -0. ... -0.97817564 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6130e-05 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9750 [-0. 0. -0. ... -0.9811863 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4737e-05 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9749 [-0. 0. -0. ... -0.9843151 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3429e-05 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9749 [-0. 0. -0. ... -0.98756605 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2235e-05 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9749 [-0. 0. -0. ... -0.99092084 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1129e-05 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9749 [-0. 0. -0. ... -0.994429 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0126e-05 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9751 [-0. 0. -0. ... -0.99803513 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 9.1957e-06 - accuracy: 1.0000 - val_loss: 0.2154 - val_accuracy: 0.9751 [-0. 0. -0. ... -1.0017804 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3395e-06 - accuracy: 1.0000 - val_loss: 0.2166 - val_accuracy: 0.9751 [-0. 0. -0. ... -1.0055803 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 7.5572e-06 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9750 [-0. 0. -0. ... -1.0094881 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8534e-06 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9750 [-0. 0. -0. ... -1.0135279 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1955e-06 - accuracy: 1.0000 - val_loss: 0.2203 - val_accuracy: 0.9749 [-0. 0. -0. ... -1.0176321 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6008e-06 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9748 [-0. 0. -0. ... -1.0218481 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0619e-06 - accuracy: 1.0000 - val_loss: 0.2229 - val_accuracy: 0.9749 [-0. 0. -0. ... -1.0262133 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5736e-06 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9749 [-0. 0. -0. ... -1.0305617 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1190e-06 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9752 [-0. 0. -0. ... -1.0350757 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7181e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9752 [-0. 0. -0. ... -1.0395976 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3489e-06 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.9752 [-0. 0. -0. ... -1.044248 0. -0. ] Sparsity at: 0.6458724517167382 Epoch 101/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0431 - accuracy: 0.9875 - val_loss: 0.1635 - val_accuracy: 0.9720 [-0. 0. -0. ... -1.0806397 0. -0. ] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.1573 - val_accuracy: 0.9738 [-0. 0. -0. ... -1.0674495 0. 0. ] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9987 - val_loss: 0.1561 - val_accuracy: 0.9739 [-0. 0. -0. ... -1.067485 0. 0. ] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.1568 - val_accuracy: 0.9741 [-0. 0. -0. ... -1.0717487 0. 0. ] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1573 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.0771368 0. 0. ] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 0.9999 - val_loss: 0.1583 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.0827911 0. 0. ] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.0886633 0. 0. ] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1601 - val_accuracy: 0.9743 [-0. 0. -0. ... -1.0946239 0. 0. ] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1608 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.100825 0. 0. ] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1616 - val_accuracy: 0.9743 [-0. 0. -0. ... -1.1069539 0. 0. ] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5225e-04 - accuracy: 1.0000 - val_loss: 0.1623 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.1135459 0. 0. ] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5302e-04 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9743 [-0. 0. -0. ... -1.1199894 0. 0. ] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6585e-04 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.1266072 0. 0. ] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9055e-04 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.1335133 0. 0. ] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2432e-04 - accuracy: 1.0000 - val_loss: 0.1659 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.1405433 0. 0. ] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6487e-04 - accuracy: 1.0000 - val_loss: 0.1669 - val_accuracy: 0.9746 [-0. 0. -0. ... -1.1476506 0. 0. ] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1332e-04 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.1551005 0. 0. ] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6562e-04 - accuracy: 1.0000 - val_loss: 0.1687 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.1628269 0. 0. ] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2392e-04 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.1707535 0. 0. ] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8579e-04 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.1787006 0. 0. ] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5043e-04 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.1869619 0. 0. ] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1863e-04 - accuracy: 1.0000 - val_loss: 0.1729 - val_accuracy: 0.9743 [-0. 0. -0. ... -1.1952964 0. 0. ] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9000e-04 - accuracy: 1.0000 - val_loss: 0.1740 - val_accuracy: 0.9743 [-0. 0. -0. ... -1.2040031 0. 0. ] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6367e-04 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.2128332 0. 0. ] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4042e-04 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.2215818 0. 0. ] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1740e-04 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.2306157 0. 0. ] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9742e-04 - accuracy: 1.0000 - val_loss: 0.1789 - val_accuracy: 0.9743 [-0. 0. -0. ... -1.2397698 0. 0. ] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7974e-04 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.2489645 0. 0. ] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6289e-04 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.2582648 0. 0. ] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4785e-04 - accuracy: 1.0000 - val_loss: 0.1829 - val_accuracy: 0.9746 [-0. 0. -0. ... -1.2677523 0. 0. ] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3371e-04 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9746 [-0. 0. -0. ... -1.2773597 0. 0. ] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2098e-04 - accuracy: 1.0000 - val_loss: 0.1857 - val_accuracy: 0.9742 [-0. 0. -0. ... -1.2868885 0. 0. ] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0975e-04 - accuracy: 1.0000 - val_loss: 0.1873 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.2965337 0. 0. ] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 9.9275e-05 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.306352 0. 0. ] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 8.9800e-05 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.3162463 0. 0. ] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 8.1073e-05 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9746 [-0. 0. -0. ... -1.3262224 0. 0. ] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3143e-05 - accuracy: 1.0000 - val_loss: 0.1935 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.3364494 0. 0. ] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 6.6010e-05 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.3465014 0. 0. ] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9599e-05 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.3567601 0. 0. ] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3671e-05 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9748 [-0. 0. -0. ... -1.3667188 0. 0. ] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8388e-05 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.3769611 0. 0. ] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3653e-05 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.3872551 0. 0. ] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9302e-05 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9747 [-0. 0. -0. ... -1.3972656 0. 0. ] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5376e-05 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.4078622 0. 0. ] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1764e-05 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.4180739 0. 0. ] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8552e-05 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.4283988 0. 0. ] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5641e-05 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9744 [-0. 0. -0. ... -1.4384574 0. 0. ] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3114e-05 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9746 [-0. 0. -0. ... -1.4489262 0. -0. ] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0759e-05 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.4591879 0. 0. ] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8644e-05 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9745 [-0. 0. -0. ... -1.4693817 0. 0. ] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0921 - accuracy: 0.9755 - val_loss: 0.1920 - val_accuracy: 0.9658 [-0. 0. -0. ... -1.3002248 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0358 - accuracy: 0.9883 - val_loss: 0.1806 - val_accuracy: 0.9672 [-0. 0. -0. ... -1.2823541 -0. -0. ] Sparsity at: 0.8448229613733905 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0259 - accuracy: 0.9911 - val_loss: 0.1750 - val_accuracy: 0.9680 [-0. 0. -0. ... -1.2756494 -0. -0. ] Sparsity at: 0.8448229613733905 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0206 - accuracy: 0.9931 - val_loss: 0.1719 - val_accuracy: 0.9686 [-0. 0. -0. ... -1.2714455 -0. -0. ] Sparsity at: 0.8448229613733905 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0172 - accuracy: 0.9944 - val_loss: 0.1697 - val_accuracy: 0.9689 [-0. 0. -0. ... -1.2680866 -0. -0. ] Sparsity at: 0.8448229613733905 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.1683 - val_accuracy: 0.9691 [-0. 0. -0. ... -1.2659676 -0. -0. ] Sparsity at: 0.8448229613733905 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0128 - accuracy: 0.9963 - val_loss: 0.1672 - val_accuracy: 0.9697 [-0. 0. -0. ... -1.2656113 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0113 - accuracy: 0.9969 - val_loss: 0.1666 - val_accuracy: 0.9700 [-0. 0. -0. ... -1.2666739 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0101 - accuracy: 0.9974 - val_loss: 0.1662 - val_accuracy: 0.9700 [-0. 0. -0. ... -1.2690679 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0091 - accuracy: 0.9980 - val_loss: 0.1660 - val_accuracy: 0.9699 [-0. 0. -0. ... -1.2734616 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 0.1661 - val_accuracy: 0.9700 [-0. 0. -0. ... -1.2793527 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0076 - accuracy: 0.9986 - val_loss: 0.1662 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.2858511 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0069 - accuracy: 0.9989 - val_loss: 0.1665 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.2927115 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0064 - accuracy: 0.9991 - val_loss: 0.1669 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.3002685 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0059 - accuracy: 0.9993 - val_loss: 0.1673 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.3080535 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0054 - accuracy: 0.9995 - val_loss: 0.1679 - val_accuracy: 0.9707 [-0. 0. -0. ... -1.315829 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0050 - accuracy: 0.9997 - val_loss: 0.1686 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.3236148 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9997 - val_loss: 0.1693 - val_accuracy: 0.9704 [-0. 0. -0. ... -1.3319994 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9998 - val_loss: 0.1702 - val_accuracy: 0.9702 [-0. 0. -0. ... -1.3405224 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0040 - accuracy: 0.9999 - val_loss: 0.1710 - val_accuracy: 0.9702 [-0. 0. -0. ... -1.3489258 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0037 - accuracy: 0.9999 - val_loss: 0.1719 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.3582197 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9999 - val_loss: 0.1729 - val_accuracy: 0.9704 [-0. 0. -0. ... -1.3671787 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9999 - val_loss: 0.1739 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.3767401 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0030 - accuracy: 0.9999 - val_loss: 0.1750 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.3861424 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.3957092 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1774 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.405351 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1785 - val_accuracy: 0.9704 [-0. 0. -0. ... -1.4152862 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1798 - val_accuracy: 0.9701 [-0. 0. -0. ... -1.4253176 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.435876 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9702 [-0. 0. -0. ... -1.4465294 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.457261 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.4676903 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.4788667 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9707 [-0. 0. -0. ... -1.4904815 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9707 [-0. 0. -0. ... -1.5023797 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.514085 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9708 [-0. 0. -0. ... -1.5256187 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9707 [-0. 0. -0. ... -1.5371948 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.549159 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9708 [-0. 0. -0. ... -1.5611707 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3864e-04 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9706 [-0. 0. -0. ... -1.5731853 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7673e-04 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.585135 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2159e-04 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.597745 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6858e-04 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9704 [-0. 0. -0. ... -1.6100304 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1607e-04 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9705 [-0. 0. -0. ... -1.6226298 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6920e-04 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.6350399 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2534e-04 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.6470389 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 5.8364e-04 - accuracy: 1.0000 - val_loss: 0.2125 - val_accuracy: 0.9704 [-0. 0. -0. ... -1.6594774 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 5.4626e-04 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.6723495 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0987e-04 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.9703 [-0. 0. -0. ... -1.6844566 -0. 0. ] Sparsity at: 0.8448229613733905 Epoch 201/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1797 - accuracy: 0.9490 - val_loss: 0.1993 - val_accuracy: 0.9515 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 202/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1006 - accuracy: 0.9682 - val_loss: 0.1783 - val_accuracy: 0.9553 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0850 - accuracy: 0.9722 - val_loss: 0.1676 - val_accuracy: 0.9572 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0764 - accuracy: 0.9750 - val_loss: 0.1606 - val_accuracy: 0.9587 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0705 - accuracy: 0.9767 - val_loss: 0.1556 - val_accuracy: 0.9599 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0661 - accuracy: 0.9783 - val_loss: 0.1517 - val_accuracy: 0.9612 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0626 - accuracy: 0.9796 - val_loss: 0.1488 - val_accuracy: 0.9614 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0597 - accuracy: 0.9806 - val_loss: 0.1464 - val_accuracy: 0.9615 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0573 - accuracy: 0.9814 - val_loss: 0.1443 - val_accuracy: 0.9620 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0551 - accuracy: 0.9819 - val_loss: 0.1426 - val_accuracy: 0.9621 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0533 - accuracy: 0.9824 - val_loss: 0.1412 - val_accuracy: 0.9623 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0516 - accuracy: 0.9830 - val_loss: 0.1400 - val_accuracy: 0.9625 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0501 - accuracy: 0.9834 - val_loss: 0.1389 - val_accuracy: 0.9629 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0487 - accuracy: 0.9840 - val_loss: 0.1381 - val_accuracy: 0.9633 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0475 - accuracy: 0.9844 - val_loss: 0.1373 - val_accuracy: 0.9638 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0464 - accuracy: 0.9848 - val_loss: 0.1367 - val_accuracy: 0.9637 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0453 - accuracy: 0.9851 - val_loss: 0.1361 - val_accuracy: 0.9643 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0443 - accuracy: 0.9855 - val_loss: 0.1356 - val_accuracy: 0.9644 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 219/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0434 - accuracy: 0.9859 - val_loss: 0.1352 - val_accuracy: 0.9648 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 220/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0426 - accuracy: 0.9861 - val_loss: 0.1349 - val_accuracy: 0.9648 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0418 - accuracy: 0.9864 - val_loss: 0.1347 - val_accuracy: 0.9652 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0410 - accuracy: 0.9867 - val_loss: 0.1345 - val_accuracy: 0.9652 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0403 - accuracy: 0.9870 - val_loss: 0.1344 - val_accuracy: 0.9650 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0396 - accuracy: 0.9872 - val_loss: 0.1343 - val_accuracy: 0.9648 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0390 - accuracy: 0.9873 - val_loss: 0.1343 - val_accuracy: 0.9650 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0384 - accuracy: 0.9877 - val_loss: 0.1344 - val_accuracy: 0.9651 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0378 - accuracy: 0.9878 - val_loss: 0.1344 - val_accuracy: 0.9652 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0372 - accuracy: 0.9880 - val_loss: 0.1345 - val_accuracy: 0.9653 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0367 - accuracy: 0.9882 - val_loss: 0.1347 - val_accuracy: 0.9652 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0362 - accuracy: 0.9884 - val_loss: 0.1348 - val_accuracy: 0.9653 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0357 - accuracy: 0.9885 - val_loss: 0.1351 - val_accuracy: 0.9652 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0352 - accuracy: 0.9890 - val_loss: 0.1353 - val_accuracy: 0.9654 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0348 - accuracy: 0.9891 - val_loss: 0.1355 - val_accuracy: 0.9656 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0344 - accuracy: 0.9893 - val_loss: 0.1359 - val_accuracy: 0.9656 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0339 - accuracy: 0.9894 - val_loss: 0.1362 - val_accuracy: 0.9656 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0335 - accuracy: 0.9897 - val_loss: 0.1365 - val_accuracy: 0.9657 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0331 - accuracy: 0.9898 - val_loss: 0.1368 - val_accuracy: 0.9658 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0327 - accuracy: 0.9900 - val_loss: 0.1372 - val_accuracy: 0.9659 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0324 - accuracy: 0.9901 - val_loss: 0.1376 - val_accuracy: 0.9660 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0320 - accuracy: 0.9902 - val_loss: 0.1380 - val_accuracy: 0.9663 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0317 - accuracy: 0.9903 - val_loss: 0.1385 - val_accuracy: 0.9663 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0313 - accuracy: 0.9905 - val_loss: 0.1388 - val_accuracy: 0.9662 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0310 - accuracy: 0.9906 - val_loss: 0.1393 - val_accuracy: 0.9662 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0307 - accuracy: 0.9908 - val_loss: 0.1398 - val_accuracy: 0.9662 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0304 - accuracy: 0.9908 - val_loss: 0.1402 - val_accuracy: 0.9661 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0301 - accuracy: 0.9909 - val_loss: 0.1407 - val_accuracy: 0.9661 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0298 - accuracy: 0.9911 - val_loss: 0.1412 - val_accuracy: 0.9659 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0295 - accuracy: 0.9912 - val_loss: 0.1416 - val_accuracy: 0.9657 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0292 - accuracy: 0.9913 - val_loss: 0.1422 - val_accuracy: 0.9657 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0289 - accuracy: 0.9914 - val_loss: 0.1427 - val_accuracy: 0.9656 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 251/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4640 - accuracy: 0.8569 - val_loss: 0.3334 - val_accuracy: 0.8991 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 252/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2711 - accuracy: 0.9116 - val_loss: 0.2809 - val_accuracy: 0.9156 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2373 - accuracy: 0.9226 - val_loss: 0.2586 - val_accuracy: 0.9203 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2195 - accuracy: 0.9280 - val_loss: 0.2453 - val_accuracy: 0.9253 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2078 - accuracy: 0.9323 - val_loss: 0.2361 - val_accuracy: 0.9278 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1991 - accuracy: 0.9352 - val_loss: 0.2292 - val_accuracy: 0.9292 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1924 - accuracy: 0.9372 - val_loss: 0.2237 - val_accuracy: 0.9312 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1870 - accuracy: 0.9393 - val_loss: 0.2193 - val_accuracy: 0.9325 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1825 - accuracy: 0.9408 - val_loss: 0.2156 - val_accuracy: 0.9331 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1786 - accuracy: 0.9419 - val_loss: 0.2124 - val_accuracy: 0.9339 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1753 - accuracy: 0.9434 - val_loss: 0.2096 - val_accuracy: 0.9351 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1724 - accuracy: 0.9444 - val_loss: 0.2072 - val_accuracy: 0.9355 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1698 - accuracy: 0.9452 - val_loss: 0.2050 - val_accuracy: 0.9364 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1676 - accuracy: 0.9459 - val_loss: 0.2031 - val_accuracy: 0.9381 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1655 - accuracy: 0.9467 - val_loss: 0.2014 - val_accuracy: 0.9390 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1637 - accuracy: 0.9475 - val_loss: 0.1998 - val_accuracy: 0.9392 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1620 - accuracy: 0.9482 - val_loss: 0.1984 - val_accuracy: 0.9391 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1605 - accuracy: 0.9488 - val_loss: 0.1971 - val_accuracy: 0.9394 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1590 - accuracy: 0.9489 - val_loss: 0.1959 - val_accuracy: 0.9398 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1577 - accuracy: 0.9495 - val_loss: 0.1949 - val_accuracy: 0.9400 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1565 - accuracy: 0.9497 - val_loss: 0.1939 - val_accuracy: 0.9402 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1554 - accuracy: 0.9501 - val_loss: 0.1930 - val_accuracy: 0.9401 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1544 - accuracy: 0.9503 - val_loss: 0.1922 - val_accuracy: 0.9409 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1533 - accuracy: 0.9509 - val_loss: 0.1914 - val_accuracy: 0.9410 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.1907 - val_accuracy: 0.9412 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1516 - accuracy: 0.9513 - val_loss: 0.1901 - val_accuracy: 0.9414 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1507 - accuracy: 0.9517 - val_loss: 0.1895 - val_accuracy: 0.9415 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1499 - accuracy: 0.9519 - val_loss: 0.1890 - val_accuracy: 0.9415 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1492 - accuracy: 0.9522 - val_loss: 0.1885 - val_accuracy: 0.9418 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1485 - accuracy: 0.9524 - val_loss: 0.1880 - val_accuracy: 0.9419 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1478 - accuracy: 0.9526 - val_loss: 0.1876 - val_accuracy: 0.9422 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1472 - accuracy: 0.9527 - val_loss: 0.1872 - val_accuracy: 0.9427 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1466 - accuracy: 0.9529 - val_loss: 0.1868 - val_accuracy: 0.9430 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1460 - accuracy: 0.9530 - val_loss: 0.1864 - val_accuracy: 0.9433 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1455 - accuracy: 0.9532 - val_loss: 0.1861 - val_accuracy: 0.9435 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1449 - accuracy: 0.9538 - val_loss: 0.1858 - val_accuracy: 0.9435 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1444 - accuracy: 0.9539 - val_loss: 0.1855 - val_accuracy: 0.9436 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1439 - accuracy: 0.9542 - val_loss: 0.1853 - val_accuracy: 0.9439 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1434 - accuracy: 0.9544 - val_loss: 0.1851 - val_accuracy: 0.9439 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1430 - accuracy: 0.9544 - val_loss: 0.1848 - val_accuracy: 0.9434 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1426 - accuracy: 0.9546 - val_loss: 0.1846 - val_accuracy: 0.9437 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1422 - accuracy: 0.9546 - val_loss: 0.1845 - val_accuracy: 0.9439 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1417 - accuracy: 0.9548 - val_loss: 0.1843 - val_accuracy: 0.9442 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1413 - accuracy: 0.9549 - val_loss: 0.1841 - val_accuracy: 0.9444 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1410 - accuracy: 0.9550 - val_loss: 0.1840 - val_accuracy: 0.9448 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1406 - accuracy: 0.9552 - val_loss: 0.1838 - val_accuracy: 0.9447 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1402 - accuracy: 0.9553 - val_loss: 0.1837 - val_accuracy: 0.9448 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1399 - accuracy: 0.9554 - val_loss: 0.1835 - val_accuracy: 0.9449 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1395 - accuracy: 0.9555 - val_loss: 0.1834 - val_accuracy: 0.9447 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1392 - accuracy: 0.9556 - val_loss: 0.1833 - val_accuracy: 0.9449 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 301/500 235/235 [==============================] - 2s 8ms/step - loss: 0.7708 - accuracy: 0.7487 - val_loss: 0.6593 - val_accuracy: 0.8015 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 302/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6307 - accuracy: 0.8051 - val_loss: 0.6132 - val_accuracy: 0.8166 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5977 - accuracy: 0.8188 - val_loss: 0.5929 - val_accuracy: 0.8244 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5806 - accuracy: 0.8247 - val_loss: 0.5803 - val_accuracy: 0.8277 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5687 - accuracy: 0.8275 - val_loss: 0.5709 - val_accuracy: 0.8307 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5595 - accuracy: 0.8300 - val_loss: 0.5634 - val_accuracy: 0.8324 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5517 - accuracy: 0.8321 - val_loss: 0.5570 - val_accuracy: 0.8339 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5451 - accuracy: 0.8340 - val_loss: 0.5514 - val_accuracy: 0.8352 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5393 - accuracy: 0.8353 - val_loss: 0.5466 - val_accuracy: 0.8363 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5342 - accuracy: 0.8365 - val_loss: 0.5424 - val_accuracy: 0.8374 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5299 - accuracy: 0.8375 - val_loss: 0.5388 - val_accuracy: 0.8382 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5261 - accuracy: 0.8384 - val_loss: 0.5357 - val_accuracy: 0.8396 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5228 - accuracy: 0.8391 - val_loss: 0.5328 - val_accuracy: 0.8400 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5199 - accuracy: 0.8395 - val_loss: 0.5302 - val_accuracy: 0.8406 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5173 - accuracy: 0.8403 - val_loss: 0.5279 - val_accuracy: 0.8414 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5149 - accuracy: 0.8408 - val_loss: 0.5258 - val_accuracy: 0.8422 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5128 - accuracy: 0.8414 - val_loss: 0.5239 - val_accuracy: 0.8422 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5108 - accuracy: 0.8416 - val_loss: 0.5221 - val_accuracy: 0.8427 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5090 - accuracy: 0.8423 - val_loss: 0.5205 - val_accuracy: 0.8425 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5073 - accuracy: 0.8428 - val_loss: 0.5190 - val_accuracy: 0.8424 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5057 - accuracy: 0.8433 - val_loss: 0.5177 - val_accuracy: 0.8433 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5042 - accuracy: 0.8434 - val_loss: 0.5164 - val_accuracy: 0.8437 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5028 - accuracy: 0.8439 - val_loss: 0.5152 - val_accuracy: 0.8445 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5015 - accuracy: 0.8442 - val_loss: 0.5141 - val_accuracy: 0.8450 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5003 - accuracy: 0.8444 - val_loss: 0.5131 - val_accuracy: 0.8449 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4991 - accuracy: 0.8449 - val_loss: 0.5121 - val_accuracy: 0.8452 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4980 - accuracy: 0.8455 - val_loss: 0.5112 - val_accuracy: 0.8456 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4969 - accuracy: 0.8458 - val_loss: 0.5103 - val_accuracy: 0.8463 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4960 - accuracy: 0.8461 - val_loss: 0.5095 - val_accuracy: 0.8462 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4950 - accuracy: 0.8465 - val_loss: 0.5087 - val_accuracy: 0.8468 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4941 - accuracy: 0.8465 - val_loss: 0.5080 - val_accuracy: 0.8469 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4932 - accuracy: 0.8472 - val_loss: 0.5073 - val_accuracy: 0.8471 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4924 - accuracy: 0.8475 - val_loss: 0.5067 - val_accuracy: 0.8471 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4916 - accuracy: 0.8480 - val_loss: 0.5061 - val_accuracy: 0.8479 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4908 - accuracy: 0.8481 - val_loss: 0.5055 - val_accuracy: 0.8482 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4901 - accuracy: 0.8483 - val_loss: 0.5049 - val_accuracy: 0.8490 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4894 - accuracy: 0.8488 - val_loss: 0.5044 - val_accuracy: 0.8492 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4887 - accuracy: 0.8490 - val_loss: 0.5040 - val_accuracy: 0.8495 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4880 - accuracy: 0.8493 - val_loss: 0.5035 - val_accuracy: 0.8497 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4874 - accuracy: 0.8495 - val_loss: 0.5031 - val_accuracy: 0.8496 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4868 - accuracy: 0.8496 - val_loss: 0.5026 - val_accuracy: 0.8499 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4862 - accuracy: 0.8498 - val_loss: 0.5023 - val_accuracy: 0.8503 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4856 - accuracy: 0.8497 - val_loss: 0.5019 - val_accuracy: 0.8509 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4851 - accuracy: 0.8497 - val_loss: 0.5015 - val_accuracy: 0.8508 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4846 - accuracy: 0.8503 - val_loss: 0.5011 - val_accuracy: 0.8510 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4840 - accuracy: 0.8505 - val_loss: 0.5008 - val_accuracy: 0.8514 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4835 - accuracy: 0.8504 - val_loss: 0.5005 - val_accuracy: 0.8515 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4830 - accuracy: 0.8503 - val_loss: 0.5001 - val_accuracy: 0.8513 [-0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4826 - accuracy: 0.8504 - val_loss: 0.4999 - val_accuracy: 0.8512 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4821 - accuracy: 0.8508 - val_loss: 0.4996 - val_accuracy: 0.8515 [-0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9718515289699571 Epoch 351/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6082 - accuracy: 0.4311 - val_loss: 1.5056 - val_accuracy: 0.4505 [-0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 352/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4894 - accuracy: 0.4552 - val_loss: 1.4884 - val_accuracy: 0.4435 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4762 - accuracy: 0.4550 - val_loss: 1.4795 - val_accuracy: 0.4572 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4681 - accuracy: 0.4604 - val_loss: 1.4729 - val_accuracy: 0.4613 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4619 - accuracy: 0.4629 - val_loss: 1.4676 - val_accuracy: 0.4638 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4571 - accuracy: 0.4653 - val_loss: 1.4633 - val_accuracy: 0.4667 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4531 - accuracy: 0.4671 - val_loss: 1.4597 - val_accuracy: 0.4697 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4498 - accuracy: 0.4685 - val_loss: 1.4568 - val_accuracy: 0.4712 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4472 - accuracy: 0.4695 - val_loss: 1.4545 - val_accuracy: 0.4713 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4450 - accuracy: 0.4701 - val_loss: 1.4526 - val_accuracy: 0.4727 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4432 - accuracy: 0.4707 - val_loss: 1.4510 - val_accuracy: 0.4733 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4415 - accuracy: 0.4711 - val_loss: 1.4495 - val_accuracy: 0.4739 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4400 - accuracy: 0.4714 - val_loss: 1.4481 - val_accuracy: 0.4742 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4386 - accuracy: 0.4718 - val_loss: 1.4468 - val_accuracy: 0.4745 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4372 - accuracy: 0.4721 - val_loss: 1.4456 - val_accuracy: 0.4748 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4359 - accuracy: 0.4726 - val_loss: 1.4443 - val_accuracy: 0.4751 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4347 - accuracy: 0.4724 - val_loss: 1.4431 - val_accuracy: 0.4754 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4334 - accuracy: 0.4725 - val_loss: 1.4418 - val_accuracy: 0.4764 [-0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4322 - accuracy: 0.4731 - val_loss: 1.4406 - val_accuracy: 0.4768 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4310 - accuracy: 0.4734 - val_loss: 1.4394 - val_accuracy: 0.4775 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4299 - accuracy: 0.4739 - val_loss: 1.4383 - val_accuracy: 0.4775 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4287 - accuracy: 0.4743 - val_loss: 1.4370 - val_accuracy: 0.4777 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4274 - accuracy: 0.4752 - val_loss: 1.4356 - val_accuracy: 0.4786 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4259 - accuracy: 0.4760 - val_loss: 1.4338 - val_accuracy: 0.4792 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4239 - accuracy: 0.4768 - val_loss: 1.4313 - val_accuracy: 0.4799 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4214 - accuracy: 0.4777 - val_loss: 1.4281 - val_accuracy: 0.4794 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4187 - accuracy: 0.4776 - val_loss: 1.4250 - val_accuracy: 0.4798 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4163 - accuracy: 0.4778 - val_loss: 1.4227 - val_accuracy: 0.4798 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4145 - accuracy: 0.4775 - val_loss: 1.4212 - val_accuracy: 0.4807 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4132 - accuracy: 0.4774 - val_loss: 1.4202 - val_accuracy: 0.4811 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4123 - accuracy: 0.4774 - val_loss: 1.4195 - val_accuracy: 0.4810 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4115 - accuracy: 0.4776 - val_loss: 1.4188 - val_accuracy: 0.4805 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4108 - accuracy: 0.4778 - val_loss: 1.4183 - val_accuracy: 0.4807 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4102 - accuracy: 0.4782 - val_loss: 1.4178 - val_accuracy: 0.4804 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 385/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4096 - accuracy: 0.4785 - val_loss: 1.4173 - val_accuracy: 0.4803 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4091 - accuracy: 0.4784 - val_loss: 1.4168 - val_accuracy: 0.4801 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4085 - accuracy: 0.4787 - val_loss: 1.4163 - val_accuracy: 0.4803 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4081 - accuracy: 0.4788 - val_loss: 1.4158 - val_accuracy: 0.4786 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4076 - accuracy: 0.4788 - val_loss: 1.4154 - val_accuracy: 0.4793 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4071 - accuracy: 0.4791 - val_loss: 1.4149 - val_accuracy: 0.4794 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4066 - accuracy: 0.4794 - val_loss: 1.4145 - val_accuracy: 0.4800 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4062 - accuracy: 0.4794 - val_loss: 1.4141 - val_accuracy: 0.4800 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 393/500 235/235 [==============================] - 2s 7ms/step - loss: 1.4057 - accuracy: 0.4798 - val_loss: 1.4137 - val_accuracy: 0.4799 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4053 - accuracy: 0.4799 - val_loss: 1.4133 - val_accuracy: 0.4799 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4048 - accuracy: 0.4805 - val_loss: 1.4129 - val_accuracy: 0.4799 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4044 - accuracy: 0.4805 - val_loss: 1.4125 - val_accuracy: 0.4803 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4039 - accuracy: 0.4810 - val_loss: 1.4121 - val_accuracy: 0.4803 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4035 - accuracy: 0.4810 - val_loss: 1.4116 - val_accuracy: 0.4809 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4030 - accuracy: 0.4815 - val_loss: 1.4112 - val_accuracy: 0.4807 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4026 - accuracy: 0.4817 - val_loss: 1.4108 - val_accuracy: 0.4810 [-0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 401/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8316 - accuracy: 0.3411 - val_loss: 1.7511 - val_accuracy: 0.3464 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7366 - accuracy: 0.3586 - val_loss: 1.7362 - val_accuracy: 0.3565 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7299 - accuracy: 0.3627 - val_loss: 1.7316 - val_accuracy: 0.3573 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7275 - accuracy: 0.3630 - val_loss: 1.7294 - val_accuracy: 0.3578 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7261 - accuracy: 0.3630 - val_loss: 1.7281 - val_accuracy: 0.3580 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7252 - accuracy: 0.3631 - val_loss: 1.7272 - val_accuracy: 0.3586 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7244 - accuracy: 0.3630 - val_loss: 1.7265 - val_accuracy: 0.3586 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7238 - accuracy: 0.3630 - val_loss: 1.7259 - val_accuracy: 0.3587 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7234 - accuracy: 0.3631 - val_loss: 1.7254 - val_accuracy: 0.3586 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7229 - accuracy: 0.3632 - val_loss: 1.7250 - val_accuracy: 0.3588 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7225 - accuracy: 0.3633 - val_loss: 1.7247 - val_accuracy: 0.3591 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7221 - accuracy: 0.3635 - val_loss: 1.7244 - val_accuracy: 0.3592 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7217 - accuracy: 0.3636 - val_loss: 1.7241 - val_accuracy: 0.3599 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7214 - accuracy: 0.3636 - val_loss: 1.7237 - val_accuracy: 0.3601 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7210 - accuracy: 0.3638 - val_loss: 1.7235 - val_accuracy: 0.3600 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7207 - accuracy: 0.3644 - val_loss: 1.7232 - val_accuracy: 0.3600 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7204 - accuracy: 0.3641 - val_loss: 1.7229 - val_accuracy: 0.3602 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7201 - accuracy: 0.3645 - val_loss: 1.7227 - val_accuracy: 0.3603 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7198 - accuracy: 0.3643 - val_loss: 1.7225 - val_accuracy: 0.3607 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7195 - accuracy: 0.3648 - val_loss: 1.7223 - val_accuracy: 0.3607 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7193 - accuracy: 0.3649 - val_loss: 1.7221 - val_accuracy: 0.3604 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7190 - accuracy: 0.3650 - val_loss: 1.7219 - val_accuracy: 0.3600 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7188 - accuracy: 0.3650 - val_loss: 1.7217 - val_accuracy: 0.3602 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7186 - accuracy: 0.3652 - val_loss: 1.7216 - val_accuracy: 0.3605 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7183 - accuracy: 0.3653 - val_loss: 1.7214 - val_accuracy: 0.3609 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7182 - accuracy: 0.3656 - val_loss: 1.7213 - val_accuracy: 0.3610 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7179 - accuracy: 0.3656 - val_loss: 1.7212 - val_accuracy: 0.3610 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7178 - accuracy: 0.3657 - val_loss: 1.7211 - val_accuracy: 0.3611 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7176 - accuracy: 0.3659 - val_loss: 1.7210 - val_accuracy: 0.3613 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7174 - accuracy: 0.3660 - val_loss: 1.7209 - val_accuracy: 0.3614 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7173 - accuracy: 0.3659 - val_loss: 1.7208 - val_accuracy: 0.3617 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7171 - accuracy: 0.3660 - val_loss: 1.7207 - val_accuracy: 0.3615 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7170 - accuracy: 0.3660 - val_loss: 1.7206 - val_accuracy: 0.3617 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7168 - accuracy: 0.3661 - val_loss: 1.7205 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7167 - accuracy: 0.3661 - val_loss: 1.7205 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7166 - accuracy: 0.3662 - val_loss: 1.7204 - val_accuracy: 0.3618 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7165 - accuracy: 0.3663 - val_loss: 1.7203 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7163 - accuracy: 0.3662 - val_loss: 1.7203 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7163 - accuracy: 0.3663 - val_loss: 1.7202 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7161 - accuracy: 0.3664 - val_loss: 1.7201 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7160 - accuracy: 0.3665 - val_loss: 1.7201 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7160 - accuracy: 0.3667 - val_loss: 1.7200 - val_accuracy: 0.3616 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7158 - accuracy: 0.3665 - val_loss: 1.7199 - val_accuracy: 0.3617 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.3666 - val_loss: 1.7199 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.3666 - val_loss: 1.7199 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7156 - accuracy: 0.3665 - val_loss: 1.7198 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7155 - accuracy: 0.3666 - val_loss: 1.7197 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7154 - accuracy: 0.3666 - val_loss: 1.7197 - val_accuracy: 0.3618 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7154 - accuracy: 0.3666 - val_loss: 1.7196 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.3667 - val_loss: 1.7196 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7152 - accuracy: 0.3668 - val_loss: 1.7195 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7151 - accuracy: 0.3668 - val_loss: 1.7195 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7150 - accuracy: 0.3668 - val_loss: 1.7195 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.3668 - val_loss: 1.7194 - val_accuracy: 0.3621 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.3668 - val_loss: 1.7193 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7148 - accuracy: 0.3670 - val_loss: 1.7193 - val_accuracy: 0.3618 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7148 - accuracy: 0.3670 - val_loss: 1.7192 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7147 - accuracy: 0.3671 - val_loss: 1.7192 - val_accuracy: 0.3619 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7146 - accuracy: 0.3670 - val_loss: 1.7191 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7145 - accuracy: 0.3669 - val_loss: 1.7190 - val_accuracy: 0.3620 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7144 - accuracy: 0.3668 - val_loss: 1.7190 - val_accuracy: 0.3621 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7144 - accuracy: 0.3671 - val_loss: 1.7190 - val_accuracy: 0.3621 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7143 - accuracy: 0.3672 - val_loss: 1.7189 - val_accuracy: 0.3621 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7142 - accuracy: 0.3674 - val_loss: 1.7189 - val_accuracy: 0.3621 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7141 - accuracy: 0.3674 - val_loss: 1.7189 - val_accuracy: 0.3622 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7141 - accuracy: 0.3674 - val_loss: 1.7188 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.3675 - val_loss: 1.7188 - val_accuracy: 0.3623 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.3677 - val_loss: 1.7187 - val_accuracy: 0.3623 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7139 - accuracy: 0.3676 - val_loss: 1.7187 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7139 - accuracy: 0.3674 - val_loss: 1.7187 - val_accuracy: 0.3623 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7138 - accuracy: 0.3675 - val_loss: 1.7186 - val_accuracy: 0.3622 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7138 - accuracy: 0.3674 - val_loss: 1.7186 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7137 - accuracy: 0.3675 - val_loss: 1.7186 - val_accuracy: 0.3625 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7137 - accuracy: 0.3675 - val_loss: 1.7185 - val_accuracy: 0.3625 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7136 - accuracy: 0.3675 - val_loss: 1.7185 - val_accuracy: 0.3622 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7136 - accuracy: 0.3676 - val_loss: 1.7184 - val_accuracy: 0.3623 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.3676 - val_loss: 1.7184 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7135 - accuracy: 0.3676 - val_loss: 1.7184 - val_accuracy: 0.3623 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.3676 - val_loss: 1.7183 - val_accuracy: 0.3623 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7134 - accuracy: 0.3677 - val_loss: 1.7183 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7134 - accuracy: 0.3676 - val_loss: 1.7183 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7133 - accuracy: 0.3676 - val_loss: 1.7182 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7133 - accuracy: 0.3676 - val_loss: 1.7182 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7133 - accuracy: 0.3677 - val_loss: 1.7182 - val_accuracy: 0.3625 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7132 - accuracy: 0.3678 - val_loss: 1.7181 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7132 - accuracy: 0.3676 - val_loss: 1.7181 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.3678 - val_loss: 1.7181 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.3679 - val_loss: 1.7180 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.3679 - val_loss: 1.7180 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7130 - accuracy: 0.3677 - val_loss: 1.7179 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7130 - accuracy: 0.3678 - val_loss: 1.7179 - val_accuracy: 0.3624 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7129 - accuracy: 0.3679 - val_loss: 1.7179 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7129 - accuracy: 0.3677 - val_loss: 1.7179 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7129 - accuracy: 0.3679 - val_loss: 1.7179 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7128 - accuracy: 0.3677 - val_loss: 1.7179 - val_accuracy: 0.3627 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7128 - accuracy: 0.3677 - val_loss: 1.7178 - val_accuracy: 0.3627 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7128 - accuracy: 0.3680 - val_loss: 1.7177 - val_accuracy: 0.3627 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.3679 - val_loss: 1.7177 - val_accuracy: 0.3627 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.3679 - val_loss: 1.7177 - val_accuracy: 0.3626 [-0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.3679 - val_loss: 1.7177 - val_accuracy: 0.3625 [-0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 1/200 235/235 [==============================] - 4s 14ms/step - loss: 2.1737 - accuracy: 0.9248 - val_loss: 1.5242 - val_accuracy: 0.8890 Epoch 2/200 235/235 [==============================] - 3s 13ms/step - loss: 0.4353 - accuracy: 0.9594 - val_loss: 0.4910 - val_accuracy: 0.9330 Epoch 3/200 235/235 [==============================] - 3s 13ms/step - loss: 0.3160 - accuracy: 0.9626 - val_loss: 0.3328 - val_accuracy: 0.9539 Epoch 4/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2849 - accuracy: 0.9646 - val_loss: 0.3188 - val_accuracy: 0.9489 Epoch 5/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2622 - accuracy: 0.9672 - val_loss: 0.3016 - val_accuracy: 0.9517 Epoch 6/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2511 - accuracy: 0.9682 - val_loss: 0.2965 - val_accuracy: 0.9513 Epoch 7/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2421 - accuracy: 0.9696 - val_loss: 0.3048 - val_accuracy: 0.9483 Epoch 8/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2340 - accuracy: 0.9703 - val_loss: 0.2974 - val_accuracy: 0.9440 Epoch 9/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2258 - accuracy: 0.9701 - val_loss: 0.2905 - val_accuracy: 0.9431 Epoch 10/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2198 - accuracy: 0.9703 - val_loss: 0.2530 - val_accuracy: 0.9561 Epoch 11/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2111 - accuracy: 0.9718 - val_loss: 0.2501 - val_accuracy: 0.9565 Epoch 12/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2095 - accuracy: 0.9714 - val_loss: 0.2555 - val_accuracy: 0.9560 Epoch 13/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2060 - accuracy: 0.9722 - val_loss: 0.2789 - val_accuracy: 0.9477 Epoch 14/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2020 - accuracy: 0.9721 - val_loss: 0.2356 - val_accuracy: 0.9598 Epoch 15/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1966 - accuracy: 0.9720 - val_loss: 0.2957 - val_accuracy: 0.9384 Epoch 16/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1935 - accuracy: 0.9732 - val_loss: 0.2861 - val_accuracy: 0.9417 Epoch 17/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1905 - accuracy: 0.9729 - val_loss: 0.2594 - val_accuracy: 0.9499 Epoch 18/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1917 - accuracy: 0.9723 - val_loss: 0.2609 - val_accuracy: 0.9489 Epoch 19/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1878 - accuracy: 0.9728 - val_loss: 0.2464 - val_accuracy: 0.9543 Epoch 20/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1825 - accuracy: 0.9737 - val_loss: 0.2536 - val_accuracy: 0.9500 Epoch 21/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1830 - accuracy: 0.9735 - val_loss: 0.2378 - val_accuracy: 0.9559 Epoch 22/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1783 - accuracy: 0.9748 - val_loss: 0.2248 - val_accuracy: 0.9606 Epoch 23/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1759 - accuracy: 0.9748 - val_loss: 0.2905 - val_accuracy: 0.9398 Epoch 24/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1769 - accuracy: 0.9738 - val_loss: 0.2315 - val_accuracy: 0.9555 Epoch 25/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1742 - accuracy: 0.9750 - val_loss: 0.2395 - val_accuracy: 0.9537 Epoch 26/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1759 - accuracy: 0.9744 - val_loss: 0.2273 - val_accuracy: 0.9585 Epoch 27/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1745 - accuracy: 0.9746 - val_loss: 0.2475 - val_accuracy: 0.9524 Epoch 28/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1732 - accuracy: 0.9755 - val_loss: 0.2300 - val_accuracy: 0.9561 Epoch 29/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1706 - accuracy: 0.9751 - val_loss: 0.2366 - val_accuracy: 0.9541 Epoch 30/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1686 - accuracy: 0.9754 - val_loss: 0.2577 - val_accuracy: 0.9481 Epoch 31/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1694 - accuracy: 0.9751 - val_loss: 0.2064 - val_accuracy: 0.9657 Epoch 32/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1667 - accuracy: 0.9761 - val_loss: 0.2472 - val_accuracy: 0.9514 Epoch 33/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1679 - accuracy: 0.9752 - val_loss: 0.2317 - val_accuracy: 0.9547 Epoch 34/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1684 - accuracy: 0.9746 - val_loss: 0.2305 - val_accuracy: 0.9549 Epoch 35/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1649 - accuracy: 0.9758 - val_loss: 0.2384 - val_accuracy: 0.9530 Epoch 36/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1639 - accuracy: 0.9754 - val_loss: 0.2561 - val_accuracy: 0.9492 Epoch 37/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1651 - accuracy: 0.9752 - val_loss: 0.2818 - val_accuracy: 0.9369 Epoch 38/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1634 - accuracy: 0.9763 - val_loss: 0.2169 - val_accuracy: 0.9604 Epoch 39/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1627 - accuracy: 0.9767 - val_loss: 0.3096 - val_accuracy: 0.9311 Epoch 40/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1627 - accuracy: 0.9767 - val_loss: 0.2196 - val_accuracy: 0.9583 Epoch 41/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1632 - accuracy: 0.9757 - val_loss: 0.2205 - val_accuracy: 0.9604 Epoch 42/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1610 - accuracy: 0.9758 - val_loss: 0.2648 - val_accuracy: 0.9433 Epoch 43/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1598 - accuracy: 0.9768 - val_loss: 0.2101 - val_accuracy: 0.9610 Epoch 44/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1600 - accuracy: 0.9767 - val_loss: 0.2638 - val_accuracy: 0.9459 Epoch 45/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1580 - accuracy: 0.9772 - val_loss: 0.2580 - val_accuracy: 0.9461 Epoch 46/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1573 - accuracy: 0.9773 - val_loss: 0.2209 - val_accuracy: 0.9580 Epoch 47/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1564 - accuracy: 0.9778 - val_loss: 0.2135 - val_accuracy: 0.9622 Epoch 48/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1580 - accuracy: 0.9758 - val_loss: 0.2297 - val_accuracy: 0.9577 Epoch 49/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1580 - accuracy: 0.9763 - val_loss: 0.2120 - val_accuracy: 0.9602 Epoch 50/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1610 - accuracy: 0.9761 - val_loss: 0.2164 - val_accuracy: 0.9597 Epoch 51/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1572 - accuracy: 0.9766 - val_loss: 0.2352 - val_accuracy: 0.9559 Epoch 52/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1596 - accuracy: 0.9759 - val_loss: 0.2283 - val_accuracy: 0.9569 Epoch 53/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1573 - accuracy: 0.9767 - val_loss: 0.2164 - val_accuracy: 0.9616 Epoch 54/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1589 - accuracy: 0.9762 - val_loss: 0.2016 - val_accuracy: 0.9631 Epoch 55/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1577 - accuracy: 0.9757 - val_loss: 0.2363 - val_accuracy: 0.9567 Epoch 56/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1528 - accuracy: 0.9786 - val_loss: 0.2122 - val_accuracy: 0.9610 Epoch 57/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1551 - accuracy: 0.9771 - val_loss: 0.2280 - val_accuracy: 0.9569 Epoch 58/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1582 - accuracy: 0.9765 - val_loss: 0.2427 - val_accuracy: 0.9498 Epoch 59/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1543 - accuracy: 0.9770 - val_loss: 0.2089 - val_accuracy: 0.9615 Epoch 60/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1523 - accuracy: 0.9779 - val_loss: 0.2028 - val_accuracy: 0.9648 Epoch 61/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1546 - accuracy: 0.9771 - val_loss: 0.2453 - val_accuracy: 0.9492 Epoch 62/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1559 - accuracy: 0.9768 - val_loss: 0.2028 - val_accuracy: 0.9646 Epoch 63/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1570 - accuracy: 0.9759 - val_loss: 0.2352 - val_accuracy: 0.9570 Epoch 64/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1554 - accuracy: 0.9773 - val_loss: 0.2496 - val_accuracy: 0.9523 Epoch 65/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1549 - accuracy: 0.9773 - val_loss: 0.2600 - val_accuracy: 0.9469 Epoch 66/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1550 - accuracy: 0.9768 - val_loss: 0.2354 - val_accuracy: 0.9553 Epoch 67/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1539 - accuracy: 0.9770 - val_loss: 0.2097 - val_accuracy: 0.9602 Epoch 68/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1553 - accuracy: 0.9770 - val_loss: 0.1960 - val_accuracy: 0.9653 Epoch 69/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1553 - accuracy: 0.9771 - val_loss: 0.2380 - val_accuracy: 0.9513 Epoch 70/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1561 - accuracy: 0.9763 - val_loss: 0.2187 - val_accuracy: 0.9585 Epoch 71/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1549 - accuracy: 0.9764 - val_loss: 0.2224 - val_accuracy: 0.9568 Epoch 72/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1508 - accuracy: 0.9774 - val_loss: 0.2300 - val_accuracy: 0.9557 Epoch 73/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1526 - accuracy: 0.9769 - val_loss: 0.2341 - val_accuracy: 0.9508 Epoch 74/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1510 - accuracy: 0.9772 - val_loss: 0.2401 - val_accuracy: 0.9498 Epoch 75/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1542 - accuracy: 0.9766 - val_loss: 0.2441 - val_accuracy: 0.9468 Epoch 76/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1511 - accuracy: 0.9777 - val_loss: 0.2059 - val_accuracy: 0.9619 Epoch 77/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1508 - accuracy: 0.9777 - val_loss: 0.2043 - val_accuracy: 0.9603 Epoch 78/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1504 - accuracy: 0.9780 - val_loss: 0.2156 - val_accuracy: 0.9578 Epoch 79/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1509 - accuracy: 0.9773 - val_loss: 0.2334 - val_accuracy: 0.9555 Epoch 80/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1540 - accuracy: 0.9771 - val_loss: 0.2820 - val_accuracy: 0.9391 Epoch 81/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1587 - accuracy: 0.9755 - val_loss: 0.2220 - val_accuracy: 0.9582 Epoch 82/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9779 - val_loss: 0.2257 - val_accuracy: 0.9531 Epoch 83/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1531 - accuracy: 0.9763 - val_loss: 0.2023 - val_accuracy: 0.9631 Epoch 84/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1506 - accuracy: 0.9774 - val_loss: 0.2146 - val_accuracy: 0.9604 Epoch 85/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1471 - accuracy: 0.9781 - val_loss: 0.2139 - val_accuracy: 0.9586 Epoch 86/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1489 - accuracy: 0.9778 - val_loss: 0.2020 - val_accuracy: 0.9630 Epoch 87/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1481 - accuracy: 0.9786 - val_loss: 0.2045 - val_accuracy: 0.9606 Epoch 88/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1476 - accuracy: 0.9776 - val_loss: 0.2242 - val_accuracy: 0.9565 Epoch 89/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1495 - accuracy: 0.9774 - val_loss: 0.2372 - val_accuracy: 0.9501 Epoch 90/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1467 - accuracy: 0.9778 - val_loss: 0.2216 - val_accuracy: 0.9582 Epoch 91/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1481 - accuracy: 0.9779 - val_loss: 0.2084 - val_accuracy: 0.9604 Epoch 92/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1490 - accuracy: 0.9784 - val_loss: 0.2122 - val_accuracy: 0.9588 Epoch 93/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1482 - accuracy: 0.9780 - val_loss: 0.2125 - val_accuracy: 0.9582 Epoch 94/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1501 - accuracy: 0.9768 - val_loss: 0.2381 - val_accuracy: 0.9502 Epoch 95/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1480 - accuracy: 0.9785 - val_loss: 0.1908 - val_accuracy: 0.9647 Epoch 96/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1463 - accuracy: 0.9784 - val_loss: 0.2145 - val_accuracy: 0.9582 Epoch 97/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1455 - accuracy: 0.9779 - val_loss: 0.1963 - val_accuracy: 0.9639 Epoch 98/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1490 - accuracy: 0.9770 - val_loss: 0.2141 - val_accuracy: 0.9583 Epoch 99/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1452 - accuracy: 0.9787 - val_loss: 0.2060 - val_accuracy: 0.9602 Epoch 100/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1474 - accuracy: 0.9773 - val_loss: 0.2376 - val_accuracy: 0.9529 Epoch 101/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1501 - accuracy: 0.9772 - val_loss: 0.2658 - val_accuracy: 0.9426 Epoch 102/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1482 - accuracy: 0.9775 - val_loss: 0.2295 - val_accuracy: 0.9521 Epoch 103/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1481 - accuracy: 0.9778 - val_loss: 0.2269 - val_accuracy: 0.9545 Epoch 104/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1488 - accuracy: 0.9777 - val_loss: 0.2024 - val_accuracy: 0.9615 Epoch 105/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1456 - accuracy: 0.9784 - val_loss: 0.2086 - val_accuracy: 0.9586 Epoch 106/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1488 - accuracy: 0.9768 - val_loss: 0.2156 - val_accuracy: 0.9593 Epoch 107/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1431 - accuracy: 0.9786 - val_loss: 0.2278 - val_accuracy: 0.9530 Epoch 108/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1486 - accuracy: 0.9768 - val_loss: 0.1887 - val_accuracy: 0.9666 Epoch 109/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1454 - accuracy: 0.9783 - val_loss: 0.2081 - val_accuracy: 0.9584 Epoch 110/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1428 - accuracy: 0.9783 - val_loss: 0.2436 - val_accuracy: 0.9491 Epoch 111/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1513 - accuracy: 0.9768 - val_loss: 0.2471 - val_accuracy: 0.9496 Epoch 112/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1474 - accuracy: 0.9780 - val_loss: 0.2137 - val_accuracy: 0.9594 Epoch 113/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1475 - accuracy: 0.9775 - val_loss: 0.2124 - val_accuracy: 0.9576 Epoch 114/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1490 - accuracy: 0.9774 - val_loss: 0.2183 - val_accuracy: 0.9569 Epoch 115/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9786 - val_loss: 0.2108 - val_accuracy: 0.9601 Epoch 116/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1455 - accuracy: 0.9776 - val_loss: 0.2479 - val_accuracy: 0.9450 Epoch 117/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9782 - val_loss: 0.1917 - val_accuracy: 0.9668 Epoch 118/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1471 - accuracy: 0.9777 - val_loss: 0.2174 - val_accuracy: 0.9569 Epoch 119/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1417 - accuracy: 0.9790 - val_loss: 0.2200 - val_accuracy: 0.9579 Epoch 120/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1440 - accuracy: 0.9778 - val_loss: 0.2018 - val_accuracy: 0.9613 Epoch 121/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1453 - accuracy: 0.9779 - val_loss: 0.2096 - val_accuracy: 0.9579 Epoch 122/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1406 - accuracy: 0.9794 - val_loss: 0.1959 - val_accuracy: 0.9635 Epoch 123/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1468 - accuracy: 0.9773 - val_loss: 0.2047 - val_accuracy: 0.9617 Epoch 124/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1418 - accuracy: 0.9785 - val_loss: 0.2532 - val_accuracy: 0.9470 Epoch 125/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1485 - accuracy: 0.9770 - val_loss: 0.2010 - val_accuracy: 0.9638 Epoch 126/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1451 - accuracy: 0.9783 - val_loss: 0.2201 - val_accuracy: 0.9553 Epoch 127/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9777 - val_loss: 0.2111 - val_accuracy: 0.9593 Epoch 128/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1406 - accuracy: 0.9790 - val_loss: 0.2143 - val_accuracy: 0.9575 Epoch 129/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1485 - accuracy: 0.9768 - val_loss: 0.1982 - val_accuracy: 0.9623 Epoch 130/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1429 - accuracy: 0.9785 - val_loss: 0.2142 - val_accuracy: 0.9578 Epoch 131/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9785 - val_loss: 0.2115 - val_accuracy: 0.9582 Epoch 132/200 235/235 [==============================] - 3s 11ms/step - loss: 0.1437 - accuracy: 0.9790 - val_loss: 0.2235 - val_accuracy: 0.9564 Epoch 133/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1453 - accuracy: 0.9781 - val_loss: 0.2057 - val_accuracy: 0.9623 Epoch 134/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1428 - accuracy: 0.9789 - val_loss: 0.2346 - val_accuracy: 0.9517 Epoch 135/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1431 - accuracy: 0.9783 - val_loss: 0.2210 - val_accuracy: 0.9581 Epoch 136/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1431 - accuracy: 0.9789 - val_loss: 0.2003 - val_accuracy: 0.9630 Epoch 137/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1452 - accuracy: 0.9776 - val_loss: 0.2035 - val_accuracy: 0.9616 Epoch 138/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1445 - accuracy: 0.9780 - val_loss: 0.2006 - val_accuracy: 0.9632 Epoch 139/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1435 - accuracy: 0.9781 - val_loss: 0.1917 - val_accuracy: 0.9631 Epoch 140/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1463 - accuracy: 0.9779 - val_loss: 0.1955 - val_accuracy: 0.9633 Epoch 141/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9790 - val_loss: 0.2072 - val_accuracy: 0.9593 Epoch 142/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9789 - val_loss: 0.2386 - val_accuracy: 0.9517 Epoch 143/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1464 - accuracy: 0.9774 - val_loss: 0.2407 - val_accuracy: 0.9499 Epoch 144/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1442 - accuracy: 0.9783 - val_loss: 0.2230 - val_accuracy: 0.9547 Epoch 145/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1445 - accuracy: 0.9784 - val_loss: 0.2205 - val_accuracy: 0.9554 Epoch 146/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1432 - accuracy: 0.9784 - val_loss: 0.2292 - val_accuracy: 0.9542 Epoch 147/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1450 - accuracy: 0.9785 - val_loss: 0.2308 - val_accuracy: 0.9554 Epoch 148/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1434 - accuracy: 0.9787 - val_loss: 0.2135 - val_accuracy: 0.9568 Epoch 149/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1381 - accuracy: 0.9796 - val_loss: 0.1945 - val_accuracy: 0.9637 Epoch 150/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1441 - accuracy: 0.9773 - val_loss: 0.2043 - val_accuracy: 0.9607 Epoch 151/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1433 - accuracy: 0.9783 - val_loss: 0.1970 - val_accuracy: 0.9644 Epoch 152/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9779 - val_loss: 0.2014 - val_accuracy: 0.9613 Epoch 153/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1429 - accuracy: 0.9781 - val_loss: 0.2060 - val_accuracy: 0.9595 Epoch 154/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1414 - accuracy: 0.9787 - val_loss: 0.1985 - val_accuracy: 0.9623 Epoch 155/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9790 - val_loss: 0.2149 - val_accuracy: 0.9589 Epoch 156/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1431 - accuracy: 0.9788 - val_loss: 0.1991 - val_accuracy: 0.9620 Epoch 157/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9793 - val_loss: 0.2390 - val_accuracy: 0.9485 Epoch 158/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1453 - accuracy: 0.9778 - val_loss: 0.2029 - val_accuracy: 0.9604 Epoch 159/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1423 - accuracy: 0.9786 - val_loss: 0.2228 - val_accuracy: 0.9560 Epoch 160/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1454 - accuracy: 0.9777 - val_loss: 0.2035 - val_accuracy: 0.9625 Epoch 161/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9789 - val_loss: 0.2168 - val_accuracy: 0.9559 Epoch 162/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1462 - accuracy: 0.9768 - val_loss: 0.2062 - val_accuracy: 0.9579 Epoch 163/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1401 - accuracy: 0.9790 - val_loss: 0.2008 - val_accuracy: 0.9592 Epoch 164/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1417 - accuracy: 0.9785 - val_loss: 0.1998 - val_accuracy: 0.9613 Epoch 165/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1448 - accuracy: 0.9781 - val_loss: 0.1890 - val_accuracy: 0.9643 Epoch 166/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1388 - accuracy: 0.9802 - val_loss: 0.2218 - val_accuracy: 0.9574 Epoch 167/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1457 - accuracy: 0.9778 - val_loss: 0.2218 - val_accuracy: 0.9560 Epoch 168/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1430 - accuracy: 0.9779 - val_loss: 0.2269 - val_accuracy: 0.9534 Epoch 169/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1432 - accuracy: 0.9790 - val_loss: 0.2055 - val_accuracy: 0.9589 Epoch 170/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1386 - accuracy: 0.9797 - val_loss: 0.2038 - val_accuracy: 0.9622 Epoch 171/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1419 - accuracy: 0.9783 - val_loss: 0.1909 - val_accuracy: 0.9623 Epoch 172/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9782 - val_loss: 0.2146 - val_accuracy: 0.9569 Epoch 173/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9787 - val_loss: 0.2059 - val_accuracy: 0.9600 Epoch 174/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1430 - accuracy: 0.9780 - val_loss: 0.2064 - val_accuracy: 0.9592 Epoch 175/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1408 - accuracy: 0.9787 - val_loss: 0.1816 - val_accuracy: 0.9646 Epoch 176/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9775 - val_loss: 0.2180 - val_accuracy: 0.9556 Epoch 177/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1458 - accuracy: 0.9775 - val_loss: 0.1949 - val_accuracy: 0.9650 Epoch 178/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1391 - accuracy: 0.9795 - val_loss: 0.1996 - val_accuracy: 0.9606 Epoch 179/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1433 - accuracy: 0.9776 - val_loss: 0.2121 - val_accuracy: 0.9608 Epoch 180/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1417 - accuracy: 0.9789 - val_loss: 0.1968 - val_accuracy: 0.9618 Epoch 181/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9790 - val_loss: 0.2084 - val_accuracy: 0.9587 Epoch 182/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1407 - accuracy: 0.9788 - val_loss: 0.2106 - val_accuracy: 0.9582 Epoch 183/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9783 - val_loss: 0.2039 - val_accuracy: 0.9619 Epoch 184/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9781 - val_loss: 0.2209 - val_accuracy: 0.9555 Epoch 185/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9780 - val_loss: 0.2305 - val_accuracy: 0.9528 Epoch 186/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9789 - val_loss: 0.2030 - val_accuracy: 0.9590 Epoch 187/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1399 - accuracy: 0.9785 - val_loss: 0.2407 - val_accuracy: 0.9505 Epoch 188/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1427 - accuracy: 0.9780 - val_loss: 0.1906 - val_accuracy: 0.9644 Epoch 189/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9794 - val_loss: 0.2146 - val_accuracy: 0.9581 Epoch 190/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1467 - accuracy: 0.9765 - val_loss: 0.2115 - val_accuracy: 0.9594 Epoch 191/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9782 - val_loss: 0.2116 - val_accuracy: 0.9571 Epoch 192/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1421 - accuracy: 0.9788 - val_loss: 0.2322 - val_accuracy: 0.9532 Epoch 193/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1429 - accuracy: 0.9780 - val_loss: 0.1936 - val_accuracy: 0.9630 Epoch 194/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9792 - val_loss: 0.1965 - val_accuracy: 0.9628 Epoch 195/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9781 - val_loss: 0.2546 - val_accuracy: 0.9474 Epoch 196/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1468 - accuracy: 0.9773 - val_loss: 0.1937 - val_accuracy: 0.9664 Epoch 197/200 235/235 [==============================] - 4s 15ms/step - loss: 0.1404 - accuracy: 0.9793 - val_loss: 0.2041 - val_accuracy: 0.9593 Epoch 198/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9787 - val_loss: 0.1964 - val_accuracy: 0.9626 Epoch 199/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9779 - val_loss: 0.1977 - val_accuracy: 0.9596 Epoch 200/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1386 - accuracy: 0.9795 - val_loss: 0.1818 - val_accuracy: 0.9655 Epoch 1/200 235/235 [==============================] - 4s 14ms/step - loss: 0.2500 - accuracy: 0.9257 - val_loss: 0.2034 - val_accuracy: 0.9598 Epoch 2/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0872 - accuracy: 0.9747 - val_loss: 0.0950 - val_accuracy: 0.9702 Epoch 3/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0502 - accuracy: 0.9861 - val_loss: 0.0908 - val_accuracy: 0.9724 Epoch 4/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0304 - accuracy: 0.9924 - val_loss: 0.0774 - val_accuracy: 0.9761 Epoch 5/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0192 - accuracy: 0.9956 - val_loss: 0.0825 - val_accuracy: 0.9762 Epoch 6/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0140 - accuracy: 0.9968 - val_loss: 0.0743 - val_accuracy: 0.9787 Epoch 7/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0123 - accuracy: 0.9967 - val_loss: 0.0877 - val_accuracy: 0.9747 Epoch 8/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.0790 - val_accuracy: 0.9794 Epoch 9/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0102 - accuracy: 0.9970 - val_loss: 0.0986 - val_accuracy: 0.9742 Epoch 10/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0126 - accuracy: 0.9962 - val_loss: 0.0937 - val_accuracy: 0.9725 Epoch 11/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0137 - accuracy: 0.9954 - val_loss: 0.1006 - val_accuracy: 0.9715 Epoch 12/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9976 - val_loss: 0.0962 - val_accuracy: 0.9758 Epoch 13/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0081 - accuracy: 0.9973 - val_loss: 0.0849 - val_accuracy: 0.9790 Epoch 14/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0092 - accuracy: 0.9971 - val_loss: 0.0831 - val_accuracy: 0.9786 Epoch 15/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9984 - val_loss: 0.0656 - val_accuracy: 0.9825 Epoch 16/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.0674 - val_accuracy: 0.9842 Epoch 17/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0708 - val_accuracy: 0.9814 Epoch 18/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0704 - val_accuracy: 0.9837 Epoch 19/200 235/235 [==============================] - 3s 13ms/step - loss: 5.2802e-04 - accuracy: 1.0000 - val_loss: 0.0660 - val_accuracy: 0.9837 Epoch 20/200 235/235 [==============================] - 3s 13ms/step - loss: 2.4735e-04 - accuracy: 1.0000 - val_loss: 0.0636 - val_accuracy: 0.9842 Epoch 21/200 235/235 [==============================] - 3s 13ms/step - loss: 1.5895e-04 - accuracy: 1.0000 - val_loss: 0.0644 - val_accuracy: 0.9846 Epoch 22/200 235/235 [==============================] - 3s 13ms/step - loss: 3.5584e-04 - accuracy: 0.9999 - val_loss: 0.0761 - val_accuracy: 0.9836 Epoch 23/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0256 - accuracy: 0.9917 - val_loss: 0.1689 - val_accuracy: 0.9609 Epoch 24/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0247 - accuracy: 0.9919 - val_loss: 0.0782 - val_accuracy: 0.9795 Epoch 25/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0069 - accuracy: 0.9980 - val_loss: 0.0719 - val_accuracy: 0.9824 Epoch 26/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0651 - val_accuracy: 0.9841 Epoch 27/200 235/235 [==============================] - 3s 11ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.0630 - val_accuracy: 0.9846 Epoch 28/200 235/235 [==============================] - 3s 13ms/step - loss: 5.2952e-04 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9839 Epoch 29/200 235/235 [==============================] - 3s 13ms/step - loss: 3.4885e-04 - accuracy: 1.0000 - val_loss: 0.0620 - val_accuracy: 0.9847 Epoch 30/200 235/235 [==============================] - 3s 13ms/step - loss: 2.5717e-04 - accuracy: 1.0000 - val_loss: 0.0619 - val_accuracy: 0.9854 Epoch 31/200 235/235 [==============================] - 3s 13ms/step - loss: 2.5684e-04 - accuracy: 1.0000 - val_loss: 0.0631 - val_accuracy: 0.9860 Epoch 32/200 235/235 [==============================] - 3s 13ms/step - loss: 1.5152e-04 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9852 Epoch 33/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2892e-04 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9854 Epoch 34/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0813 - val_accuracy: 0.9809 Epoch 35/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0223 - accuracy: 0.9926 - val_loss: 0.1289 - val_accuracy: 0.9717 Epoch 36/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0130 - accuracy: 0.9956 - val_loss: 0.0763 - val_accuracy: 0.9816 Epoch 37/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.0734 - val_accuracy: 0.9810 Epoch 38/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.0644 - val_accuracy: 0.9851 Epoch 39/200 235/235 [==============================] - 3s 13ms/step - loss: 6.8622e-04 - accuracy: 0.9999 - val_loss: 0.0637 - val_accuracy: 0.9852 Epoch 40/200 235/235 [==============================] - 3s 13ms/step - loss: 4.1688e-04 - accuracy: 1.0000 - val_loss: 0.0629 - val_accuracy: 0.9859 Epoch 41/200 235/235 [==============================] - 3s 13ms/step - loss: 2.2065e-04 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9862 Epoch 42/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4948e-04 - accuracy: 1.0000 - val_loss: 0.0632 - val_accuracy: 0.9862 Epoch 43/200 235/235 [==============================] - 3s 13ms/step - loss: 1.3227e-04 - accuracy: 1.0000 - val_loss: 0.0634 - val_accuracy: 0.9863 Epoch 44/200 235/235 [==============================] - 3s 13ms/step - loss: 9.6100e-05 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9864 Epoch 45/200 235/235 [==============================] - 3s 13ms/step - loss: 9.5771e-05 - accuracy: 1.0000 - val_loss: 0.0642 - val_accuracy: 0.9863 Epoch 46/200 235/235 [==============================] - 3s 13ms/step - loss: 1.3316e-04 - accuracy: 1.0000 - val_loss: 0.0645 - val_accuracy: 0.9861 Epoch 47/200 235/235 [==============================] - 3s 13ms/step - loss: 8.4740e-05 - accuracy: 1.0000 - val_loss: 0.0654 - val_accuracy: 0.9862 Epoch 48/200 235/235 [==============================] - 3s 13ms/step - loss: 8.8681e-05 - accuracy: 1.0000 - val_loss: 0.0643 - val_accuracy: 0.9864 Epoch 49/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1404 - val_accuracy: 0.9761 Epoch 50/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0286 - accuracy: 0.9909 - val_loss: 0.1092 - val_accuracy: 0.9751 Epoch 51/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0089 - accuracy: 0.9971 - val_loss: 0.0812 - val_accuracy: 0.9816 Epoch 52/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0657 - val_accuracy: 0.9846 Epoch 53/200 235/235 [==============================] - 3s 13ms/step - loss: 9.7368e-04 - accuracy: 0.9999 - val_loss: 0.0676 - val_accuracy: 0.9844 Epoch 54/200 235/235 [==============================] - 3s 13ms/step - loss: 7.5978e-04 - accuracy: 0.9999 - val_loss: 0.0679 - val_accuracy: 0.9846 Epoch 55/200 235/235 [==============================] - 3s 13ms/step - loss: 5.7695e-04 - accuracy: 0.9999 - val_loss: 0.0668 - val_accuracy: 0.9852 Epoch 56/200 235/235 [==============================] - 3s 13ms/step - loss: 3.0574e-04 - accuracy: 1.0000 - val_loss: 0.0664 - val_accuracy: 0.9850 Epoch 57/200 235/235 [==============================] - 3s 13ms/step - loss: 2.2152e-04 - accuracy: 1.0000 - val_loss: 0.0666 - val_accuracy: 0.9850 Epoch 58/200 235/235 [==============================] - 3s 13ms/step - loss: 1.3104e-04 - accuracy: 1.0000 - val_loss: 0.0693 - val_accuracy: 0.9855 Epoch 59/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2827e-04 - accuracy: 1.0000 - val_loss: 0.0665 - val_accuracy: 0.9857 Epoch 60/200 235/235 [==============================] - 3s 13ms/step - loss: 2.4903e-04 - accuracy: 0.9999 - val_loss: 0.0782 - val_accuracy: 0.9834 Epoch 61/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.1336 - val_accuracy: 0.9706 Epoch 62/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0155 - accuracy: 0.9950 - val_loss: 0.0894 - val_accuracy: 0.9817 Epoch 63/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0845 - val_accuracy: 0.9819 Epoch 64/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0768 - val_accuracy: 0.9842 Epoch 65/200 235/235 [==============================] - 3s 13ms/step - loss: 4.7961e-04 - accuracy: 0.9999 - val_loss: 0.0749 - val_accuracy: 0.9846 Epoch 66/200 235/235 [==============================] - 3s 13ms/step - loss: 2.1002e-04 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9854 Epoch 67/200 235/235 [==============================] - 3s 13ms/step - loss: 1.3170e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9847 Epoch 68/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0363e-04 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 0.9854 Epoch 69/200 235/235 [==============================] - 3s 13ms/step - loss: 8.2688e-05 - accuracy: 1.0000 - val_loss: 0.0730 - val_accuracy: 0.9855 Epoch 70/200 235/235 [==============================] - 3s 13ms/step - loss: 7.3269e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9847 Epoch 71/200 235/235 [==============================] - 3s 13ms/step - loss: 6.0541e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9850 Epoch 72/200 235/235 [==============================] - 3s 13ms/step - loss: 5.9681e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9855 Epoch 73/200 235/235 [==============================] - 3s 13ms/step - loss: 5.8945e-05 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9856 Epoch 74/200 235/235 [==============================] - 3s 13ms/step - loss: 4.1104e-05 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9854 Epoch 75/200 235/235 [==============================] - 3s 13ms/step - loss: 3.7607e-05 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9850 Epoch 76/200 235/235 [==============================] - 3s 13ms/step - loss: 5.8262e-05 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9844 Epoch 77/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9995 - val_loss: 0.2597 - val_accuracy: 0.9580 Epoch 78/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0228 - accuracy: 0.9926 - val_loss: 0.1128 - val_accuracy: 0.9766 Epoch 79/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0804 - val_accuracy: 0.9824 Epoch 80/200 235/235 [==============================] - 3s 15ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0761 - val_accuracy: 0.9830 Epoch 81/200 235/235 [==============================] - 3s 13ms/step - loss: 7.6214e-04 - accuracy: 0.9999 - val_loss: 0.0722 - val_accuracy: 0.9841 Epoch 82/200 235/235 [==============================] - 3s 13ms/step - loss: 3.1554e-04 - accuracy: 0.9999 - val_loss: 0.0712 - val_accuracy: 0.9848 Epoch 83/200 235/235 [==============================] - 3s 13ms/step - loss: 1.7432e-04 - accuracy: 1.0000 - val_loss: 0.0726 - val_accuracy: 0.9852 Epoch 84/200 235/235 [==============================] - 3s 13ms/step - loss: 2.9353e-04 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9851 Epoch 85/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2013e-04 - accuracy: 1.0000 - val_loss: 0.0726 - val_accuracy: 0.9857 Epoch 86/200 235/235 [==============================] - 3s 13ms/step - loss: 8.8858e-05 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9849 Epoch 87/200 235/235 [==============================] - 3s 13ms/step - loss: 6.8913e-05 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9851 Epoch 88/200 235/235 [==============================] - 3s 13ms/step - loss: 5.5074e-05 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9852 Epoch 89/200 235/235 [==============================] - 3s 13ms/step - loss: 4.9412e-05 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9851 Epoch 90/200 235/235 [==============================] - 3s 13ms/step - loss: 6.4044e-04 - accuracy: 0.9998 - val_loss: 0.0771 - val_accuracy: 0.9841 Epoch 91/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0039 - accuracy: 0.9989 - val_loss: 0.1449 - val_accuracy: 0.9723 Epoch 92/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0111 - accuracy: 0.9961 - val_loss: 0.0946 - val_accuracy: 0.9806 Epoch 93/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0862 - val_accuracy: 0.9823 Epoch 94/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.0824 - val_accuracy: 0.9828 Epoch 95/200 235/235 [==============================] - 3s 14ms/step - loss: 6.0720e-04 - accuracy: 0.9999 - val_loss: 0.0827 - val_accuracy: 0.9826 Epoch 96/200 235/235 [==============================] - 3s 14ms/step - loss: 1.7349e-04 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9837 Epoch 97/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0773e-04 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9838 Epoch 98/200 235/235 [==============================] - 3s 13ms/step - loss: 7.5179e-05 - accuracy: 1.0000 - val_loss: 0.0790 - val_accuracy: 0.9842 Epoch 99/200 235/235 [==============================] - 3s 13ms/step - loss: 6.9671e-05 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9841 Epoch 100/200 235/235 [==============================] - 3s 13ms/step - loss: 6.3644e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9840 Epoch 101/200 235/235 [==============================] - 2s 11ms/step - loss: 4.7930e-04 - accuracy: 0.9999 - val_loss: 0.0934 - val_accuracy: 0.9810 Epoch 102/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.1061 - val_accuracy: 0.9796 Epoch 103/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.1127 - val_accuracy: 0.9778 Epoch 104/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.0850 - val_accuracy: 0.9834 Epoch 105/200 235/235 [==============================] - 3s 13ms/step - loss: 8.6719e-04 - accuracy: 0.9998 - val_loss: 0.0896 - val_accuracy: 0.9832 Epoch 106/200 235/235 [==============================] - 3s 13ms/step - loss: 7.0985e-04 - accuracy: 0.9998 - val_loss: 0.0881 - val_accuracy: 0.9823 Epoch 107/200 235/235 [==============================] - 3s 13ms/step - loss: 2.9134e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9834 Epoch 108/200 235/235 [==============================] - 3s 13ms/step - loss: 3.8053e-04 - accuracy: 0.9999 - val_loss: 0.0876 - val_accuracy: 0.9840 Epoch 109/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0990e-04 - accuracy: 1.0000 - val_loss: 0.0874 - val_accuracy: 0.9842 Epoch 110/200 235/235 [==============================] - 3s 13ms/step - loss: 6.3434e-05 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9842 Epoch 111/200 235/235 [==============================] - 3s 13ms/step - loss: 4.4527e-05 - accuracy: 1.0000 - val_loss: 0.0862 - val_accuracy: 0.9841 Epoch 112/200 235/235 [==============================] - 3s 13ms/step - loss: 5.1674e-05 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 0.9844 Epoch 113/200 235/235 [==============================] - 3s 13ms/step - loss: 4.1969e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9848 Epoch 114/200 235/235 [==============================] - 3s 13ms/step - loss: 3.5263e-05 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9846 Epoch 115/200 235/235 [==============================] - 3s 13ms/step - loss: 2.5140e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9847 Epoch 116/200 235/235 [==============================] - 3s 13ms/step - loss: 2.0595e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9847 Epoch 117/200 235/235 [==============================] - 3s 13ms/step - loss: 1.7066e-05 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9846 Epoch 118/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4851e-05 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9843 Epoch 119/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4968e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9851 Epoch 120/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4227e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9849 Epoch 121/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2198e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9846 Epoch 122/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1184e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9849 Epoch 123/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0178e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9848 Epoch 124/200 235/235 [==============================] - 3s 13ms/step - loss: 8.6506e-06 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9846 Epoch 125/200 235/235 [==============================] - 3s 13ms/step - loss: 8.0232e-06 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9847 Epoch 126/200 235/235 [==============================] - 3s 13ms/step - loss: 8.0942e-06 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9846 Epoch 127/200 235/235 [==============================] - 3s 13ms/step - loss: 7.7319e-06 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9847 Epoch 128/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0390e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9846 Epoch 129/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0092 - accuracy: 0.9974 - val_loss: 0.1943 - val_accuracy: 0.9661 Epoch 130/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0960 - val_accuracy: 0.9825 Epoch 131/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.0897 - val_accuracy: 0.9839 Epoch 132/200 235/235 [==============================] - 3s 13ms/step - loss: 3.5302e-04 - accuracy: 0.9999 - val_loss: 0.0891 - val_accuracy: 0.9846 Epoch 133/200 235/235 [==============================] - 3s 13ms/step - loss: 1.6709e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9842 Epoch 134/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1259e-04 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9843 Epoch 135/200 235/235 [==============================] - 3s 13ms/step - loss: 8.7330e-05 - accuracy: 1.0000 - val_loss: 0.0887 - val_accuracy: 0.9842 Epoch 136/200 235/235 [==============================] - 3s 13ms/step - loss: 6.5443e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9843 Epoch 137/200 235/235 [==============================] - 3s 13ms/step - loss: 6.3298e-05 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9840 Epoch 138/200 235/235 [==============================] - 3s 13ms/step - loss: 2.3583e-04 - accuracy: 0.9999 - val_loss: 0.0889 - val_accuracy: 0.9836 Epoch 139/200 235/235 [==============================] - 3s 13ms/step - loss: 1.7986e-04 - accuracy: 0.9999 - val_loss: 0.0912 - val_accuracy: 0.9842 Epoch 140/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1250 - val_accuracy: 0.9778 Epoch 141/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1199 - val_accuracy: 0.9799 Epoch 142/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.1083 - val_accuracy: 0.9822 Epoch 143/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1050 - val_accuracy: 0.9829 Epoch 144/200 235/235 [==============================] - 3s 13ms/step - loss: 4.2916e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9836 Epoch 145/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2900e-04 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9843 Epoch 146/200 235/235 [==============================] - 3s 13ms/step - loss: 3.2260e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9840 Epoch 147/200 235/235 [==============================] - 3s 13ms/step - loss: 3.0878e-04 - accuracy: 0.9999 - val_loss: 0.0993 - val_accuracy: 0.9834 Epoch 148/200 235/235 [==============================] - 3s 13ms/step - loss: 1.5986e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9838 Epoch 149/200 235/235 [==============================] - 3s 14ms/step - loss: 4.6506e-05 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9836 Epoch 150/200 235/235 [==============================] - 3s 13ms/step - loss: 9.3467e-05 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9841 Epoch 151/200 235/235 [==============================] - 3s 13ms/step - loss: 7.6336e-05 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9850 Epoch 152/200 235/235 [==============================] - 3s 14ms/step - loss: 3.4568e-05 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9847 Epoch 153/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3326e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9831 Epoch 154/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1169 - val_accuracy: 0.9797 Epoch 155/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.1048 - val_accuracy: 0.9814 Epoch 156/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1043 - val_accuracy: 0.9841 Epoch 157/200 235/235 [==============================] - 3s 14ms/step - loss: 9.2297e-04 - accuracy: 0.9998 - val_loss: 0.1003 - val_accuracy: 0.9846 Epoch 158/200 235/235 [==============================] - 3s 14ms/step - loss: 2.9797e-04 - accuracy: 0.9999 - val_loss: 0.0965 - val_accuracy: 0.9858 Epoch 159/200 235/235 [==============================] - 3s 15ms/step - loss: 1.9169e-04 - accuracy: 0.9999 - val_loss: 0.0968 - val_accuracy: 0.9854 Epoch 160/200 235/235 [==============================] - 3s 14ms/step - loss: 5.8025e-04 - accuracy: 0.9999 - val_loss: 0.0951 - val_accuracy: 0.9855 Epoch 161/200 235/235 [==============================] - 4s 15ms/step - loss: 2.7857e-04 - accuracy: 0.9999 - val_loss: 0.0920 - val_accuracy: 0.9856 Epoch 162/200 235/235 [==============================] - 4s 15ms/step - loss: 1.3455e-04 - accuracy: 1.0000 - val_loss: 0.0935 - val_accuracy: 0.9858 Epoch 163/200 235/235 [==============================] - 4s 15ms/step - loss: 2.7533e-04 - accuracy: 0.9999 - val_loss: 0.0920 - val_accuracy: 0.9857 Epoch 164/200 235/235 [==============================] - 4s 15ms/step - loss: 2.9938e-04 - accuracy: 0.9999 - val_loss: 0.0915 - val_accuracy: 0.9853 Epoch 165/200 235/235 [==============================] - 3s 15ms/step - loss: 9.3321e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9861 Epoch 166/200 235/235 [==============================] - 3s 15ms/step - loss: 3.1419e-04 - accuracy: 0.9999 - val_loss: 0.0967 - val_accuracy: 0.9852 Epoch 167/200 235/235 [==============================] - 3s 15ms/step - loss: 3.7885e-04 - accuracy: 0.9999 - val_loss: 0.0988 - val_accuracy: 0.9859 Epoch 168/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0869e-04 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9852 Epoch 169/200 235/235 [==============================] - 3s 14ms/step - loss: 2.6530e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9858 Epoch 170/200 235/235 [==============================] - 4s 15ms/step - loss: 2.5307e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9852 Epoch 171/200 235/235 [==============================] - 4s 15ms/step - loss: 2.1889e-05 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9863 Epoch 172/200 235/235 [==============================] - 3s 14ms/step - loss: 2.1447e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9858 Epoch 173/200 235/235 [==============================] - 3s 14ms/step - loss: 1.4869e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9853 Epoch 174/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0171e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9854 Epoch 175/200 235/235 [==============================] - 3s 15ms/step - loss: 8.7902e-06 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9856 Epoch 176/200 235/235 [==============================] - 3s 14ms/step - loss: 1.3818e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9856 Epoch 177/200 235/235 [==============================] - 3s 14ms/step - loss: 6.9275e-06 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9860 Epoch 178/200 235/235 [==============================] - 3s 14ms/step - loss: 7.0674e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9858 Epoch 179/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9991 - val_loss: 0.1946 - val_accuracy: 0.9686 Epoch 180/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0123 - accuracy: 0.9966 - val_loss: 0.1087 - val_accuracy: 0.9823 Epoch 181/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1076 - val_accuracy: 0.9831 Epoch 182/200 235/235 [==============================] - 3s 14ms/step - loss: 4.1651e-04 - accuracy: 0.9999 - val_loss: 0.0969 - val_accuracy: 0.9844 Epoch 183/200 235/235 [==============================] - 3s 14ms/step - loss: 1.0187e-04 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9841 Epoch 184/200 235/235 [==============================] - 3s 14ms/step - loss: 7.6745e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9846 Epoch 185/200 235/235 [==============================] - 3s 14ms/step - loss: 6.3686e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9845 Epoch 186/200 235/235 [==============================] - 4s 15ms/step - loss: 4.2680e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9845 Epoch 187/200 235/235 [==============================] - 3s 14ms/step - loss: 4.0016e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9848 Epoch 188/200 235/235 [==============================] - 3s 14ms/step - loss: 2.9176e-05 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9848 Epoch 189/200 235/235 [==============================] - 3s 14ms/step - loss: 2.8233e-05 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9848 Epoch 190/200 235/235 [==============================] - 3s 14ms/step - loss: 2.2996e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9850 Epoch 191/200 235/235 [==============================] - 3s 14ms/step - loss: 1.8016e-04 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9851 Epoch 192/200 235/235 [==============================] - 3s 15ms/step - loss: 9.6464e-05 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9849 Epoch 193/200 235/235 [==============================] - 3s 14ms/step - loss: 3.1440e-04 - accuracy: 0.9999 - val_loss: 0.1198 - val_accuracy: 0.9803 Epoch 194/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.1195 - val_accuracy: 0.9799 Epoch 195/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9989 - val_loss: 0.1102 - val_accuracy: 0.9824 Epoch 196/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.1122 - val_accuracy: 0.9826 Epoch 197/200 235/235 [==============================] - 3s 14ms/step - loss: 9.9016e-04 - accuracy: 0.9997 - val_loss: 0.1051 - val_accuracy: 0.9822 Epoch 198/200 235/235 [==============================] - 3s 14ms/step - loss: 3.0982e-04 - accuracy: 0.9999 - val_loss: 0.1049 - val_accuracy: 0.9828 Epoch 199/200 235/235 [==============================] - 3s 14ms/step - loss: 9.3398e-05 - accuracy: 1.0000 - val_loss: 0.1020 - val_accuracy: 0.9831 Epoch 200/200 235/235 [==============================] - 3s 14ms/step - loss: 8.2174e-05 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9837 Epoch 1/200 235/235 [==============================] - 3s 10ms/step - loss: 1.5630 - accuracy: 0.8562 - val_loss: 0.9242 - val_accuracy: 0.9017 Epoch 2/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8750 - accuracy: 0.8969 - val_loss: 0.8301 - val_accuracy: 0.8999 Epoch 3/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8358 - accuracy: 0.8970 - val_loss: 0.8150 - val_accuracy: 0.8979 Epoch 4/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8247 - accuracy: 0.8966 - val_loss: 0.8085 - val_accuracy: 0.8968 Epoch 5/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8188 - accuracy: 0.8967 - val_loss: 0.8032 - val_accuracy: 0.8976 Epoch 6/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8154 - accuracy: 0.8964 - val_loss: 0.8003 - val_accuracy: 0.8972 Epoch 7/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8125 - accuracy: 0.8972 - val_loss: 0.7985 - val_accuracy: 0.8979 Epoch 8/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8114 - accuracy: 0.8973 - val_loss: 0.7966 - val_accuracy: 0.8983 Epoch 9/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8102 - accuracy: 0.8979 - val_loss: 0.7955 - val_accuracy: 0.8986 Epoch 10/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8093 - accuracy: 0.8978 - val_loss: 0.7934 - val_accuracy: 0.8995 Epoch 11/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8084 - accuracy: 0.8979 - val_loss: 0.7931 - val_accuracy: 0.8998 Epoch 12/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8082 - accuracy: 0.8978 - val_loss: 0.7923 - val_accuracy: 0.8993 Epoch 13/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8080 - accuracy: 0.8982 - val_loss: 0.7910 - val_accuracy: 0.9003 Epoch 14/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8072 - accuracy: 0.8981 - val_loss: 0.7921 - val_accuracy: 0.8998 Epoch 15/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8072 - accuracy: 0.8983 - val_loss: 0.7922 - val_accuracy: 0.8991 Epoch 16/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8067 - accuracy: 0.8980 - val_loss: 0.7909 - val_accuracy: 0.8995 Epoch 17/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8065 - accuracy: 0.8983 - val_loss: 0.7899 - val_accuracy: 0.9004 Epoch 18/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8061 - accuracy: 0.8982 - val_loss: 0.7904 - val_accuracy: 0.9005 Epoch 19/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8062 - accuracy: 0.8982 - val_loss: 0.7908 - val_accuracy: 0.9000 Epoch 20/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8062 - accuracy: 0.8980 - val_loss: 0.7892 - val_accuracy: 0.9009 Epoch 21/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8985 - val_loss: 0.7891 - val_accuracy: 0.9009 Epoch 22/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8056 - accuracy: 0.8987 - val_loss: 0.7901 - val_accuracy: 0.9003 Epoch 23/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8057 - accuracy: 0.8985 - val_loss: 0.7892 - val_accuracy: 0.9005 Epoch 24/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8982 - val_loss: 0.7893 - val_accuracy: 0.9009 Epoch 25/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8054 - accuracy: 0.8985 - val_loss: 0.7887 - val_accuracy: 0.9009 Epoch 26/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8054 - accuracy: 0.8984 - val_loss: 0.7895 - val_accuracy: 0.9013 Epoch 27/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8985 - val_loss: 0.7889 - val_accuracy: 0.9016 Epoch 28/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.8989 - val_loss: 0.7889 - val_accuracy: 0.9009 Epoch 29/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.8988 - val_loss: 0.7890 - val_accuracy: 0.9011 Epoch 30/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8052 - accuracy: 0.8985 - val_loss: 0.7887 - val_accuracy: 0.9009 Epoch 31/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8052 - accuracy: 0.8987 - val_loss: 0.7883 - val_accuracy: 0.9010 Epoch 32/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.8989 - val_loss: 0.7891 - val_accuracy: 0.9015 Epoch 33/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.8989 - val_loss: 0.7890 - val_accuracy: 0.9008 Epoch 34/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.8987 - val_loss: 0.7884 - val_accuracy: 0.9011 Epoch 35/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8049 - accuracy: 0.8986 - val_loss: 0.7879 - val_accuracy: 0.9012 Epoch 36/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8988 - val_loss: 0.7887 - val_accuracy: 0.9015 Epoch 37/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8047 - accuracy: 0.8988 - val_loss: 0.7876 - val_accuracy: 0.9009 Epoch 38/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8989 - val_loss: 0.7883 - val_accuracy: 0.9013 Epoch 39/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8988 - val_loss: 0.7877 - val_accuracy: 0.9013 Epoch 40/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.8985 - val_loss: 0.7888 - val_accuracy: 0.9013 Epoch 41/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8047 - accuracy: 0.8984 - val_loss: 0.7879 - val_accuracy: 0.9017 Epoch 42/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8990 - val_loss: 0.7892 - val_accuracy: 0.9008 Epoch 43/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8990 - val_loss: 0.7881 - val_accuracy: 0.9019 Epoch 44/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.8988 - val_loss: 0.7883 - val_accuracy: 0.9014 Epoch 45/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8987 - val_loss: 0.7881 - val_accuracy: 0.9006 Epoch 46/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8985 - val_loss: 0.7882 - val_accuracy: 0.9009 Epoch 47/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8987 - val_loss: 0.7883 - val_accuracy: 0.9015 Epoch 48/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8047 - accuracy: 0.8986 - val_loss: 0.7885 - val_accuracy: 0.9017 Epoch 49/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9024 Epoch 50/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8986 - val_loss: 0.7885 - val_accuracy: 0.9004 Epoch 51/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.8985 - val_loss: 0.7877 - val_accuracy: 0.9018 Epoch 52/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9013 Epoch 53/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8992 - val_loss: 0.7876 - val_accuracy: 0.9020 Epoch 54/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8988 - val_loss: 0.7861 - val_accuracy: 0.9018 Epoch 55/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.8988 - val_loss: 0.7875 - val_accuracy: 0.9016 Epoch 56/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9022 Epoch 57/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8990 - val_loss: 0.7877 - val_accuracy: 0.9017 Epoch 58/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7876 - val_accuracy: 0.9018 Epoch 59/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8045 - accuracy: 0.8990 - val_loss: 0.7864 - val_accuracy: 0.9017 Epoch 60/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7872 - val_accuracy: 0.9017 Epoch 61/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8990 - val_loss: 0.7865 - val_accuracy: 0.9021 Epoch 62/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8990 - val_loss: 0.7861 - val_accuracy: 0.9017 Epoch 63/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8989 - val_loss: 0.7871 - val_accuracy: 0.9024 Epoch 64/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9015 Epoch 65/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8989 - val_loss: 0.7874 - val_accuracy: 0.9013 Epoch 66/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8990 - val_loss: 0.7874 - val_accuracy: 0.9018 Epoch 67/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8987 - val_loss: 0.7877 - val_accuracy: 0.9016 Epoch 68/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8992 - val_loss: 0.7877 - val_accuracy: 0.9020 Epoch 69/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9020 Epoch 70/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8992 - val_loss: 0.7869 - val_accuracy: 0.9024 Epoch 71/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8988 - val_loss: 0.7870 - val_accuracy: 0.9016 Epoch 72/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8991 - val_loss: 0.7873 - val_accuracy: 0.9016 Epoch 73/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8990 - val_loss: 0.7869 - val_accuracy: 0.9018 Epoch 74/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9020 Epoch 75/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9021 Epoch 76/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8990 - val_loss: 0.7872 - val_accuracy: 0.9017 Epoch 77/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8986 - val_loss: 0.7868 - val_accuracy: 0.9019 Epoch 78/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8988 - val_loss: 0.7864 - val_accuracy: 0.9018 Epoch 79/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7870 - val_accuracy: 0.9014 Epoch 80/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9020 Epoch 81/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9016 Epoch 82/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7859 - val_accuracy: 0.9019 Epoch 83/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8990 - val_loss: 0.7870 - val_accuracy: 0.9021 Epoch 84/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7872 - val_accuracy: 0.9021 Epoch 85/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8987 - val_loss: 0.7873 - val_accuracy: 0.9019 Epoch 86/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7876 - val_accuracy: 0.9016 Epoch 87/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7872 - val_accuracy: 0.9017 Epoch 88/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7877 - val_accuracy: 0.9015 Epoch 89/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8994 - val_loss: 0.7877 - val_accuracy: 0.9012 Epoch 90/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7866 - val_accuracy: 0.9019 Epoch 91/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7874 - val_accuracy: 0.9024 Epoch 92/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9019 Epoch 93/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8993 - val_loss: 0.7875 - val_accuracy: 0.9019 Epoch 94/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7875 - val_accuracy: 0.9023 Epoch 95/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7877 - val_accuracy: 0.9014 Epoch 96/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8988 - val_loss: 0.7871 - val_accuracy: 0.9017 Epoch 97/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.8991 - val_loss: 0.7878 - val_accuracy: 0.9020 Epoch 98/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9020 Epoch 99/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8988 - val_loss: 0.7866 - val_accuracy: 0.9016 Epoch 100/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9021 Epoch 101/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7866 - val_accuracy: 0.9021 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9015 Epoch 103/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8986 - val_loss: 0.7879 - val_accuracy: 0.9020 Epoch 104/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8991 - val_loss: 0.7870 - val_accuracy: 0.9020 Epoch 105/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8991 - val_loss: 0.7866 - val_accuracy: 0.9022 Epoch 106/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8990 - val_loss: 0.7868 - val_accuracy: 0.9009 Epoch 107/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8992 - val_loss: 0.7861 - val_accuracy: 0.9023 Epoch 108/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8995 - val_loss: 0.7876 - val_accuracy: 0.9012 Epoch 109/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7871 - val_accuracy: 0.9018 Epoch 110/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7866 - val_accuracy: 0.9022 Epoch 111/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9020 Epoch 112/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7867 - val_accuracy: 0.9013 Epoch 113/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8989 - val_loss: 0.7872 - val_accuracy: 0.9021 Epoch 114/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8997 - val_loss: 0.7866 - val_accuracy: 0.9018 Epoch 115/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8989 - val_loss: 0.7874 - val_accuracy: 0.9018 Epoch 116/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7862 - val_accuracy: 0.9021 Epoch 117/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9018 Epoch 118/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7874 - val_accuracy: 0.9011 Epoch 119/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8991 - val_loss: 0.7870 - val_accuracy: 0.9022 Epoch 120/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7868 - val_accuracy: 0.9021 Epoch 121/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8992 - val_loss: 0.7868 - val_accuracy: 0.9026 Epoch 122/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8988 - val_loss: 0.7867 - val_accuracy: 0.9023 Epoch 123/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9022 Epoch 124/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8988 - val_loss: 0.7872 - val_accuracy: 0.9021 Epoch 125/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7868 - val_accuracy: 0.9022 Epoch 126/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9013 Epoch 127/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8990 - val_loss: 0.7869 - val_accuracy: 0.9019 Epoch 128/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9020 Epoch 129/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9020 Epoch 130/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7866 - val_accuracy: 0.9025 Epoch 131/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7863 - val_accuracy: 0.9025 Epoch 132/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7869 - val_accuracy: 0.9018 Epoch 133/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7871 - val_accuracy: 0.9018 Epoch 134/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8995 - val_loss: 0.7877 - val_accuracy: 0.9017 Epoch 135/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9017 Epoch 136/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8994 - val_loss: 0.7871 - val_accuracy: 0.9019 Epoch 137/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7859 - val_accuracy: 0.9022 Epoch 138/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8992 - val_loss: 0.7871 - val_accuracy: 0.9018 Epoch 139/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.8988 - val_loss: 0.7871 - val_accuracy: 0.9021 Epoch 140/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7865 - val_accuracy: 0.9015 Epoch 141/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8991 - val_loss: 0.7866 - val_accuracy: 0.9020 Epoch 142/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7868 - val_accuracy: 0.9017 Epoch 143/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8991 - val_loss: 0.7874 - val_accuracy: 0.9021 Epoch 144/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7862 - val_accuracy: 0.9020 Epoch 145/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7866 - val_accuracy: 0.9018 Epoch 146/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9014 Epoch 147/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8988 - val_loss: 0.7864 - val_accuracy: 0.9019 Epoch 148/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8994 - val_loss: 0.7875 - val_accuracy: 0.9013 Epoch 149/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8993 - val_loss: 0.7865 - val_accuracy: 0.9024 Epoch 150/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8994 - val_loss: 0.7868 - val_accuracy: 0.9019 Epoch 151/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7873 - val_accuracy: 0.9020 Epoch 152/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8986 - val_loss: 0.7868 - val_accuracy: 0.9016 Epoch 153/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8991 - val_loss: 0.7870 - val_accuracy: 0.9023 Epoch 154/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8994 - val_loss: 0.7866 - val_accuracy: 0.9022 Epoch 155/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7862 - val_accuracy: 0.9018 Epoch 156/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8992 - val_loss: 0.7869 - val_accuracy: 0.9020 Epoch 157/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9017 Epoch 158/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8991 - val_loss: 0.7872 - val_accuracy: 0.9021 Epoch 159/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.8990 - val_loss: 0.7871 - val_accuracy: 0.9017 Epoch 160/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8994 - val_loss: 0.7872 - val_accuracy: 0.9015 Epoch 161/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8992 - val_loss: 0.7878 - val_accuracy: 0.9011 Epoch 162/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7861 - val_accuracy: 0.9021 Epoch 163/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7866 - val_accuracy: 0.9018 Epoch 164/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7864 - val_accuracy: 0.9020 Epoch 165/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8995 - val_loss: 0.7876 - val_accuracy: 0.9020 Epoch 166/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7864 - val_accuracy: 0.9019 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9022 Epoch 168/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8990 - val_loss: 0.7865 - val_accuracy: 0.9017 Epoch 169/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8996 - val_loss: 0.7871 - val_accuracy: 0.9019 Epoch 170/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8994 - val_loss: 0.7870 - val_accuracy: 0.9015 Epoch 171/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8994 - val_loss: 0.7871 - val_accuracy: 0.9021 Epoch 172/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8991 - val_loss: 0.7868 - val_accuracy: 0.9022 Epoch 173/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7872 - val_accuracy: 0.9021 Epoch 174/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8995 - val_loss: 0.7864 - val_accuracy: 0.9019 Epoch 175/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7870 - val_accuracy: 0.9024 Epoch 176/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7868 - val_accuracy: 0.9018 Epoch 177/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7863 - val_accuracy: 0.9023 Epoch 178/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9022 Epoch 179/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8988 - val_loss: 0.7869 - val_accuracy: 0.9020 Epoch 180/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8988 - val_loss: 0.7873 - val_accuracy: 0.9021 Epoch 181/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8990 - val_loss: 0.7861 - val_accuracy: 0.9019 Epoch 182/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.8990 - val_loss: 0.7874 - val_accuracy: 0.9019 Epoch 183/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7861 - val_accuracy: 0.9022 Epoch 184/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7875 - val_accuracy: 0.9020 Epoch 185/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7870 - val_accuracy: 0.9017 Epoch 186/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8994 - val_loss: 0.7873 - val_accuracy: 0.9014 Epoch 187/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8994 - val_loss: 0.7868 - val_accuracy: 0.9021 Epoch 188/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9018 Epoch 189/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9020 Epoch 190/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7868 - val_accuracy: 0.9020 Epoch 191/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8990 - val_loss: 0.7866 - val_accuracy: 0.9020 Epoch 192/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.8988 - val_loss: 0.7869 - val_accuracy: 0.9021 Epoch 193/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8993 - val_loss: 0.7869 - val_accuracy: 0.9023 Epoch 194/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8992 - val_loss: 0.7873 - val_accuracy: 0.9024 Epoch 195/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8989 - val_loss: 0.7873 - val_accuracy: 0.9025 Epoch 196/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.8992 - val_loss: 0.7864 - val_accuracy: 0.9023 Epoch 197/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8990 - val_loss: 0.7871 - val_accuracy: 0.9024 Epoch 198/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.8993 - val_loss: 0.7865 - val_accuracy: 0.9017 Epoch 199/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.8989 - val_loss: 0.7869 - val_accuracy: 0.9022 Epoch 200/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.8993 - val_loss: 0.7859 - val_accuracy: 0.9024 Epoch 1/200 235/235 [==============================] - 2s 8ms/step - loss: 0.4727 - accuracy: 0.8708 - val_loss: 0.2562 - val_accuracy: 0.9248 Epoch 2/200 235/235 [==============================] - 2s 8ms/step - loss: 0.2279 - accuracy: 0.9343 - val_loss: 0.1894 - val_accuracy: 0.9429 Epoch 3/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1709 - accuracy: 0.9510 - val_loss: 0.1531 - val_accuracy: 0.9549 Epoch 4/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1367 - accuracy: 0.9603 - val_loss: 0.1326 - val_accuracy: 0.9604 Epoch 5/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1131 - accuracy: 0.9664 - val_loss: 0.1207 - val_accuracy: 0.9624 Epoch 6/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0953 - accuracy: 0.9717 - val_loss: 0.1125 - val_accuracy: 0.9651 Epoch 7/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0812 - accuracy: 0.9758 - val_loss: 0.1078 - val_accuracy: 0.9670 Epoch 8/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0699 - accuracy: 0.9790 - val_loss: 0.1033 - val_accuracy: 0.9676 Epoch 9/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0603 - accuracy: 0.9823 - val_loss: 0.1012 - val_accuracy: 0.9686 Epoch 10/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0519 - accuracy: 0.9850 - val_loss: 0.0991 - val_accuracy: 0.9689 Epoch 11/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0448 - accuracy: 0.9876 - val_loss: 0.1000 - val_accuracy: 0.9693 Epoch 12/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0388 - accuracy: 0.9896 - val_loss: 0.0995 - val_accuracy: 0.9699 Epoch 13/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0336 - accuracy: 0.9915 - val_loss: 0.1003 - val_accuracy: 0.9716 Epoch 14/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0289 - accuracy: 0.9929 - val_loss: 0.1015 - val_accuracy: 0.9715 Epoch 15/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0250 - accuracy: 0.9943 - val_loss: 0.1025 - val_accuracy: 0.9718 Epoch 16/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0215 - accuracy: 0.9955 - val_loss: 0.1043 - val_accuracy: 0.9724 Epoch 17/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0186 - accuracy: 0.9965 - val_loss: 0.1068 - val_accuracy: 0.9724 Epoch 18/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.1082 - val_accuracy: 0.9724 Epoch 19/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0138 - accuracy: 0.9978 - val_loss: 0.1132 - val_accuracy: 0.9718 Epoch 20/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0119 - accuracy: 0.9984 - val_loss: 0.1175 - val_accuracy: 0.9717 Epoch 21/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0107 - accuracy: 0.9985 - val_loss: 0.1230 - val_accuracy: 0.9710 Epoch 22/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0095 - accuracy: 0.9986 - val_loss: 0.1242 - val_accuracy: 0.9713 Epoch 23/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9985 - val_loss: 0.1363 - val_accuracy: 0.9706 Epoch 24/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9983 - val_loss: 0.1312 - val_accuracy: 0.9709 Epoch 25/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.1423 - val_accuracy: 0.9709 Epoch 26/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0088 - accuracy: 0.9977 - val_loss: 0.1384 - val_accuracy: 0.9715 Epoch 27/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0078 - accuracy: 0.9981 - val_loss: 0.1490 - val_accuracy: 0.9706 Epoch 28/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0070 - accuracy: 0.9981 - val_loss: 0.1358 - val_accuracy: 0.9725 Epoch 29/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0061 - accuracy: 0.9985 - val_loss: 0.1359 - val_accuracy: 0.9735 Epoch 30/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 0.1346 - val_accuracy: 0.9726 Epoch 31/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 0.1401 - val_accuracy: 0.9728 Epoch 32/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9989 - val_loss: 0.1454 - val_accuracy: 0.9725 Epoch 33/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9995 - val_loss: 0.1540 - val_accuracy: 0.9717 Epoch 34/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9990 - val_loss: 0.1417 - val_accuracy: 0.9732 Epoch 35/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1620 - val_accuracy: 0.9697 Epoch 36/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.1590 - val_accuracy: 0.9701 Epoch 37/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1550 - val_accuracy: 0.9721 Epoch 38/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.1539 - val_accuracy: 0.9729 Epoch 39/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.1458 - val_accuracy: 0.9741 Epoch 40/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0017 - accuracy: 0.9998 - val_loss: 0.1515 - val_accuracy: 0.9727 Epoch 41/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 0.1595 - val_accuracy: 0.9720 Epoch 42/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.1482 - val_accuracy: 0.9737 Epoch 43/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0051 - accuracy: 0.9981 - val_loss: 0.1798 - val_accuracy: 0.9677 Epoch 44/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 0.1630 - val_accuracy: 0.9722 Epoch 45/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.1630 - val_accuracy: 0.9713 Epoch 46/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1505 - val_accuracy: 0.9739 Epoch 47/200 235/235 [==============================] - 2s 8ms/step - loss: 8.2963e-04 - accuracy: 0.9999 - val_loss: 0.1476 - val_accuracy: 0.9747 Epoch 48/200 235/235 [==============================] - 2s 8ms/step - loss: 6.1204e-04 - accuracy: 1.0000 - val_loss: 0.1499 - val_accuracy: 0.9749 Epoch 49/200 235/235 [==============================] - 2s 8ms/step - loss: 6.5627e-04 - accuracy: 0.9999 - val_loss: 0.1532 - val_accuracy: 0.9742 Epoch 50/200 235/235 [==============================] - 2s 8ms/step - loss: 3.0503e-04 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9744 Epoch 51/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2239e-04 - accuracy: 1.0000 - val_loss: 0.1514 - val_accuracy: 0.9746 Epoch 52/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8022e-04 - accuracy: 1.0000 - val_loss: 0.1519 - val_accuracy: 0.9744 Epoch 53/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5741e-04 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9745 Epoch 54/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4068e-04 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9744 Epoch 55/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2738e-04 - accuracy: 1.0000 - val_loss: 0.1533 - val_accuracy: 0.9743 Epoch 56/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1583e-04 - accuracy: 1.0000 - val_loss: 0.1540 - val_accuracy: 0.9744 Epoch 57/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0558e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9744 Epoch 58/200 235/235 [==============================] - 2s 8ms/step - loss: 9.6603e-05 - accuracy: 1.0000 - val_loss: 0.1555 - val_accuracy: 0.9747 Epoch 59/200 235/235 [==============================] - 2s 8ms/step - loss: 8.8239e-05 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9746 Epoch 60/200 235/235 [==============================] - 2s 8ms/step - loss: 8.0766e-05 - accuracy: 1.0000 - val_loss: 0.1572 - val_accuracy: 0.9751 Epoch 61/200 235/235 [==============================] - 2s 8ms/step - loss: 7.3818e-05 - accuracy: 1.0000 - val_loss: 0.1583 - val_accuracy: 0.9752 Epoch 62/200 235/235 [==============================] - 2s 8ms/step - loss: 6.7451e-05 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9752 Epoch 63/200 235/235 [==============================] - 2s 8ms/step - loss: 6.1634e-05 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9754 Epoch 64/200 235/235 [==============================] - 2s 8ms/step - loss: 5.6047e-05 - accuracy: 1.0000 - val_loss: 0.1613 - val_accuracy: 0.9755 Epoch 65/200 235/235 [==============================] - 2s 9ms/step - loss: 5.1083e-05 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9756 Epoch 66/200 235/235 [==============================] - 2s 9ms/step - loss: 4.6468e-05 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9756 Epoch 67/200 235/235 [==============================] - 2s 8ms/step - loss: 4.2245e-05 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9755 Epoch 68/200 235/235 [==============================] - 2s 8ms/step - loss: 3.8341e-05 - accuracy: 1.0000 - val_loss: 0.1661 - val_accuracy: 0.9754 Epoch 69/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4662e-05 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9753 Epoch 70/200 235/235 [==============================] - 2s 8ms/step - loss: 3.1363e-05 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9753 Epoch 71/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8292e-05 - accuracy: 1.0000 - val_loss: 0.1702 - val_accuracy: 0.9753 Epoch 72/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5557e-05 - accuracy: 1.0000 - val_loss: 0.1717 - val_accuracy: 0.9753 Epoch 73/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2980e-05 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9754 Epoch 74/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0661e-05 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9753 Epoch 75/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8541e-05 - accuracy: 1.0000 - val_loss: 0.1761 - val_accuracy: 0.9753 Epoch 76/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6647e-05 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9753 Epoch 77/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4888e-05 - accuracy: 1.0000 - val_loss: 0.1792 - val_accuracy: 0.9752 Epoch 78/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3299e-05 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9752 Epoch 79/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1920e-05 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9750 Epoch 80/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0625e-05 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9749 Epoch 81/200 235/235 [==============================] - 2s 8ms/step - loss: 9.4743e-06 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9749 Epoch 82/200 235/235 [==============================] - 2s 8ms/step - loss: 8.4510e-06 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9749 Epoch 83/200 235/235 [==============================] - 2s 8ms/step - loss: 7.5154e-06 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9748 Epoch 84/200 235/235 [==============================] - 2s 8ms/step - loss: 6.6881e-06 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9750 Epoch 85/200 235/235 [==============================] - 2s 8ms/step - loss: 5.9486e-06 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9749 Epoch 86/200 235/235 [==============================] - 2s 8ms/step - loss: 5.2764e-06 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9750 Epoch 87/200 235/235 [==============================] - 2s 8ms/step - loss: 4.6952e-06 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9751 Epoch 88/200 235/235 [==============================] - 2s 8ms/step - loss: 4.1655e-06 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9752 Epoch 89/200 235/235 [==============================] - 2s 8ms/step - loss: 3.6936e-06 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9751 Epoch 90/200 235/235 [==============================] - 2s 8ms/step - loss: 3.2785e-06 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9752 Epoch 91/200 235/235 [==============================] - 2s 8ms/step - loss: 2.9047e-06 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9752 Epoch 92/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5774e-06 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9752 Epoch 93/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2841e-06 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9750 Epoch 94/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0245e-06 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9749 Epoch 95/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7953e-06 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9749 Epoch 96/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5899e-06 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9748 Epoch 97/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4100e-06 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9749 Epoch 98/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2515e-06 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9749 Epoch 99/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1105e-06 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9747 Epoch 100/200 235/235 [==============================] - 2s 8ms/step - loss: 9.8493e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9748 Epoch 101/200 235/235 [==============================] - 2s 8ms/step - loss: 8.7410e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9746 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 7.7644e-07 - accuracy: 1.0000 - val_loss: 0.2230 - val_accuracy: 0.9746 Epoch 103/200 235/235 [==============================] - 2s 8ms/step - loss: 6.8919e-07 - accuracy: 1.0000 - val_loss: 0.2247 - val_accuracy: 0.9748 Epoch 104/200 235/235 [==============================] - 2s 8ms/step - loss: 6.1290e-07 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.9747 Epoch 105/200 235/235 [==============================] - 2s 8ms/step - loss: 5.4580e-07 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9748 Epoch 106/200 235/235 [==============================] - 2s 8ms/step - loss: 4.8559e-07 - accuracy: 1.0000 - val_loss: 0.2299 - val_accuracy: 0.9747 Epoch 107/200 235/235 [==============================] - 2s 8ms/step - loss: 4.3296e-07 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9747 Epoch 108/200 235/235 [==============================] - 2s 8ms/step - loss: 3.8604e-07 - accuracy: 1.0000 - val_loss: 0.2334 - val_accuracy: 0.9745 Epoch 109/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4529e-07 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9745 Epoch 110/200 235/235 [==============================] - 2s 8ms/step - loss: 3.0814e-07 - accuracy: 1.0000 - val_loss: 0.2368 - val_accuracy: 0.9745 Epoch 111/200 235/235 [==============================] - 2s 7ms/step - loss: 2.7618e-07 - accuracy: 1.0000 - val_loss: 0.2383 - val_accuracy: 0.9745 Epoch 112/200 235/235 [==============================] - 2s 8ms/step - loss: 2.4750e-07 - accuracy: 1.0000 - val_loss: 0.2399 - val_accuracy: 0.9745 Epoch 113/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2214e-07 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9746 Epoch 114/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9964e-07 - accuracy: 1.0000 - val_loss: 0.2431 - val_accuracy: 0.9746 Epoch 115/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7973e-07 - accuracy: 1.0000 - val_loss: 0.2446 - val_accuracy: 0.9745 Epoch 116/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6200e-07 - accuracy: 1.0000 - val_loss: 0.2461 - val_accuracy: 0.9744 Epoch 117/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4651e-07 - accuracy: 1.0000 - val_loss: 0.2476 - val_accuracy: 0.9744 Epoch 118/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3242e-07 - accuracy: 1.0000 - val_loss: 0.2490 - val_accuracy: 0.9745 Epoch 119/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2014e-07 - accuracy: 1.0000 - val_loss: 0.2504 - val_accuracy: 0.9745 Epoch 120/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0935e-07 - accuracy: 1.0000 - val_loss: 0.2517 - val_accuracy: 0.9745 Epoch 121/200 235/235 [==============================] - 2s 8ms/step - loss: 9.9643e-08 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9745 Epoch 122/200 235/235 [==============================] - 2s 8ms/step - loss: 9.0901e-08 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9745 Epoch 123/200 235/235 [==============================] - 2s 8ms/step - loss: 8.3252e-08 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.9745 Epoch 124/200 235/235 [==============================] - 2s 8ms/step - loss: 7.6210e-08 - accuracy: 1.0000 - val_loss: 0.2568 - val_accuracy: 0.9745 Epoch 125/200 235/235 [==============================] - 2s 8ms/step - loss: 7.0079e-08 - accuracy: 1.0000 - val_loss: 0.2580 - val_accuracy: 0.9745 Epoch 126/200 235/235 [==============================] - 2s 8ms/step - loss: 6.4592e-08 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9746 Epoch 127/200 235/235 [==============================] - 2s 8ms/step - loss: 5.9617e-08 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9746 Epoch 128/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5186e-08 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9746 Epoch 129/200 235/235 [==============================] - 2s 8ms/step - loss: 5.1127e-08 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9747 Epoch 130/200 235/235 [==============================] - 2s 8ms/step - loss: 4.7588e-08 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9746 Epoch 131/200 235/235 [==============================] - 2s 8ms/step - loss: 4.4378e-08 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9748 Epoch 132/200 235/235 [==============================] - 2s 8ms/step - loss: 4.1358e-08 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9748 Epoch 133/200 235/235 [==============================] - 2s 8ms/step - loss: 3.8733e-08 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9748 Epoch 134/200 235/235 [==============================] - 2s 8ms/step - loss: 3.6363e-08 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9748 Epoch 135/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4171e-08 - accuracy: 1.0000 - val_loss: 0.2678 - val_accuracy: 0.9749 Epoch 136/200 235/235 [==============================] - 2s 8ms/step - loss: 3.2298e-08 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9749 Epoch 137/200 235/235 [==============================] - 2s 8ms/step - loss: 3.0446e-08 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9749 Epoch 138/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8797e-08 - accuracy: 1.0000 - val_loss: 0.2701 - val_accuracy: 0.9749 Epoch 139/200 235/235 [==============================] - 2s 8ms/step - loss: 2.7237e-08 - accuracy: 1.0000 - val_loss: 0.2708 - val_accuracy: 0.9748 Epoch 140/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5892e-08 - accuracy: 1.0000 - val_loss: 0.2715 - val_accuracy: 0.9748 Epoch 141/200 235/235 [==============================] - 2s 8ms/step - loss: 2.4565e-08 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9749 Epoch 142/200 235/235 [==============================] - 2s 8ms/step - loss: 2.3387e-08 - accuracy: 1.0000 - val_loss: 0.2729 - val_accuracy: 0.9748 Epoch 143/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2316e-08 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9748 Epoch 144/200 235/235 [==============================] - 2s 8ms/step - loss: 2.1350e-08 - accuracy: 1.0000 - val_loss: 0.2742 - val_accuracy: 0.9749 Epoch 145/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0500e-08 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9750 Epoch 146/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9602e-08 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9750 Epoch 147/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8803e-08 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9750 Epoch 148/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8060e-08 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9751 Epoch 149/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7349e-08 - accuracy: 1.0000 - val_loss: 0.2769 - val_accuracy: 0.9750 Epoch 150/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6669e-08 - accuracy: 1.0000 - val_loss: 0.2774 - val_accuracy: 0.9751 Epoch 151/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6123e-08 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.9750 Epoch 152/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5545e-08 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9749 Epoch 153/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5036e-08 - accuracy: 1.0000 - val_loss: 0.2788 - val_accuracy: 0.9750 Epoch 154/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4565e-08 - accuracy: 1.0000 - val_loss: 0.2791 - val_accuracy: 0.9750 Epoch 155/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4077e-08 - accuracy: 1.0000 - val_loss: 0.2796 - val_accuracy: 0.9750 Epoch 156/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3645e-08 - accuracy: 1.0000 - val_loss: 0.2800 - val_accuracy: 0.9750 Epoch 157/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3230e-08 - accuracy: 1.0000 - val_loss: 0.2803 - val_accuracy: 0.9750 Epoch 158/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2837e-08 - accuracy: 1.0000 - val_loss: 0.2806 - val_accuracy: 0.9750 Epoch 159/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2438e-08 - accuracy: 1.0000 - val_loss: 0.2809 - val_accuracy: 0.9750 Epoch 160/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2094e-08 - accuracy: 1.0000 - val_loss: 0.2814 - val_accuracy: 0.9750 Epoch 161/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1750e-08 - accuracy: 1.0000 - val_loss: 0.2816 - val_accuracy: 0.9750 Epoch 162/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1379e-08 - accuracy: 1.0000 - val_loss: 0.2818 - val_accuracy: 0.9749 Epoch 163/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1086e-08 - accuracy: 1.0000 - val_loss: 0.2820 - val_accuracy: 0.9750 Epoch 164/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0786e-08 - accuracy: 1.0000 - val_loss: 0.2824 - val_accuracy: 0.9751 Epoch 165/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0602e-08 - accuracy: 1.0000 - val_loss: 0.2825 - val_accuracy: 0.9749 Epoch 166/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0316e-08 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9750 Epoch 167/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0061e-08 - accuracy: 1.0000 - val_loss: 0.2831 - val_accuracy: 0.9749 Epoch 168/200 235/235 [==============================] - 2s 8ms/step - loss: 9.8288e-09 - accuracy: 1.0000 - val_loss: 0.2832 - val_accuracy: 0.9750 Epoch 169/200 235/235 [==============================] - 2s 8ms/step - loss: 9.6401e-09 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9749 Epoch 170/200 235/235 [==============================] - 2s 8ms/step - loss: 9.3997e-09 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9749 Epoch 171/200 235/235 [==============================] - 2s 8ms/step - loss: 9.2208e-09 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.9749 Epoch 172/200 235/235 [==============================] - 2s 8ms/step - loss: 9.0082e-09 - accuracy: 1.0000 - val_loss: 0.2841 - val_accuracy: 0.9749 Epoch 173/200 235/235 [==============================] - 2s 8ms/step - loss: 8.7758e-09 - accuracy: 1.0000 - val_loss: 0.2843 - val_accuracy: 0.9749 Epoch 174/200 235/235 [==============================] - 2s 8ms/step - loss: 8.6387e-09 - accuracy: 1.0000 - val_loss: 0.2844 - val_accuracy: 0.9749 Epoch 175/200 235/235 [==============================] - 2s 8ms/step - loss: 8.4182e-09 - accuracy: 1.0000 - val_loss: 0.2848 - val_accuracy: 0.9749 Epoch 176/200 235/235 [==============================] - 2s 8ms/step - loss: 8.2751e-09 - accuracy: 1.0000 - val_loss: 0.2851 - val_accuracy: 0.9749 Epoch 177/200 235/235 [==============================] - 2s 8ms/step - loss: 8.1182e-09 - accuracy: 1.0000 - val_loss: 0.2853 - val_accuracy: 0.9749 Epoch 178/200 235/235 [==============================] - 2s 8ms/step - loss: 7.9930e-09 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.9749 Epoch 179/200 235/235 [==============================] - 2s 8ms/step - loss: 7.7486e-09 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9748 Epoch 180/200 235/235 [==============================] - 2s 8ms/step - loss: 7.6751e-09 - accuracy: 1.0000 - val_loss: 0.2857 - val_accuracy: 0.9747 Epoch 181/200 235/235 [==============================] - 2s 8ms/step - loss: 7.5042e-09 - accuracy: 1.0000 - val_loss: 0.2858 - val_accuracy: 0.9748 Epoch 182/200 235/235 [==============================] - 2s 8ms/step - loss: 7.3870e-09 - accuracy: 1.0000 - val_loss: 0.2861 - val_accuracy: 0.9747 Epoch 183/200 235/235 [==============================] - 2s 8ms/step - loss: 7.1903e-09 - accuracy: 1.0000 - val_loss: 0.2862 - val_accuracy: 0.9747 Epoch 184/200 235/235 [==============================] - 2s 8ms/step - loss: 7.0969e-09 - accuracy: 1.0000 - val_loss: 0.2863 - val_accuracy: 0.9748 Epoch 185/200 235/235 [==============================] - 2s 8ms/step - loss: 6.9559e-09 - accuracy: 1.0000 - val_loss: 0.2866 - val_accuracy: 0.9747 Epoch 186/200 235/235 [==============================] - 2s 8ms/step - loss: 6.8247e-09 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9746 Epoch 187/200 235/235 [==============================] - 2s 8ms/step - loss: 6.7055e-09 - accuracy: 1.0000 - val_loss: 0.2869 - val_accuracy: 0.9746 Epoch 188/200 235/235 [==============================] - 2s 8ms/step - loss: 6.5764e-09 - accuracy: 1.0000 - val_loss: 0.2870 - val_accuracy: 0.9745 Epoch 189/200 235/235 [==============================] - 2s 8ms/step - loss: 6.4969e-09 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9746 Epoch 190/200 235/235 [==============================] - 2s 8ms/step - loss: 6.3817e-09 - accuracy: 1.0000 - val_loss: 0.2872 - val_accuracy: 0.9745 Epoch 191/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2744e-09 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9745 Epoch 192/200 235/235 [==============================] - 2s 8ms/step - loss: 6.1631e-09 - accuracy: 1.0000 - val_loss: 0.2875 - val_accuracy: 0.9745 Epoch 193/200 235/235 [==============================] - 2s 8ms/step - loss: 6.0618e-09 - accuracy: 1.0000 - val_loss: 0.2876 - val_accuracy: 0.9746 Epoch 194/200 235/235 [==============================] - 2s 8ms/step - loss: 5.9446e-09 - accuracy: 1.0000 - val_loss: 0.2877 - val_accuracy: 0.9747 Epoch 195/200 235/235 [==============================] - 2s 8ms/step - loss: 5.8671e-09 - accuracy: 1.0000 - val_loss: 0.2878 - val_accuracy: 0.9746 Epoch 196/200 235/235 [==============================] - 2s 8ms/step - loss: 5.7062e-09 - accuracy: 1.0000 - val_loss: 0.2879 - val_accuracy: 0.9746 Epoch 197/200 235/235 [==============================] - 2s 8ms/step - loss: 5.6187e-09 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9747 Epoch 198/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5591e-09 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9747 Epoch 199/200 235/235 [==============================] - 2s 8ms/step - loss: 5.4995e-09 - accuracy: 1.0000 - val_loss: 0.2883 - val_accuracy: 0.9747 Epoch 200/200 235/235 [==============================] - 2s 8ms/step - loss: 5.3863e-09 - accuracy: 1.0000 - val_loss: 0.2884 - val_accuracy: 0.9747 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03702937625348568 Thresholhold 0.04698251932859421 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06157276965677738 Thresholhold 0.09646058827638626 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 0. 0.] [0. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.11527079716324806 Thresholhold 0.05421745777130127 Using suggest threshold. Applying new mask Percentage zeros 0.249 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 0. 1. 1.] [0. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 0. 1. 1. 0. 0. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 1/235 [..............................] - ETA: 4:22:18 - loss: 8.0735 - accuracy: 0.1133WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0086s vs `on_train_batch_begin` time: 11.0853s). Check your callbacks. 235/235 [==============================] - 70s 12ms/step - loss: 2.1990 - accuracy: 0.9126 - val_loss: 1.8356 - val_accuracy: 0.7753 [ 3.2516712e-07 -2.7024671e-06 -3.1244156e-07 ... -2.8429953e-03 -8.9802414e-02 -1.4404383e-01] Sparsity at: 0.4990570999248685 Epoch 2/500 235/235 [==============================] - 3s 13ms/step - loss: 0.4643 - accuracy: 0.9624 - val_loss: 0.6491 - val_accuracy: 0.9479 [-1.9452903e-13 -5.1824933e-12 -7.5400027e-15 ... 3.2725368e-02 -6.4627461e-02 -1.2673180e-01] Sparsity at: 0.4990570999248685 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2954 - accuracy: 0.9670 - val_loss: 0.3028 - val_accuracy: 0.9622 [-7.9141589e-18 -6.5815181e-17 7.1753617e-18 ... 5.0768904e-02 -4.7156829e-02 -9.4213687e-02] Sparsity at: 0.4990570999248685 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2580 - accuracy: 0.9700 - val_loss: 0.2929 - val_accuracy: 0.9549 [ 1.3020048e-23 -2.9036794e-22 -2.1165660e-23 ... 7.3847942e-02 -4.4844553e-02 -6.8516657e-02] Sparsity at: 0.4990570999248685 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2385 - accuracy: 0.9712 - val_loss: 0.2767 - val_accuracy: 0.9564 [-1.6890093e-28 2.3128108e-28 1.7465688e-28 ... 9.8315038e-02 -4.8644219e-02 -5.2487023e-02] Sparsity at: 0.4990570999248685 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2282 - accuracy: 0.9732 - val_loss: 0.2569 - val_accuracy: 0.9619 [ 2.4301027e-35 -7.7633192e-34 -1.5248102e-34 ... 1.2401281e-01 -4.3334827e-02 -3.7429992e-02] Sparsity at: 0.4990570999248685 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2177 - accuracy: 0.9736 - val_loss: 0.2609 - val_accuracy: 0.9583 [ 2.4301027e-35 6.2977183e-34 -1.5248102e-34 ... 1.3538611e-01 -3.9015461e-02 -3.7069984e-02] Sparsity at: 0.4990570999248685 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2097 - accuracy: 0.9745 - val_loss: 0.2403 - val_accuracy: 0.9628 [ 2.4301027e-35 6.2977183e-34 -1.5248102e-34 ... 1.4269824e-01 -3.0737000e-02 -3.2334145e-02] Sparsity at: 0.4990570999248685 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2014 - accuracy: 0.9748 - val_loss: 0.2477 - val_accuracy: 0.9595 [ 2.4301027e-35 6.2977183e-34 -1.5248102e-34 ... 1.3941075e-01 -3.3443172e-02 -2.8537080e-02] Sparsity at: 0.4990570999248685 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1980 - accuracy: 0.9753 - val_loss: 0.2311 - val_accuracy: 0.9620 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4180030e-01 -2.9412949e-02 -2.0636633e-02] Sparsity at: 0.4990570999248685 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1903 - accuracy: 0.9766 - val_loss: 0.2325 - val_accuracy: 0.9609 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4770201e-01 -3.2879747e-02 -2.1796416e-02] Sparsity at: 0.49906085649887305 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1896 - accuracy: 0.9757 - val_loss: 0.2238 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4073397e-01 -3.4778457e-02 -2.2761177e-02] Sparsity at: 0.49906085649887305 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1857 - accuracy: 0.9757 - val_loss: 0.2309 - val_accuracy: 0.9602 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4807923e-01 -3.5239980e-02 -2.5581982e-02] Sparsity at: 0.49906085649887305 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1793 - accuracy: 0.9771 - val_loss: 0.2315 - val_accuracy: 0.9569 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4449796e-01 -3.4427825e-02 -3.0831164e-02] Sparsity at: 0.49906085649887305 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1800 - accuracy: 0.9763 - val_loss: 0.2507 - val_accuracy: 0.9522 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4495338e-01 -2.9290933e-02 -2.6060814e-02] Sparsity at: 0.49906085649887305 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1751 - accuracy: 0.9772 - val_loss: 0.2194 - val_accuracy: 0.9624 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4387380e-01 -2.3434890e-02 -2.2215407e-02] Sparsity at: 0.49906085649887305 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1730 - accuracy: 0.9766 - val_loss: 0.2173 - val_accuracy: 0.9624 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.4289762e-01 -2.4991723e-02 -2.3437455e-02] Sparsity at: 0.4990646130728775 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1698 - accuracy: 0.9777 - val_loss: 0.2256 - val_accuracy: 0.9595 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.3880359e-01 -2.8190466e-02 -2.4149274e-02] Sparsity at: 0.4990646130728775 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1693 - accuracy: 0.9771 - val_loss: 0.2353 - val_accuracy: 0.9577 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2990518e-01 -2.3204282e-02 -2.1257430e-02] Sparsity at: 0.4990646130728775 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1658 - accuracy: 0.9780 - val_loss: 0.2371 - val_accuracy: 0.9563 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.3673255e-01 -3.3271465e-02 -2.2169089e-02] Sparsity at: 0.4990646130728775 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1600 - accuracy: 0.9791 - val_loss: 0.2395 - val_accuracy: 0.9546 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.3372675e-01 -3.1127065e-02 -2.3714870e-02] Sparsity at: 0.4990646130728775 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1623 - accuracy: 0.9779 - val_loss: 0.2168 - val_accuracy: 0.9614 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.3705161e-01 -3.4036573e-02 -2.0164441e-02] Sparsity at: 0.4990646130728775 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1633 - accuracy: 0.9774 - val_loss: 0.2121 - val_accuracy: 0.9617 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2779360e-01 -4.0591486e-02 -1.7904308e-02] Sparsity at: 0.4990646130728775 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1603 - accuracy: 0.9780 - val_loss: 0.2371 - val_accuracy: 0.9561 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.3419038e-01 -3.9705627e-02 -9.8506780e-03] Sparsity at: 0.4990646130728775 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1587 - accuracy: 0.9784 - val_loss: 0.2150 - val_accuracy: 0.9596 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2982833e-01 -3.4766037e-02 -6.1551309e-03] Sparsity at: 0.4990646130728775 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1567 - accuracy: 0.9781 - val_loss: 0.2189 - val_accuracy: 0.9574 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2714484e-01 -3.4287896e-02 -6.4728386e-03] Sparsity at: 0.4990646130728775 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1586 - accuracy: 0.9778 - val_loss: 0.2277 - val_accuracy: 0.9579 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2906639e-01 -3.1838134e-02 -1.1366219e-02] Sparsity at: 0.4990646130728775 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1522 - accuracy: 0.9793 - val_loss: 0.2013 - val_accuracy: 0.9639 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2826347e-01 -3.3876583e-02 -1.3617308e-02] Sparsity at: 0.4990646130728775 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1520 - accuracy: 0.9790 - val_loss: 0.2005 - val_accuracy: 0.9622 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2656803e-01 -2.7728448e-02 -1.3380367e-02] Sparsity at: 0.4990646130728775 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1482 - accuracy: 0.9798 - val_loss: 0.1959 - val_accuracy: 0.9660 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.3245885e-01 -2.8199118e-02 -9.2595043e-03] Sparsity at: 0.4990646130728775 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9800 - val_loss: 0.2181 - val_accuracy: 0.9579 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2289265e-01 -2.4929702e-02 -1.1284482e-02] Sparsity at: 0.4990646130728775 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1507 - accuracy: 0.9789 - val_loss: 0.2027 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2357540e-01 -2.3656294e-02 -1.3087492e-02] Sparsity at: 0.4990646130728775 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1494 - accuracy: 0.9789 - val_loss: 0.1919 - val_accuracy: 0.9680 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.2288350e-01 -2.3512403e-02 -2.1896288e-02] Sparsity at: 0.4990646130728775 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1500 - accuracy: 0.9789 - val_loss: 0.2390 - val_accuracy: 0.9515 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1849226e-01 -1.9743333e-02 -2.1349028e-02] Sparsity at: 0.4990646130728775 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1453 - accuracy: 0.9801 - val_loss: 0.2116 - val_accuracy: 0.9587 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1712099e-01 -1.5877519e-02 -2.2148145e-02] Sparsity at: 0.4990646130728775 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1447 - accuracy: 0.9799 - val_loss: 0.1853 - val_accuracy: 0.9654 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.18665911e-01 -1.48033025e-02 -2.77230330e-02] Sparsity at: 0.4990646130728775 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1431 - accuracy: 0.9805 - val_loss: 0.2113 - val_accuracy: 0.9597 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.16605684e-01 -1.70457568e-02 -2.46723704e-02] Sparsity at: 0.4990646130728775 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1435 - accuracy: 0.9805 - val_loss: 0.2439 - val_accuracy: 0.9475 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.23846635e-01 -1.59696676e-02 -2.85557564e-02] Sparsity at: 0.4990646130728775 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1471 - accuracy: 0.9787 - val_loss: 0.2330 - val_accuracy: 0.9529 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.12500206e-01 -1.26188770e-02 -2.31704768e-02] Sparsity at: 0.4990646130728775 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1442 - accuracy: 0.9793 - val_loss: 0.2169 - val_accuracy: 0.9567 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.12576224e-01 -2.25286894e-02 -1.80566255e-02] Sparsity at: 0.4990646130728775 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9807 - val_loss: 0.2207 - val_accuracy: 0.9550 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1326956e-01 -2.3931531e-02 -1.9872673e-02] Sparsity at: 0.4990646130728775 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1460 - accuracy: 0.9791 - val_loss: 0.2155 - val_accuracy: 0.9555 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1725549e-01 -2.5225190e-02 -1.9058302e-02] Sparsity at: 0.4990646130728775 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1424 - accuracy: 0.9798 - val_loss: 0.1890 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1045574e-01 -3.1097116e-02 -1.6292842e-02] Sparsity at: 0.4990646130728775 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9807 - val_loss: 0.1893 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1311470e-01 -2.9580811e-02 -1.7479774e-02] Sparsity at: 0.4990646130728775 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1468 - accuracy: 0.9782 - val_loss: 0.2052 - val_accuracy: 0.9623 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1114168e-01 -2.7089834e-02 -1.0597990e-02] Sparsity at: 0.4990646130728775 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9807 - val_loss: 0.2062 - val_accuracy: 0.9605 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1449118e-01 -2.3293629e-02 -2.0317370e-02] Sparsity at: 0.4990646130728775 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9805 - val_loss: 0.2024 - val_accuracy: 0.9616 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.08019374e-01 -3.20873149e-02 -1.38234627e-02] Sparsity at: 0.4990646130728775 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9812 - val_loss: 0.1964 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.1229980e-01 -2.6377989e-02 -1.7086959e-02] Sparsity at: 0.4990646130728775 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9803 - val_loss: 0.1986 - val_accuracy: 0.9626 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.0670014e-01 -2.8510800e-02 -1.3014200e-02] Sparsity at: 0.4990646130728775 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9795 - val_loss: 0.2044 - val_accuracy: 0.9618 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.06501214e-01 -3.14009376e-02 -1.33210421e-02] Sparsity at: 0.4990646130728775 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 1.97998486942231e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 4.1518576282012116e-10 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 0. 0.] [0. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.02615267192720605 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.249 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 0. 1. 0. 1. 1.] [0. 1. 0. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 0.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 0. 1. 1. 1. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 0. 0. 0.] [1. 1. 1. 1. 0. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 0. 1. 1.] [1. 1. 0. 0. 0. 0. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [0. 1. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 0.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 0. 1. 1. 0. 1. 1. 0. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 0. 1. 1. 0. 0.] [0. 1. 0. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 0. 0. 1. 1. 1.] [0. 1. 0. 0. 0. 0. 1. 0. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 0. 1.] [1. 1. 1. 0. 0. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 0. 1. 1. 1. 1. 0.] [1. 0. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 0. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 0.] [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 0. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [0. 1. 0. 0. 1. 0. 1. 0. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 0. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 0. 0. 1. 1.] [0. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 0. 1. 1. 0. 0. 1. 1.] [0. 0. 1. 1. 1. 0. 0. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 0. 0. 1.] [1. 0. 1. 1. 0. 1. 1. 0. 0. 1.] [1. 1. 0. 0. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 218s 12ms/step - loss: 0.1410 - accuracy: 0.9798 - val_loss: 0.2031 - val_accuracy: 0.9622 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.0216445e-01 -2.9031906e-02 -4.9266792e-03] Sparsity at: 0.4990646130728775 Epoch 52/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1392 - accuracy: 0.9809 - val_loss: 0.2123 - val_accuracy: 0.9557 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.0533931e-01 -3.1304531e-02 -7.8413403e-03] Sparsity at: 0.4990646130728775 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9807 - val_loss: 0.2155 - val_accuracy: 0.9571 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.7516291e-02 -3.0046932e-02 -6.2251054e-03] Sparsity at: 0.4990646130728775 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9803 - val_loss: 0.1963 - val_accuracy: 0.9640 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.05405375e-01 -3.40809226e-02 -2.76778452e-03] Sparsity at: 0.4990646130728775 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1422 - accuracy: 0.9790 - val_loss: 0.1931 - val_accuracy: 0.9649 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.0065380e-01 -3.1292714e-02 -2.4235400e-03] Sparsity at: 0.4990646130728775 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9794 - val_loss: 0.1908 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.0336387e-01 -3.5925906e-02 -6.2457481e-03] Sparsity at: 0.4990646130728775 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9802 - val_loss: 0.1921 - val_accuracy: 0.9662 [ 2.43010269e-35 4.83000297e-34 -1.52481018e-34 ... 1.02969006e-01 -2.85652969e-02 -7.58287683e-03] Sparsity at: 0.4990646130728775 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9807 - val_loss: 0.2053 - val_accuracy: 0.9614 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.0060604e-01 -3.2730751e-02 -1.3487593e-02] Sparsity at: 0.4990646130728775 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9794 - val_loss: 0.1815 - val_accuracy: 0.9673 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.7383089e-02 -3.3228386e-02 -9.5593706e-03] Sparsity at: 0.4990646130728775 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9806 - val_loss: 0.2069 - val_accuracy: 0.9609 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.2883646e-02 -3.8999550e-02 -6.6111474e-03] Sparsity at: 0.4990646130728775 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9801 - val_loss: 0.1760 - val_accuracy: 0.9686 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.4028339e-02 -4.3009229e-02 -1.1744168e-02] Sparsity at: 0.4990646130728775 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9804 - val_loss: 0.2017 - val_accuracy: 0.9602 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.3351141e-02 -4.0263925e-02 -5.3798617e-03] Sparsity at: 0.4990646130728775 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9802 - val_loss: 0.2206 - val_accuracy: 0.9534 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.0260461e-02 -3.7853736e-02 -8.3367471e-03] Sparsity at: 0.4990646130728775 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9795 - val_loss: 0.1982 - val_accuracy: 0.9616 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.1740035e-02 -3.6197659e-02 -3.6647578e-03] Sparsity at: 0.4990646130728775 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9801 - val_loss: 0.2008 - val_accuracy: 0.9614 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.6211719e-02 -3.9025985e-02 -1.2332376e-03] Sparsity at: 0.4990646130728775 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9801 - val_loss: 0.2225 - val_accuracy: 0.9554 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.8218063e-02 -4.3001432e-02 -5.4468317e-03] Sparsity at: 0.4990646130728775 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9808 - val_loss: 0.1913 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.3244113e-02 -3.1186070e-02 -1.0912329e-02] Sparsity at: 0.4990646130728775 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9804 - val_loss: 0.2012 - val_accuracy: 0.9618 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4076338e-02 -3.0445876e-02 -4.7609378e-03] Sparsity at: 0.4990646130728775 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9804 - val_loss: 0.2106 - val_accuracy: 0.9605 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.9413799e-02 -3.5725482e-02 -8.0840355e-03] Sparsity at: 0.4990646130728775 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9807 - val_loss: 0.2005 - val_accuracy: 0.9622 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.9458026e-02 -3.2345943e-02 -1.2858319e-02] Sparsity at: 0.4990646130728775 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9799 - val_loss: 0.2050 - val_accuracy: 0.9610 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.8047810e-02 -3.9924309e-02 -6.0624573e-03] Sparsity at: 0.4990646130728775 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9801 - val_loss: 0.2183 - val_accuracy: 0.9560 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.3228020e-02 -3.4409579e-02 -7.4734702e-03] Sparsity at: 0.4990646130728775 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9810 - val_loss: 0.2027 - val_accuracy: 0.9603 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.7458856e-02 -3.3984419e-02 -1.3455188e-02] Sparsity at: 0.4990646130728775 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9808 - val_loss: 0.2012 - val_accuracy: 0.9606 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.1118835e-02 -3.2223009e-02 -7.6602860e-03] Sparsity at: 0.4990646130728775 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9808 - val_loss: 0.2176 - val_accuracy: 0.9574 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3347380e-02 -3.4052953e-02 -9.2499657e-03] Sparsity at: 0.4990646130728775 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9798 - val_loss: 0.1903 - val_accuracy: 0.9647 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.6058885e-02 -2.9969865e-02 -9.1278758e-03] Sparsity at: 0.4990646130728775 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9816 - val_loss: 0.1865 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.8588886e-02 -3.5780240e-02 -1.1137055e-02] Sparsity at: 0.4990646130728775 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9811 - val_loss: 0.2389 - val_accuracy: 0.9505 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.5217237e-02 -3.6592830e-02 -8.8278512e-03] Sparsity at: 0.4990646130728775 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9800 - val_loss: 0.1928 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3480626e-02 -4.0244151e-02 -1.5103066e-02] Sparsity at: 0.4990646130728775 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9821 - val_loss: 0.2009 - val_accuracy: 0.9636 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3134897e-02 -3.5703976e-02 -1.8586395e-02] Sparsity at: 0.4990646130728775 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9802 - val_loss: 0.1919 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3321884e-02 -3.5714004e-02 -7.3275915e-03] Sparsity at: 0.4990646130728775 Epoch 82/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1339 - accuracy: 0.9806 - val_loss: 0.1896 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.6707257e-02 -3.4485251e-02 -1.1072615e-02] Sparsity at: 0.4990646130728775 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9819 - val_loss: 0.2021 - val_accuracy: 0.9611 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.2121067e-02 -3.8933024e-02 -1.0218536e-02] Sparsity at: 0.4990646130728775 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9803 - val_loss: 0.1919 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.6136269e-02 -3.7639730e-02 -1.0959951e-02] Sparsity at: 0.4990646130728775 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9808 - val_loss: 0.2040 - val_accuracy: 0.9618 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.3479831e-02 -3.4481358e-02 -1.3022921e-02] Sparsity at: 0.4990646130728775 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9814 - val_loss: 0.2039 - val_accuracy: 0.9581 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8424253e-02 -3.5150114e-02 -1.1521598e-02] Sparsity at: 0.4990646130728775 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9794 - val_loss: 0.1896 - val_accuracy: 0.9631 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.1256323e-02 -3.1995628e-02 -4.3646451e-03] Sparsity at: 0.4990646130728775 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9809 - val_loss: 0.2374 - val_accuracy: 0.9519 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5590417e-02 -3.5723213e-02 -5.5943551e-03] Sparsity at: 0.4990646130728775 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9818 - val_loss: 0.1984 - val_accuracy: 0.9622 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.0361597e-02 -3.9723706e-02 -1.1772426e-02] Sparsity at: 0.4990646130728775 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9806 - val_loss: 0.2067 - val_accuracy: 0.9611 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3187737e-02 -3.9827980e-02 -6.4904764e-03] Sparsity at: 0.4990646130728775 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9822 - val_loss: 0.2124 - val_accuracy: 0.9560 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.5095808e-02 -4.1745052e-02 -1.2713413e-02] Sparsity at: 0.4990646130728775 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9811 - val_loss: 0.1837 - val_accuracy: 0.9675 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5999938e-02 -3.9119866e-02 -1.1606320e-03] Sparsity at: 0.4990646130728775 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1322 - accuracy: 0.9810 - val_loss: 0.1899 - val_accuracy: 0.9639 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.1125237e-02 -4.1048095e-02 -5.3042336e-03] Sparsity at: 0.4990646130728775 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9801 - val_loss: 0.2146 - val_accuracy: 0.9573 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.7829845e-02 -3.7022736e-02 -5.4009221e-03] Sparsity at: 0.4990646130728775 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9813 - val_loss: 0.2067 - val_accuracy: 0.9589 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.6646827e-02 -3.3785097e-02 -4.1853623e-03] Sparsity at: 0.4990646130728775 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9814 - val_loss: 0.1961 - val_accuracy: 0.9636 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5005025e-02 -3.3235203e-02 -1.9651565e-03] Sparsity at: 0.4990646130728775 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9813 - val_loss: 0.2250 - val_accuracy: 0.9552 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.2283609e-02 -3.7922949e-02 8.1962842e-04] Sparsity at: 0.4990646130728775 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9808 - val_loss: 0.1992 - val_accuracy: 0.9621 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8205943e-02 -3.7396371e-02 5.0896418e-04] Sparsity at: 0.4990646130728775 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9816 - val_loss: 0.1965 - val_accuracy: 0.9636 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.4333303e-02 -3.4131184e-02 -1.8500192e-03] Sparsity at: 0.4990646130728775 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9819 - val_loss: 0.2192 - val_accuracy: 0.9581 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.7949308e-02 -3.9483614e-02 -6.2176748e-03] Sparsity at: 0.4990646130728775 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 3.249622759097033e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 2.7366301750201237e-05 Thresholhold -0.0021473937667906284 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 0. 0.] [0. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.035031816048792574 Thresholhold 0.06007113307714462 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 240s 12ms/step - loss: 0.1325 - accuracy: 0.9806 - val_loss: 0.2019 - val_accuracy: 0.9616 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.4153095e-02 -4.1713137e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 102/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1307 - accuracy: 0.9808 - val_loss: 0.1830 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.3752351e-02 -4.1555680e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9808 - val_loss: 0.1909 - val_accuracy: 0.9638 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6810340e-02 -4.4376079e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9814 - val_loss: 0.2261 - val_accuracy: 0.9527 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7703411e-02 -4.6923324e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9807 - val_loss: 0.1792 - val_accuracy: 0.9668 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.9385581e-02 -4.3198194e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 106/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1280 - accuracy: 0.9816 - val_loss: 0.2078 - val_accuracy: 0.9601 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.0054114e-02 -4.4841763e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9811 - val_loss: 0.2170 - val_accuracy: 0.9562 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2786003e-02 -3.7705638e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9814 - val_loss: 0.1874 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6734172e-02 -4.2044513e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9809 - val_loss: 0.1772 - val_accuracy: 0.9691 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1491092e-02 -4.1254226e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9806 - val_loss: 0.2357 - val_accuracy: 0.9521 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8186104e-02 -4.2629603e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9814 - val_loss: 0.1766 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7791760e-02 -4.5308668e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1295 - accuracy: 0.9814 - val_loss: 0.1850 - val_accuracy: 0.9660 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9970737e-02 -4.5667335e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9806 - val_loss: 0.1753 - val_accuracy: 0.9686 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5151587e-02 -3.8311619e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9801 - val_loss: 0.2068 - val_accuracy: 0.9596 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6308513e-02 -4.6791196e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9806 - val_loss: 0.1877 - val_accuracy: 0.9636 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5842874e-02 -4.6072509e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1284 - accuracy: 0.9811 - val_loss: 0.1886 - val_accuracy: 0.9648 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3804351e-02 -4.8296969e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9793 - val_loss: 0.1778 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1685767e-02 -4.7509484e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 118/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1284 - accuracy: 0.9809 - val_loss: 0.1648 - val_accuracy: 0.9711 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5286405e-02 -4.7037240e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9812 - val_loss: 0.1869 - val_accuracy: 0.9637 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6707738e-02 -4.7545038e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9805 - val_loss: 0.1877 - val_accuracy: 0.9640 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2296947e-02 -5.3387381e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9791 - val_loss: 0.1799 - val_accuracy: 0.9661 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5195166e-02 -4.9064200e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9808 - val_loss: 0.1920 - val_accuracy: 0.9608 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3770831e-02 -4.4713102e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9809 - val_loss: 0.1953 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0163233e-02 -4.8844308e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9809 - val_loss: 0.2206 - val_accuracy: 0.9575 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.4309135e-02 -5.8023777e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9811 - val_loss: 0.1988 - val_accuracy: 0.9621 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6108122e-02 -5.9665013e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9808 - val_loss: 0.2029 - val_accuracy: 0.9607 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8489239e-02 -5.7320721e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9810 - val_loss: 0.2085 - val_accuracy: 0.9590 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8289392e-02 -5.4693259e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9808 - val_loss: 0.2139 - val_accuracy: 0.9576 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7348287e-02 -5.6001794e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - accuracy: 0.9797 - val_loss: 0.1702 - val_accuracy: 0.9705 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3662440e-02 -5.6459140e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9812 - val_loss: 0.1689 - val_accuracy: 0.9688 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6184014e-02 -5.3563055e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 131/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1250 - accuracy: 0.9811 - val_loss: 0.1963 - val_accuracy: 0.9633 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1934141e-02 -5.2874990e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 132/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1280 - accuracy: 0.9807 - val_loss: 0.2099 - val_accuracy: 0.9590 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.9467440e-02 -4.8977815e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9808 - val_loss: 0.2017 - val_accuracy: 0.9582 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8042517e-02 -5.3853985e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9809 - val_loss: 0.2361 - val_accuracy: 0.9497 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5117767e-02 -5.3059746e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9804 - val_loss: 0.1960 - val_accuracy: 0.9624 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.7062003e-02 -5.6845471e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9814 - val_loss: 0.1792 - val_accuracy: 0.9662 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.4245311e-02 -5.6123320e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9812 - val_loss: 0.1852 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.0886932e-02 -4.5882858e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9811 - val_loss: 0.2043 - val_accuracy: 0.9603 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2373591e-02 -5.0678208e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9805 - val_loss: 0.1819 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.4700601e-02 -3.8172096e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9815 - val_loss: 0.1967 - val_accuracy: 0.9613 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8051852e-02 -4.1603245e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 141/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1246 - accuracy: 0.9817 - val_loss: 0.1983 - val_accuracy: 0.9618 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.4672647e-02 -4.8387717e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9807 - val_loss: 0.1753 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5198623e-02 -4.5971073e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9821 - val_loss: 0.1858 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6450365e-02 -4.1316841e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9816 - val_loss: 0.1760 - val_accuracy: 0.9668 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6992618e-02 -4.4316400e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9818 - val_loss: 0.1982 - val_accuracy: 0.9620 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5910071e-02 -4.5756068e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9806 - val_loss: 0.1825 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6028252e-02 -4.4554263e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1247 - accuracy: 0.9819 - val_loss: 0.1895 - val_accuracy: 0.9633 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.0561081e-02 -4.9273774e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9802 - val_loss: 0.1826 - val_accuracy: 0.9649 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1731858e-02 -5.3298570e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 149/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1258 - accuracy: 0.9815 - val_loss: 0.1787 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5369494e-02 -5.1626660e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9814 - val_loss: 0.2002 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7033254e-02 -5.4476336e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 4.3824882699611385e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 3.387793882952462e-05 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] ... [1. 1. 1. ... 0. 0. 0.] [0. 0. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.04549154068983019 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 247s 12ms/step - loss: 0.1242 - accuracy: 0.9816 - val_loss: 0.1851 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6644713e-02 -5.1309980e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 152/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9818 - val_loss: 0.2322 - val_accuracy: 0.9514 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5132573e-02 -5.0654743e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9817 - val_loss: 0.1715 - val_accuracy: 0.9705 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8993405e-02 -4.7729790e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 154/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1249 - accuracy: 0.9815 - val_loss: 0.2057 - val_accuracy: 0.9628 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.4992269e-02 -5.1991716e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 155/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1270 - accuracy: 0.9812 - val_loss: 0.1799 - val_accuracy: 0.9666 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2374858e-02 -5.3462394e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9826 - val_loss: 0.1762 - val_accuracy: 0.9681 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7823924e-02 -5.9274200e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9810 - val_loss: 0.1749 - val_accuracy: 0.9683 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.9508411e-02 -5.4240901e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9815 - val_loss: 0.1943 - val_accuracy: 0.9630 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.4938217e-02 -6.1101396e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9806 - val_loss: 0.1943 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2065346e-02 -5.3729214e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9808 - val_loss: 0.1827 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.4635597e-02 -5.3411961e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9815 - val_loss: 0.1989 - val_accuracy: 0.9600 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2592653e-02 -4.9779791e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9820 - val_loss: 0.1798 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1669057e-02 -5.0605670e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9810 - val_loss: 0.1872 - val_accuracy: 0.9641 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1541525e-02 -5.4424278e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9813 - val_loss: 0.1957 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1156584e-02 -5.9582531e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9811 - val_loss: 0.1934 - val_accuracy: 0.9643 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.3771819e-02 -5.4980770e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9805 - val_loss: 0.1846 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.6204643e-02 -5.4552551e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9822 - val_loss: 0.1790 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.7373065e-02 -5.9153091e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9812 - val_loss: 0.1924 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.1450939e-02 -5.3423770e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9820 - val_loss: 0.1854 - val_accuracy: 0.9666 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.0813281e-02 -6.0137209e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9808 - val_loss: 0.1902 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5508893e-02 -6.0987826e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.2056 - val_accuracy: 0.9597 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.6336853e-02 -6.4013273e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9811 - val_loss: 0.1828 - val_accuracy: 0.9654 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.0794178e-02 -5.8093440e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9804 - val_loss: 0.1878 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9662994e-02 -4.5517623e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1243 - accuracy: 0.9818 - val_loss: 0.1791 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9455480e-02 -5.2206144e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9822 - val_loss: 0.1745 - val_accuracy: 0.9673 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.6533690e-02 -4.6288349e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 176/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1235 - accuracy: 0.9817 - val_loss: 0.1947 - val_accuracy: 0.9630 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.7102900e-02 -5.0720576e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9820 - val_loss: 0.1854 - val_accuracy: 0.9624 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8827068e-02 -4.7553882e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 178/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1265 - accuracy: 0.9813 - val_loss: 0.1727 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9972766e-02 -4.9455214e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 179/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1242 - accuracy: 0.9813 - val_loss: 0.1790 - val_accuracy: 0.9662 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.1821755e-02 -4.8202977e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9809 - val_loss: 0.1918 - val_accuracy: 0.9637 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.7524930e-02 -4.2053681e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9804 - val_loss: 0.1849 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8666814e-02 -5.1102839e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9818 - val_loss: 0.1890 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3357804e-02 -5.2880403e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9806 - val_loss: 0.1744 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9391643e-02 -5.0100256e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 184/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1263 - accuracy: 0.9808 - val_loss: 0.1780 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3936356e-02 -4.7739953e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9819 - val_loss: 0.1912 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8962550e-02 -5.8271457e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9807 - val_loss: 0.1756 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8321353e-02 -5.3413030e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9809 - val_loss: 0.1800 - val_accuracy: 0.9647 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.6347321e-02 -5.7265740e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9811 - val_loss: 0.1740 - val_accuracy: 0.9673 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5714476e-02 -5.2130401e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9823 - val_loss: 0.1712 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9397954e-02 -4.8189718e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9800 - val_loss: 0.1888 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.5601600e-02 -4.3553635e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9804 - val_loss: 0.1843 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.0346867e-02 -4.4926006e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9812 - val_loss: 0.1620 - val_accuracy: 0.9704 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.5573636e-02 -4.3258540e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9815 - val_loss: 0.1859 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9673037e-02 -4.6526659e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9810 - val_loss: 0.1889 - val_accuracy: 0.9651 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.2042396e-02 -5.1611707e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9811 - val_loss: 0.1869 - val_accuracy: 0.9654 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4665097e-02 -4.5845572e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9819 - val_loss: 0.2066 - val_accuracy: 0.9602 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.9864582e-02 -4.8335023e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9819 - val_loss: 0.1890 - val_accuracy: 0.9637 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4804125e-02 -5.5267274e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9815 - val_loss: 0.1736 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.5101239e-02 -5.6904275e-02 -0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9819 - val_loss: 0.1845 - val_accuracy: 0.9625 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.7628350e-02 -5.1236529e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9815 - val_loss: 0.1910 - val_accuracy: 0.9650 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.5922398e-02 -4.4920709e-02 0.0000000e+00] Sparsity at: 0.5009804658151765 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 5.534202426343992e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.0019179677067498457 Thresholhold 1.3473632520799583e-07 Using suggest threshold. Applying new mask Percentage zeros 0.7637 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.06272514250561123 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 222s 12ms/step - loss: 0.1277 - accuracy: 0.9801 - val_loss: 0.1815 - val_accuracy: 0.9670 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3273061e-02 -4.2444535e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 202/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1223 - accuracy: 0.9816 - val_loss: 0.1971 - val_accuracy: 0.9623 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.6281210e-02 -4.9002454e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9806 - val_loss: 0.1761 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3101735e-02 -4.5378406e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9810 - val_loss: 0.1743 - val_accuracy: 0.9676 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.2586281e-02 -5.4883596e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9809 - val_loss: 0.1852 - val_accuracy: 0.9644 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.7525003e-02 -5.6930233e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9814 - val_loss: 0.1591 - val_accuracy: 0.9734 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4817775e-02 -5.3554110e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9814 - val_loss: 0.1815 - val_accuracy: 0.9685 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.5084975e-02 -5.8694873e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9815 - val_loss: 0.1797 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.7991312e-02 -5.2663766e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9821 - val_loss: 0.1699 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.9164637e-02 -5.5979781e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9809 - val_loss: 0.1838 - val_accuracy: 0.9666 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.1922708e-02 -4.8950512e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9809 - val_loss: 0.1829 - val_accuracy: 0.9647 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.2653513e-02 -4.6645068e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9819 - val_loss: 0.1749 - val_accuracy: 0.9687 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.1962144e-02 -4.6410728e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9808 - val_loss: 0.1766 - val_accuracy: 0.9683 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4473334e-02 -4.5723654e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9812 - val_loss: 0.1728 - val_accuracy: 0.9687 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.0912292e-02 -4.3305788e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9807 - val_loss: 0.2040 - val_accuracy: 0.9609 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4561668e-02 -3.8257103e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9815 - val_loss: 0.1732 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9160511e-02 -3.7258040e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 217/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1215 - accuracy: 0.9814 - val_loss: 0.1835 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.3753735e-02 -3.8720492e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9829 - val_loss: 0.1774 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.0109492e-02 -3.8732864e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9821 - val_loss: 0.1865 - val_accuracy: 0.9642 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4607773e-02 -3.8562607e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9802 - val_loss: 0.1952 - val_accuracy: 0.9608 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.2834944e-02 -4.0912442e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9815 - val_loss: 0.1789 - val_accuracy: 0.9662 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.1630543e-02 -4.3507129e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9824 - val_loss: 0.1982 - val_accuracy: 0.9629 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.9129167e-02 -3.9730258e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9817 - val_loss: 0.1887 - val_accuracy: 0.9648 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4036402e-02 -3.6696710e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9823 - val_loss: 0.1815 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.2216173e-02 -3.8851839e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9808 - val_loss: 0.1748 - val_accuracy: 0.9665 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4119216e-02 -3.5044391e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 226/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1204 - accuracy: 0.9824 - val_loss: 0.1800 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 9.0393059e-02 -3.6499742e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9827 - val_loss: 0.1816 - val_accuracy: 0.9632 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.7993331e-02 -3.4650363e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9815 - val_loss: 0.1966 - val_accuracy: 0.9601 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.4375538e-02 -3.3407096e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9819 - val_loss: 0.1781 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 8.1816010e-02 -3.6354475e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1241 - accuracy: 0.9808 - val_loss: 0.1786 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.8956388e-02 -3.5611629e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9825 - val_loss: 0.1807 - val_accuracy: 0.9677 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.1143523e-02 -4.1211564e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9806 - val_loss: 0.1814 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.9114134e-02 -4.2323619e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9809 - val_loss: 0.1684 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.6989064e-02 -4.7698054e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9830 - val_loss: 0.1887 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8205521e-02 -4.5620017e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9812 - val_loss: 0.1810 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2019987e-02 -3.8553219e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9812 - val_loss: 0.1782 - val_accuracy: 0.9677 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2069451e-02 -4.0509690e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9825 - val_loss: 0.1822 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7631550e-02 -4.0560920e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9813 - val_loss: 0.1769 - val_accuracy: 0.9677 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5033257e-02 -3.8046215e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9818 - val_loss: 0.1768 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.0019148e-02 -3.7541594e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9826 - val_loss: 0.1836 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.2931865e-02 -4.8101734e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9817 - val_loss: 0.1881 - val_accuracy: 0.9632 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3701555e-02 -5.0687797e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9817 - val_loss: 0.1806 - val_accuracy: 0.9641 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.8405636e-02 -4.4079047e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9822 - val_loss: 0.1787 - val_accuracy: 0.9677 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3570820e-02 -3.8991448e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 244/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1221 - accuracy: 0.9814 - val_loss: 0.2010 - val_accuracy: 0.9618 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0963273e-02 -4.7763743e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9824 - val_loss: 0.1705 - val_accuracy: 0.9687 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1253607e-02 -4.1996706e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9819 - val_loss: 0.1737 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6949668e-02 -3.7555564e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9816 - val_loss: 0.1926 - val_accuracy: 0.9650 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1061051e-02 -4.5742612e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9818 - val_loss: 0.2097 - val_accuracy: 0.9572 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1773714e-02 -5.4334398e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9815 - val_loss: 0.1748 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8386438e-02 -4.4209778e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9814 - val_loss: 0.1812 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.2588669e-02 -4.4599790e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.001930874255643561 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.007406638455651315 Thresholhold -3.9400154491886497e-05 Using suggest threshold. Applying new mask Percentage zeros 0.7637 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.07629896593094188 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 203s 12ms/step - loss: 0.1236 - accuracy: 0.9810 - val_loss: 0.1844 - val_accuracy: 0.9662 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9696715e-02 -5.2972123e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1210 - accuracy: 0.9812 - val_loss: 0.2036 - val_accuracy: 0.9591 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1732911e-02 -5.0197758e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9815 - val_loss: 0.1767 - val_accuracy: 0.9673 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7258092e-02 -4.4403467e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9819 - val_loss: 0.1643 - val_accuracy: 0.9714 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9170172e-02 -4.8469592e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9820 - val_loss: 0.1900 - val_accuracy: 0.9635 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0328159e-02 -4.5164734e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9809 - val_loss: 0.1794 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8638662e-02 -4.8170734e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9817 - val_loss: 0.1802 - val_accuracy: 0.9640 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6878380e-02 -3.7134618e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9816 - val_loss: 0.1834 - val_accuracy: 0.9652 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4294255e-02 -3.5178769e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9823 - val_loss: 0.1957 - val_accuracy: 0.9632 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1715998e-02 -4.1311860e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9814 - val_loss: 0.1662 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1148189e-02 -4.3737374e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9814 - val_loss: 0.1940 - val_accuracy: 0.9624 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7351753e-02 -3.8536653e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9828 - val_loss: 0.2005 - val_accuracy: 0.9606 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8126897e-02 -3.5436902e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9812 - val_loss: 0.2097 - val_accuracy: 0.9583 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7143640e-02 -3.8507584e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9817 - val_loss: 0.1689 - val_accuracy: 0.9692 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9633780e-02 -4.1500472e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 265/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1208 - accuracy: 0.9818 - val_loss: 0.1971 - val_accuracy: 0.9620 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1735682e-02 -4.7247067e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9810 - val_loss: 0.1805 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.4279199e-02 -4.9546435e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9823 - val_loss: 0.1796 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0687639e-02 -4.3423194e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9822 - val_loss: 0.1909 - val_accuracy: 0.9616 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6595564e-02 -5.1196244e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9819 - val_loss: 0.2077 - val_accuracy: 0.9597 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8121249e-02 -4.9615230e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9816 - val_loss: 0.1874 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2576989e-02 -4.9543031e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9818 - val_loss: 0.1799 - val_accuracy: 0.9643 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3376768e-02 -4.3356668e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9808 - val_loss: 0.1846 - val_accuracy: 0.9654 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2715134e-02 -3.1923495e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9823 - val_loss: 0.1795 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3808670e-02 -3.7514828e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 274/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1234 - accuracy: 0.9806 - val_loss: 0.1878 - val_accuracy: 0.9654 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1647700e-02 -3.5442106e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 275/500 235/235 [==============================] - 4s 17ms/step - loss: 0.1179 - accuracy: 0.9830 - val_loss: 0.1766 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1340185e-02 -3.2402080e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9814 - val_loss: 0.1895 - val_accuracy: 0.9632 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3857207e-02 -3.2855079e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9816 - val_loss: 0.1926 - val_accuracy: 0.9623 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7986353e-02 -3.0507095e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9811 - val_loss: 0.1795 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4378774e-02 -3.5495017e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9829 - val_loss: 0.1868 - val_accuracy: 0.9640 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5975627e-02 -3.9069470e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1664 - val_accuracy: 0.9700 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4068558e-02 -4.1458447e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9821 - val_loss: 0.1705 - val_accuracy: 0.9692 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9569031e-02 -4.7539957e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9815 - val_loss: 0.1872 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6400812e-02 -4.0377919e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9812 - val_loss: 0.1762 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3374320e-02 -4.6579041e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9819 - val_loss: 0.1874 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2447822e-02 -4.4216827e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 285/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1197 - accuracy: 0.9818 - val_loss: 0.1802 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4317858e-02 -4.6591286e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9818 - val_loss: 0.1740 - val_accuracy: 0.9676 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8510337e-02 -4.3629818e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9808 - val_loss: 0.1679 - val_accuracy: 0.9695 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3273242e-02 -5.1111978e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9817 - val_loss: 0.2006 - val_accuracy: 0.9603 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0299508e-02 -5.2788112e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9812 - val_loss: 0.1822 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5149784e-02 -4.7281776e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 290/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1230 - accuracy: 0.9809 - val_loss: 0.1847 - val_accuracy: 0.9628 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5108925e-02 -4.3764487e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 291/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1211 - accuracy: 0.9819 - val_loss: 0.1796 - val_accuracy: 0.9647 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3365869e-02 -4.7766022e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9816 - val_loss: 0.1756 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8159258e-02 -4.6221983e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9812 - val_loss: 0.1638 - val_accuracy: 0.9704 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6811202e-02 -5.0119221e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9815 - val_loss: 0.1839 - val_accuracy: 0.9660 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4606996e-02 -5.1408641e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9811 - val_loss: 0.1759 - val_accuracy: 0.9649 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3786229e-02 -5.6371730e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9816 - val_loss: 0.1882 - val_accuracy: 0.9614 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0890051e-02 -5.7071332e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9820 - val_loss: 0.1810 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4841630e-02 -5.2711930e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9797 - val_loss: 0.1826 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1687062e-02 -5.6083657e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9824 - val_loss: 0.1830 - val_accuracy: 0.9649 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5452410e-02 -5.7725169e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 300/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1214 - accuracy: 0.9813 - val_loss: 0.1790 - val_accuracy: 0.9662 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9231458e-02 -5.5183873e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.009478229064779131 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.01935673618825362 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7637 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.08640147756436711 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 173s 12ms/step - loss: 0.1206 - accuracy: 0.9815 - val_loss: 0.1774 - val_accuracy: 0.9693 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.4369943e-02 -5.3008098e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 302/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1253 - accuracy: 0.9798 - val_loss: 0.1903 - val_accuracy: 0.9624 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9863767e-02 -5.7023995e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9826 - val_loss: 0.1648 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6663009e-02 -5.0374798e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9823 - val_loss: 0.1821 - val_accuracy: 0.9657 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.8397577e-02 -5.3025898e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9811 - val_loss: 0.1841 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.2758457e-02 -6.1350178e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9817 - val_loss: 0.1975 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.2939901e-02 -4.9583387e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9824 - val_loss: 0.1973 - val_accuracy: 0.9621 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0117098e-02 -5.0896283e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9809 - val_loss: 0.1710 - val_accuracy: 0.9683 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6190865e-02 -5.3821113e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9812 - val_loss: 0.1855 - val_accuracy: 0.9647 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6129581e-02 -5.9413936e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9819 - val_loss: 0.1727 - val_accuracy: 0.9681 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.7898848e-02 -5.5294558e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9808 - val_loss: 0.1793 - val_accuracy: 0.9670 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1917970e-02 -5.2081071e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9815 - val_loss: 0.1725 - val_accuracy: 0.9676 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2356623e-02 -4.6857473e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9815 - val_loss: 0.1855 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7389520e-02 -5.8947045e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9814 - val_loss: 0.1710 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7414986e-02 -6.1920211e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9824 - val_loss: 0.1777 - val_accuracy: 0.9641 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3125571e-02 -5.9684813e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9811 - val_loss: 0.1736 - val_accuracy: 0.9677 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2644875e-02 -6.8043016e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9810 - val_loss: 0.1760 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7034552e-02 -6.3794054e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9813 - val_loss: 0.1651 - val_accuracy: 0.9708 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7562057e-02 -6.3511446e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9819 - val_loss: 0.2032 - val_accuracy: 0.9602 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7024650e-02 -6.5533839e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9812 - val_loss: 0.1730 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5569761e-02 -6.9111377e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 321/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.1792 - val_accuracy: 0.9661 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3033147e-02 -6.5309189e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9814 - val_loss: 0.1782 - val_accuracy: 0.9677 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6667637e-02 -6.7463025e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1809 - val_accuracy: 0.9636 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4592106e-02 -6.7473255e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9819 - val_loss: 0.1956 - val_accuracy: 0.9606 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7963576e-02 -7.1060248e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9813 - val_loss: 0.1725 - val_accuracy: 0.9674 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4509021e-02 -6.5719426e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9821 - val_loss: 0.1835 - val_accuracy: 0.9644 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0640138e-02 -6.2829845e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9810 - val_loss: 0.1862 - val_accuracy: 0.9648 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9789173e-02 -7.3465258e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 328/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1198 - accuracy: 0.9816 - val_loss: 0.2043 - val_accuracy: 0.9592 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1750036e-02 -7.2529994e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9801 - val_loss: 0.1798 - val_accuracy: 0.9660 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1198343e-02 -7.6601893e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9828 - val_loss: 0.1677 - val_accuracy: 0.9696 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5074930e-02 -8.1219181e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9821 - val_loss: 0.1844 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0943883e-02 -7.7246159e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9811 - val_loss: 0.1866 - val_accuracy: 0.9676 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0629010e-02 -8.0631077e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 333/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1218 - accuracy: 0.9809 - val_loss: 0.1641 - val_accuracy: 0.9698 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.3222788e-02 -8.7490901e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9821 - val_loss: 0.1858 - val_accuracy: 0.9639 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9663884e-02 -8.7933935e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9814 - val_loss: 0.1900 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9311308e-02 -8.1089906e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9812 - val_loss: 0.1709 - val_accuracy: 0.9699 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.5227948e-02 -7.9613738e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9824 - val_loss: 0.1811 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.0469864e-02 -7.8176275e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9816 - val_loss: 0.1700 - val_accuracy: 0.9697 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7454822e-02 -7.5582162e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9820 - val_loss: 0.1937 - val_accuracy: 0.9610 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.9663316e-02 -7.2121166e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9812 - val_loss: 0.1716 - val_accuracy: 0.9693 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.0657559e-02 -7.3629960e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9832 - val_loss: 0.1783 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 7.2989903e-02 -7.3160090e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9808 - val_loss: 0.1780 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.7845210e-02 -7.0872329e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9810 - val_loss: 0.1753 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9804909e-02 -7.1400575e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9815 - val_loss: 0.1827 - val_accuracy: 0.9660 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8919687e-02 -7.0807412e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9824 - val_loss: 0.1714 - val_accuracy: 0.9694 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0287867e-02 -6.7067578e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9817 - val_loss: 0.1681 - val_accuracy: 0.9689 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.1404780e-02 -7.3658973e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1172 - accuracy: 0.9821 - val_loss: 0.1881 - val_accuracy: 0.9641 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0033813e-02 -7.0336565e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9810 - val_loss: 0.1736 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.2218059e-02 -7.4841708e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9825 - val_loss: 0.1799 - val_accuracy: 0.9652 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0810562e-02 -7.6333873e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9810 - val_loss: 0.2111 - val_accuracy: 0.9587 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9665427e-02 -6.8433031e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.016688620131205534 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.029351832709330505 Thresholhold -9.85904989647679e-05 Using suggest threshold. Applying new mask Percentage zeros 0.7637 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.09073571856490581 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 165s 12ms/step - loss: 0.1207 - accuracy: 0.9818 - val_loss: 0.1627 - val_accuracy: 0.9695 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5560101e-02 -7.1519010e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 352/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1154 - accuracy: 0.9824 - val_loss: 0.1796 - val_accuracy: 0.9666 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0437996e-02 -7.6312751e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9818 - val_loss: 0.1918 - val_accuracy: 0.9643 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6559142e-02 -8.2453147e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9815 - val_loss: 0.1681 - val_accuracy: 0.9702 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8854088e-02 -8.4034249e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9813 - val_loss: 0.1834 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3068284e-02 -7.9083376e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.1882 - val_accuracy: 0.9621 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.8552554e-02 -8.1572741e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9816 - val_loss: 0.1846 - val_accuracy: 0.9631 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0652105e-02 -8.2072325e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9821 - val_loss: 0.1799 - val_accuracy: 0.9638 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.7726262e-02 -8.5656025e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9812 - val_loss: 0.1737 - val_accuracy: 0.9657 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1560655e-02 -8.3771244e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9826 - val_loss: 0.1841 - val_accuracy: 0.9673 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9595211e-02 -8.4752977e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9821 - val_loss: 0.1806 - val_accuracy: 0.9675 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2828282e-02 -7.7370092e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9805 - val_loss: 0.1903 - val_accuracy: 0.9652 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0753614e-02 -8.3689690e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 363/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1169 - accuracy: 0.9825 - val_loss: 0.1684 - val_accuracy: 0.9700 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5417288e-02 -7.7832952e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9813 - val_loss: 0.1775 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0313890e-02 -7.7491939e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9823 - val_loss: 0.1851 - val_accuracy: 0.9647 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0849091e-02 -7.6174654e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9818 - val_loss: 0.1753 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0584696e-02 -7.9181299e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9816 - val_loss: 0.1792 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9366359e-02 -7.7394627e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9818 - val_loss: 0.1738 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.8921525e-02 -7.2019443e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 369/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1174 - accuracy: 0.9823 - val_loss: 0.1802 - val_accuracy: 0.9644 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5693485e-02 -8.0594011e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 370/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1202 - accuracy: 0.9815 - val_loss: 0.1802 - val_accuracy: 0.9657 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2301489e-02 -7.8005575e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 371/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1201 - accuracy: 0.9818 - val_loss: 0.1915 - val_accuracy: 0.9623 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0460115e-02 -7.9404175e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9808 - val_loss: 0.1665 - val_accuracy: 0.9716 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1340688e-02 -7.0210971e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9814 - val_loss: 0.1727 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.8372758e-02 -7.2725527e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1172 - accuracy: 0.9823 - val_loss: 0.1859 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.7838617e-02 -6.6613324e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9818 - val_loss: 0.1609 - val_accuracy: 0.9712 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6686564e-02 -7.4004747e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9813 - val_loss: 0.1803 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1728692e-02 -7.4736468e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9815 - val_loss: 0.1707 - val_accuracy: 0.9681 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6745118e-02 -7.9371236e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9814 - val_loss: 0.1795 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0736431e-02 -7.2571523e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9819 - val_loss: 0.1759 - val_accuracy: 0.9658 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2565034e-02 -7.6930694e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 380/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1184 - accuracy: 0.9815 - val_loss: 0.1686 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3262077e-02 -7.8081153e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9822 - val_loss: 0.1748 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.1591378e-02 -7.6572403e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9802 - val_loss: 0.1807 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.5055182e-02 -7.7161364e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9816 - val_loss: 0.1801 - val_accuracy: 0.9657 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7602547e-02 -7.3279753e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9818 - val_loss: 0.1649 - val_accuracy: 0.9690 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0775517e-02 -6.9644541e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1168 - accuracy: 0.9824 - val_loss: 0.1779 - val_accuracy: 0.9649 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9477376e-02 -6.3372791e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9807 - val_loss: 0.1751 - val_accuracy: 0.9684 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0969962e-02 -6.7346632e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9823 - val_loss: 0.1748 - val_accuracy: 0.9663 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.6709619e-02 -5.9531592e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9817 - val_loss: 0.1756 - val_accuracy: 0.9662 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9211018e-02 -6.3189097e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9820 - val_loss: 0.1710 - val_accuracy: 0.9675 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0357223e-02 -6.7651913e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 390/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1185 - accuracy: 0.9818 - val_loss: 0.1787 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.5126770e-02 -7.3608994e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9804 - val_loss: 0.1715 - val_accuracy: 0.9700 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2706737e-02 -6.9384746e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1157 - accuracy: 0.9823 - val_loss: 0.1941 - val_accuracy: 0.9648 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.2120574e-02 -7.0982292e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9815 - val_loss: 0.2003 - val_accuracy: 0.9621 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.6779511e-02 -6.8187371e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9817 - val_loss: 0.1746 - val_accuracy: 0.9651 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 6.0019657e-02 -6.8839170e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9810 - val_loss: 0.1796 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.9837986e-02 -6.7362763e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9827 - val_loss: 0.1857 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4570634e-02 -6.3578650e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9808 - val_loss: 0.1936 - val_accuracy: 0.9615 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.3470284e-02 -5.9525628e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9809 - val_loss: 0.1796 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9755186e-02 -6.9769964e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1156 - accuracy: 0.9824 - val_loss: 0.1966 - val_accuracy: 0.9616 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0049376e-02 -7.4785471e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9806 - val_loss: 0.1808 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.7813335e-02 -6.9377683e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.020554253280615553 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] [0. 1. 0. ... 1. 0. 1.] ... [1. 0. 0. ... 1. 0. 0.] [1. 1. 1. ... 0. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.03466311425334778 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7637 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 0. ... 1. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [1. 0. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.09264636512113889 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.759 tf.Tensor( [[1. 0. 1. 0. 0. 1. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 0. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 1. 0.] [1. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 0. 1. 0. 1. 0. 1. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 1. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [1. 0. 1. 1. 0. 1. 0. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 0. 0. 0. 0.] [0. 1. 1. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 1. 1. 0. 0. 0.] [1. 1. 0. 0. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 1. 1. 0. 0.] [1. 1. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 1. 0. 0. 0.] [0. 0. 1. 1. 0. 0. 1. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 1. 1. 1. 1. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 1. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 1. 0. 0. 1. 0. 0. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 1. 0. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 1.] [1. 0. 1. 1. 1. 0. 0. 0. 0. 1.] [0. 0. 0. 1. 0. 1. 0. 0. 1. 0.] [1. 0. 1. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 1. 0. 1.] [0. 1. 0. 0. 0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [1. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 1. 1. 1. 0. 1. 0. 0.] [1. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 0. 0. 0. 1. 1. 1. 0.] [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 1. 0. 1. 1. 1.] [0. 0. 0. 0. 0. 1. 0. 0. 1. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 1. 1. 1. 0. 0. 1. 0. 1. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 1. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 1.] [0. 0. 0. 0. 0. 1. 1. 0. 0. 0.] [0. 1. 0. 1. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 1. 0.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 167s 12ms/step - loss: 0.1181 - accuracy: 0.9823 - val_loss: 0.1930 - val_accuracy: 0.9625 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.9732041e-02 -6.7579523e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 402/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1168 - accuracy: 0.9826 - val_loss: 0.1881 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.5102511e-02 -6.8674386e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9817 - val_loss: 0.1785 - val_accuracy: 0.9671 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.2050544e-02 -6.6917844e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9819 - val_loss: 0.1681 - val_accuracy: 0.9704 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.2026754e-02 -6.9111072e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9821 - val_loss: 0.1715 - val_accuracy: 0.9668 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.3105371e-02 -6.0759831e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9822 - val_loss: 0.2088 - val_accuracy: 0.9601 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.4524731e-02 -7.2963074e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9817 - val_loss: 0.1857 - val_accuracy: 0.9644 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 5.0975461e-02 -6.8098776e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9814 - val_loss: 0.1783 - val_accuracy: 0.9657 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.7487956e-02 -5.8443230e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 409/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1192 - accuracy: 0.9815 - val_loss: 0.1746 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.3682195e-02 -7.1174122e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1203 - accuracy: 0.9815 - val_loss: 0.1910 - val_accuracy: 0.9652 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 4.5653518e-02 -7.3513329e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9812 - val_loss: 0.1713 - val_accuracy: 0.9672 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.8367353e-02 -7.3611766e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1139 - accuracy: 0.9827 - val_loss: 0.1893 - val_accuracy: 0.9630 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.2611836e-02 -7.7581361e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9808 - val_loss: 0.2186 - val_accuracy: 0.9559 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.1390578e-02 -7.6063685e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9824 - val_loss: 0.1789 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.8403139e-02 -7.3021933e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 415/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1175 - accuracy: 0.9820 - val_loss: 0.1892 - val_accuracy: 0.9614 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.8050827e-02 -7.3061198e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9810 - val_loss: 0.1779 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.6313739e-02 -7.4836351e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9816 - val_loss: 0.1863 - val_accuracy: 0.9648 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.1568475e-02 -6.4207986e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9819 - val_loss: 0.1965 - val_accuracy: 0.9600 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.5554439e-02 -6.8918124e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9809 - val_loss: 0.1872 - val_accuracy: 0.9635 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.8984828e-02 -6.4084627e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9824 - val_loss: 0.2099 - val_accuracy: 0.9570 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.0490417e-02 -7.3417574e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9817 - val_loss: 0.1843 - val_accuracy: 0.9660 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.5181176e-02 -7.2477058e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9817 - val_loss: 0.1764 - val_accuracy: 0.9668 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3996552e-02 -7.2548978e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9818 - val_loss: 0.1717 - val_accuracy: 0.9668 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.4313317e-02 -6.1505701e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1158 - accuracy: 0.9826 - val_loss: 0.2082 - val_accuracy: 0.9568 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.1199366e-02 -5.9796121e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9809 - val_loss: 0.1844 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.4957646e-02 -5.3530514e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1169 - accuracy: 0.9827 - val_loss: 0.1744 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.1539539e-02 -5.5417050e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9820 - val_loss: 0.1748 - val_accuracy: 0.9665 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.2949910e-02 -6.3194901e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9820 - val_loss: 0.1850 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7509313e-02 -5.7286132e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9804 - val_loss: 0.1988 - val_accuracy: 0.9590 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7769269e-02 -6.5292791e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 430/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1190 - accuracy: 0.9813 - val_loss: 0.1776 - val_accuracy: 0.9668 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.2578398e-02 -6.2684059e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9825 - val_loss: 0.1700 - val_accuracy: 0.9682 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.9915622e-02 -6.4054996e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1148 - accuracy: 0.9829 - val_loss: 0.1953 - val_accuracy: 0.9636 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7618339e-02 -6.0588796e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1167 - accuracy: 0.9828 - val_loss: 0.1880 - val_accuracy: 0.9640 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.4674157e-02 -6.6436954e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9813 - val_loss: 0.1848 - val_accuracy: 0.9640 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.1270795e-02 -6.9627233e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9822 - val_loss: 0.1983 - val_accuracy: 0.9613 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.1441260e-02 -7.4110880e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9818 - val_loss: 0.1772 - val_accuracy: 0.9666 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3663381e-02 -7.0970483e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9814 - val_loss: 0.2047 - val_accuracy: 0.9598 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3029947e-02 -7.4412450e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1200 - accuracy: 0.9816 - val_loss: 0.1766 - val_accuracy: 0.9651 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.1444330e-02 -6.7232549e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9817 - val_loss: 0.1886 - val_accuracy: 0.9648 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6186859e-02 -6.8143860e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1177 - accuracy: 0.9823 - val_loss: 0.1949 - val_accuracy: 0.9606 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6214305e-02 -7.0450276e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9808 - val_loss: 0.1679 - val_accuracy: 0.9686 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.5156023e-02 -6.3374937e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9813 - val_loss: 0.1836 - val_accuracy: 0.9664 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7264230e-02 -6.3370183e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9825 - val_loss: 0.1743 - val_accuracy: 0.9675 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.2392951e-02 -7.3161647e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9805 - val_loss: 0.1742 - val_accuracy: 0.9666 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.0205844e-02 -7.5671375e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9820 - val_loss: 0.1949 - val_accuracy: 0.9628 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6850436e-02 -7.1346842e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9816 - val_loss: 0.1690 - val_accuracy: 0.9693 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.5708238e-02 -7.1120195e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1873 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.2309197e-02 -7.0252419e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 448/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1230 - accuracy: 0.9805 - val_loss: 0.1757 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.2024939e-02 -7.0962891e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9817 - val_loss: 0.1992 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.0294169e-02 -6.9299564e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9813 - val_loss: 0.1573 - val_accuracy: 0.9722 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6200058e-02 -6.6038102e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9824 - val_loss: 0.1810 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7808040e-02 -7.8157604e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9808 - val_loss: 0.1759 - val_accuracy: 0.9669 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.9381875e-02 -7.0881046e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9809 - val_loss: 0.1792 - val_accuracy: 0.9667 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6443304e-02 -6.7262635e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9812 - val_loss: 0.1880 - val_accuracy: 0.9646 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.8382227e-02 -6.0024992e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9810 - val_loss: 0.1856 - val_accuracy: 0.9661 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.9349731e-02 -6.7814931e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9814 - val_loss: 0.1703 - val_accuracy: 0.9686 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6967738e-02 -6.9297642e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9807 - val_loss: 0.1966 - val_accuracy: 0.9627 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.7382748e-02 -6.3568488e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9807 - val_loss: 0.1696 - val_accuracy: 0.9681 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.7405368e-02 -6.4932838e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9819 - val_loss: 0.1816 - val_accuracy: 0.9655 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.2853961e-02 -6.1906662e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9807 - val_loss: 0.1986 - val_accuracy: 0.9629 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3836767e-02 -7.1683630e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9813 - val_loss: 0.1737 - val_accuracy: 0.9670 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.9120512e-02 -6.8445072e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1167 - accuracy: 0.9821 - val_loss: 0.1709 - val_accuracy: 0.9698 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3053484e-02 -7.1202010e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9814 - val_loss: 0.1865 - val_accuracy: 0.9634 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.5805347e-02 -6.7788966e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9814 - val_loss: 0.1711 - val_accuracy: 0.9679 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3443112e-02 -7.1268700e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1180 - accuracy: 0.9812 - val_loss: 0.2403 - val_accuracy: 0.9518 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7160592e-02 -7.5857066e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9819 - val_loss: 0.1743 - val_accuracy: 0.9659 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.1911489e-02 -7.7323176e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9810 - val_loss: 0.1992 - val_accuracy: 0.9591 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.3913672e-02 -8.1514142e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9816 - val_loss: 0.1745 - val_accuracy: 0.9680 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.2820776e-02 -6.7961380e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9809 - val_loss: 0.2033 - val_accuracy: 0.9585 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.7386025e-02 -6.9979891e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9822 - val_loss: 0.1931 - val_accuracy: 0.9608 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.2180028e-02 -7.6714978e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1188 - accuracy: 0.9819 - val_loss: 0.2018 - val_accuracy: 0.9604 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.3887966e-02 -8.0209203e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9807 - val_loss: 0.1838 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 1.9135147e-02 -8.2527965e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 473/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1185 - accuracy: 0.9819 - val_loss: 0.1872 - val_accuracy: 0.9645 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.1278309e-02 -8.5850790e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1183 - accuracy: 0.9811 - val_loss: 0.1793 - val_accuracy: 0.9679 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7943283e-02 -8.6171664e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 475/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1165 - accuracy: 0.9826 - val_loss: 0.2095 - val_accuracy: 0.9557 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.5875464e-02 -7.7409700e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9815 - val_loss: 0.1803 - val_accuracy: 0.9644 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3022750e-02 -7.6050930e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9808 - val_loss: 0.1902 - val_accuracy: 0.9642 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6207011e-02 -8.4599718e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1166 - accuracy: 0.9822 - val_loss: 0.1860 - val_accuracy: 0.9630 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7955789e-02 -7.4234828e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9807 - val_loss: 0.1732 - val_accuracy: 0.9676 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3031896e-02 -7.4040778e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1163 - accuracy: 0.9818 - val_loss: 0.1710 - val_accuracy: 0.9653 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.9743990e-02 -7.5337909e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9820 - val_loss: 0.1773 - val_accuracy: 0.9681 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.0275602e-02 -7.7659838e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9816 - val_loss: 0.1776 - val_accuracy: 0.9678 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.2818519e-02 -7.1814224e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9817 - val_loss: 0.1830 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.8309593e-02 -7.9773158e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9799 - val_loss: 0.2179 - val_accuracy: 0.9577 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.0889010e-02 -7.8810006e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9809 - val_loss: 0.1840 - val_accuracy: 0.9642 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.3815405e-02 -7.7942654e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1152 - accuracy: 0.9827 - val_loss: 0.2057 - val_accuracy: 0.9595 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.8688289e-02 -7.4251547e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9813 - val_loss: 0.1830 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.5521819e-02 -7.9576910e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9816 - val_loss: 0.2209 - val_accuracy: 0.9552 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7744390e-02 -8.0918208e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9807 - val_loss: 0.1745 - val_accuracy: 0.9675 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.9248513e-02 -7.8275710e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9810 - val_loss: 0.1869 - val_accuracy: 0.9649 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.6693981e-02 -8.6062737e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9819 - val_loss: 0.1854 - val_accuracy: 0.9661 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.5839536e-02 -8.4945716e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9825 - val_loss: 0.1852 - val_accuracy: 0.9650 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.8811285e-02 -8.1879854e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9817 - val_loss: 0.2073 - val_accuracy: 0.9572 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7949737e-02 -8.4388733e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1124 - accuracy: 0.9835 - val_loss: 0.1781 - val_accuracy: 0.9654 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.3549622e-02 -8.9119829e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9816 - val_loss: 0.2077 - val_accuracy: 0.9611 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.7956065e-02 -7.5247832e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9816 - val_loss: 0.1688 - val_accuracy: 0.9696 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.6694072e-02 -8.0416314e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.1874 - val_accuracy: 0.9656 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.4941919e-02 -8.7644063e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 498/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1200 - accuracy: 0.9816 - val_loss: 0.1753 - val_accuracy: 0.9673 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 2.8980058e-02 -8.2324252e-02 -0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 499/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1169 - accuracy: 0.9820 - val_loss: 0.1923 - val_accuracy: 0.9617 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.0615116e-02 -8.1803173e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 500/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1206 - accuracy: 0.9810 - val_loss: 0.1852 - val_accuracy: 0.9642 [ 2.4301027e-35 4.8300030e-34 -1.5248102e-34 ... 3.4869742e-02 -8.9520596e-02 0.0000000e+00] Sparsity at: 0.5306987227648384 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03729514218866825 Thresholhold 0.07077749073505402 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [1. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06108436360955238 Thresholhold 0.11014298349618912 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 0. 1.] [1. 1. 1. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.11433566734194756 Thresholhold 0.021502435207366943 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 1/235 [..............................] - ETA: 4:19:51 - loss: 2.8171 - accuracy: 0.1133WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0097s vs `on_train_batch_begin` time: 10.9913s). Check your callbacks. 235/235 [==============================] - 69s 12ms/step - loss: 0.3073 - accuracy: 0.9094 - val_loss: 0.2947 - val_accuracy: 0.9498 [ 0.07077749 0. -0.06288844 ... 0.22002321 0.15723547 -0.08000243] Sparsity at: 0.49854244928625097 Epoch 2/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1084 - accuracy: 0.9692 - val_loss: 0.1111 - val_accuracy: 0.9672 [ 0.07077749 0. -0.06288844 ... 0.23815425 0.16523206 -0.09816986] Sparsity at: 0.49854244928625097 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0649 - accuracy: 0.9819 - val_loss: 0.0929 - val_accuracy: 0.9708 [ 0.07077749 0. -0.06288844 ... 0.25273883 0.17163971 -0.1132081 ] Sparsity at: 0.49854244928625097 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0397 - accuracy: 0.9896 - val_loss: 0.0885 - val_accuracy: 0.9739 [ 0.07077749 0. -0.06288844 ... 0.26333734 0.17927584 -0.13006447] Sparsity at: 0.49854244928625097 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.0831 - val_accuracy: 0.9762 [ 0.07077749 0. -0.06288844 ... 0.27253237 0.18492188 -0.14669353] Sparsity at: 0.49854244928625097 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0149 - accuracy: 0.9976 - val_loss: 0.0834 - val_accuracy: 0.9761 [ 0.07077749 0. -0.06288844 ... 0.28217748 0.19032665 -0.16107848] Sparsity at: 0.49854244928625097 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0088 - accuracy: 0.9987 - val_loss: 0.0834 - val_accuracy: 0.9776 [ 0.07077749 0. -0.06288844 ... 0.28581628 0.19839114 -0.16943207] Sparsity at: 0.49854244928625097 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0064 - accuracy: 0.9992 - val_loss: 0.0871 - val_accuracy: 0.9772 [ 0.07077749 0. -0.06288844 ... 0.29002464 0.20172007 -0.17702314] Sparsity at: 0.49854244928625097 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0054 - accuracy: 0.9994 - val_loss: 0.0853 - val_accuracy: 0.9784 [ 0.07077749 0. -0.06288844 ... 0.29775524 0.1996231 -0.18559293] Sparsity at: 0.49854244928625097 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9997 - val_loss: 0.0885 - val_accuracy: 0.9785 [ 0.07077749 0. -0.06288844 ... 0.2977176 0.20936799 -0.19329135] Sparsity at: 0.49854244928625097 Epoch 11/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0086 - accuracy: 0.9977 - val_loss: 0.1082 - val_accuracy: 0.9723 [ 0.07077749 0. -0.06288844 ... 0.3031407 0.21410725 -0.19940211] Sparsity at: 0.49854244928625097 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0205 - accuracy: 0.9931 - val_loss: 0.1126 - val_accuracy: 0.9716 [ 0.07077749 0. -0.06288844 ... 0.31711555 0.23267792 -0.2142889 ] Sparsity at: 0.49854244928625097 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0858 - val_accuracy: 0.9775 [ 0.07077749 0. -0.06288844 ... 0.32153633 0.23518695 -0.22723737] Sparsity at: 0.49854244928625097 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.0945 - val_accuracy: 0.9770 [ 0.07077749 0. -0.06288844 ... 0.3177723 0.23601383 -0.22487037] Sparsity at: 0.49854244928625097 Epoch 15/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0833 - val_accuracy: 0.9796 [ 0.07077749 0. -0.06288844 ... 0.31561244 0.24372697 -0.23453945] Sparsity at: 0.49854244928625097 Epoch 16/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.0820 - val_accuracy: 0.9796 [ 0.07077749 0. -0.06288844 ... 0.32024342 0.24781373 -0.24281093] Sparsity at: 0.49854244928625097 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0841 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.3199674 0.25186288 -0.24338253] Sparsity at: 0.49854244928625097 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4033e-04 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.32075918 0.25214246 -0.24510679] Sparsity at: 0.49854244928625097 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4197e-04 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.32288677 0.2528428 -0.24660347] Sparsity at: 0.49854244928625097 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4686e-04 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.32307908 0.25374904 -0.24795352] Sparsity at: 0.49854244928625097 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0902e-04 - accuracy: 1.0000 - val_loss: 0.0792 - val_accuracy: 0.9819 [ 0.07077749 0. -0.06288844 ... 0.3245389 0.25439408 -0.24895991] Sparsity at: 0.49854244928625097 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7221e-04 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.32472652 0.25547686 -0.250604 ] Sparsity at: 0.49854244928625097 Epoch 23/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4737e-04 - accuracy: 1.0000 - val_loss: 0.0799 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.32515404 0.25620818 -0.25184518] Sparsity at: 0.49854244928625097 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9969 - val_loss: 0.2388 - val_accuracy: 0.9495 [ 0.07077749 0. -0.06288844 ... 0.3087237 0.26554403 -0.2538967 ] Sparsity at: 0.49854244928625097 Epoch 25/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0441 - accuracy: 0.9855 - val_loss: 0.0900 - val_accuracy: 0.9765 [ 0.07077749 0. -0.06288844 ... 0.34163398 0.2591452 -0.24381962] Sparsity at: 0.49854244928625097 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0098 - accuracy: 0.9968 - val_loss: 0.0821 - val_accuracy: 0.9800 [ 0.07077749 0. -0.06288844 ... 0.34675792 0.27508634 -0.25312257] Sparsity at: 0.49854244928625097 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 0.0764 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.34221265 0.2719962 -0.25716573] Sparsity at: 0.49854244928625097 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0746 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.34327203 0.27452973 -0.25982776] Sparsity at: 0.49854244928625097 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 6.9485e-04 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.34680638 0.2764082 -0.2621237 ] Sparsity at: 0.49854244928625097 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1931e-04 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.34568217 0.27910382 -0.26209444] Sparsity at: 0.49854244928625097 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5432e-04 - accuracy: 1.0000 - val_loss: 0.0743 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.34602118 0.28032547 -0.26345834] Sparsity at: 0.49854244928625097 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8734e-04 - accuracy: 1.0000 - val_loss: 0.0753 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.3465471 0.28212556 -0.26467472] Sparsity at: 0.49854244928625097 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2779e-04 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.3471293 0.283362 -0.26637486] Sparsity at: 0.49854244928625097 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9578e-04 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.34866834 0.28551835 -0.2685639 ] Sparsity at: 0.49854244928625097 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7019e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.34896877 0.28679556 -0.26984993] Sparsity at: 0.49854244928625097 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6588e-04 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.34963942 0.28934515 -0.27146277] Sparsity at: 0.49854244928625097 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2536e-04 - accuracy: 1.0000 - val_loss: 0.0788 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.3504736 0.2914797 -0.27249596] Sparsity at: 0.49854244928625097 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0944e-04 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.35088098 0.2928936 -0.27379957] Sparsity at: 0.49854244928625097 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0990 - val_accuracy: 0.9795 [ 0.07077749 0. -0.06288844 ... 0.35099474 0.31612152 -0.275935 ] Sparsity at: 0.49854244928625097 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0429 - accuracy: 0.9863 - val_loss: 0.1130 - val_accuracy: 0.9731 [ 0.07077749 0. -0.06288844 ... 0.3463997 0.2818052 -0.26495454] Sparsity at: 0.49854244928625097 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0799 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.3494644 0.29963967 -0.27299172] Sparsity at: 0.49854244928625097 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0031 - accuracy: 0.9993 - val_loss: 0.0777 - val_accuracy: 0.9819 [ 0.07077749 0. -0.06288844 ... 0.35379577 0.29911366 -0.28268498] Sparsity at: 0.49854244928625097 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.0755 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.35728338 0.3025636 -0.2842815 ] Sparsity at: 0.49854244928625097 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1508e-04 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.35861632 0.30346766 -0.2841287 ] Sparsity at: 0.49854244928625097 Epoch 45/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8774e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.35919982 0.30602425 -0.28446555] Sparsity at: 0.49854244928625097 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1538e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.36036348 0.30720234 -0.28495345] Sparsity at: 0.49854244928625097 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4510e-04 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.3620657 0.3077274 -0.286137 ] Sparsity at: 0.49854244928625097 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1964e-04 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.36227435 0.3085687 -0.28639978] Sparsity at: 0.49854244928625097 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9234e-04 - accuracy: 1.0000 - val_loss: 0.0799 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.364083 0.30841202 -0.2875514 ] Sparsity at: 0.49854244928625097 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5624e-04 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.3660537 0.30860677 -0.28848886] Sparsity at: 0.49854244928625097 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.04219267063914156 Thresholhold 0.07077749073505402 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.05782828008594976 Thresholhold 0.17557062208652496 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.30201215466478715 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 210s 12ms/step - loss: 3.4459e-04 - accuracy: 1.0000 - val_loss: 0.0844 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.36873147 0.31173706 -0.2902761 ] Sparsity at: 0.6438542449286251 Epoch 52/500 235/235 [==============================] - 3s 12ms/step - loss: 2.6257e-04 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.36943266 0.3147518 -0.29183933] Sparsity at: 0.6438542449286251 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8323e-04 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.37166137 0.31441537 -0.2936888 ] Sparsity at: 0.6438542449286251 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2806e-04 - accuracy: 1.0000 - val_loss: 0.0840 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.37296653 0.31513628 -0.2944754 ] Sparsity at: 0.6438542449286251 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1173e-04 - accuracy: 1.0000 - val_loss: 0.0854 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.37383756 0.31587008 -0.29723474] Sparsity at: 0.6438542449286251 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 9.1000e-05 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.37671083 0.31654465 -0.29745802] Sparsity at: 0.6438542449286251 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0113 - accuracy: 0.9962 - val_loss: 0.1567 - val_accuracy: 0.9703 [ 0.07077749 0. -0.06288844 ... 0.37474856 0.33121437 -0.30526078] Sparsity at: 0.6438542449286251 Epoch 58/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0081 - accuracy: 0.9974 - val_loss: 0.0972 - val_accuracy: 0.9790 [ 0.07077749 0. -0.06288844 ... 0.37586954 0.34598792 -0.29147017] Sparsity at: 0.6438542449286251 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0901 - val_accuracy: 0.9802 [ 0.07077749 0. -0.06288844 ... 0.37612247 0.34635046 -0.2971241 ] Sparsity at: 0.6438542449286251 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5932e-04 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9819 [ 0.07077749 0. -0.06288844 ... 0.3768259 0.34961793 -0.301167 ] Sparsity at: 0.6438542449286251 Epoch 61/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5596e-04 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.37654376 0.35318434 -0.29934832] Sparsity at: 0.6438542449286251 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9375e-04 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.37706822 0.3546309 -0.29935822] Sparsity at: 0.6438542449286251 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1210e-04 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.37555593 0.35534126 -0.29644278] Sparsity at: 0.6438542449286251 Epoch 64/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5687e-04 - accuracy: 1.0000 - val_loss: 0.0846 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.37594578 0.35759154 -0.3014578 ] Sparsity at: 0.6438542449286251 Epoch 65/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1539e-04 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.37640432 0.35910308 -0.30048954] Sparsity at: 0.6438542449286251 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2288e-04 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.3771613 0.36070925 -0.30131897] Sparsity at: 0.6438542449286251 Epoch 67/500 235/235 [==============================] - 3s 13ms/step - loss: 8.8050e-05 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.37843913 0.361947 -0.30179012] Sparsity at: 0.6438542449286251 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8004e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.37876475 0.36362126 -0.30456167] Sparsity at: 0.6438542449286251 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5430e-05 - accuracy: 1.0000 - val_loss: 0.0869 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.37916338 0.36370558 -0.3050292 ] Sparsity at: 0.6438542449286251 Epoch 70/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8475e-05 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.3807698 0.36578977 -0.3066363 ] Sparsity at: 0.6438542449286251 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3140e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.3825623 0.366857 -0.30782598] Sparsity at: 0.6438542449286251 Epoch 72/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0535e-05 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.3832957 0.36860645 -0.3069975 ] Sparsity at: 0.6438542449286251 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0946e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.3843137 0.3691921 -0.306836 ] Sparsity at: 0.6438542449286251 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4763e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.38429472 0.37089497 -0.30856842] Sparsity at: 0.6438542449286251 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9862e-05 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.38466182 0.37157458 -0.3099805 ] Sparsity at: 0.6438542449286251 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8850e-05 - accuracy: 1.0000 - val_loss: 0.0912 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.38536263 0.37737125 -0.3085505 ] Sparsity at: 0.6438542449286251 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0035 - accuracy: 0.9990 - val_loss: 0.1811 - val_accuracy: 0.9706 [ 0.07077749 0. -0.06288844 ... 0.37676919 0.3963469 -0.32958907] Sparsity at: 0.6438542449286251 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0158 - accuracy: 0.9948 - val_loss: 0.1082 - val_accuracy: 0.9792 [ 0.07077749 0. -0.06288844 ... 0.3760096 0.34293857 -0.29264963] Sparsity at: 0.6438542449286251 Epoch 79/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.0939 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.37961665 0.3532812 -0.3148994 ] Sparsity at: 0.6438542449286251 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3680e-04 - accuracy: 0.9998 - val_loss: 0.0907 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.37760004 0.3614354 -0.31973007] Sparsity at: 0.6438542449286251 Epoch 81/500 235/235 [==============================] - 4s 15ms/step - loss: 3.8103e-04 - accuracy: 0.9999 - val_loss: 0.0897 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.37874147 0.35829222 -0.32277417] Sparsity at: 0.6438542449286251 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9900e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.37799653 0.3586325 -0.32455206] Sparsity at: 0.6438542449286251 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3243e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.37893268 0.36066502 -0.3251246 ] Sparsity at: 0.6438542449286251 Epoch 84/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3508e-04 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.3793772 0.36284816 -0.3276236 ] Sparsity at: 0.6438542449286251 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0113e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.37936458 0.36402363 -0.32850617] Sparsity at: 0.6438542449286251 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2303e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.37987393 0.36451986 -0.3287336 ] Sparsity at: 0.6438542449286251 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0885e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.38061684 0.36586884 -0.3296575 ] Sparsity at: 0.6438542449286251 Epoch 88/500 235/235 [==============================] - 3s 13ms/step - loss: 5.5893e-05 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.3814249 0.36651826 -0.33042783] Sparsity at: 0.6438542449286251 Epoch 89/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3539e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.38215655 0.3670291 -0.3317808 ] Sparsity at: 0.6438542449286251 Epoch 90/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9458e-05 - accuracy: 1.0000 - val_loss: 0.0911 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.3821436 0.3688642 -0.33242083] Sparsity at: 0.6438542449286251 Epoch 91/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1969e-05 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.3823596 0.37121758 -0.3345406 ] Sparsity at: 0.6438542449286251 Epoch 92/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3222e-05 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.38323575 0.37313348 -0.33129802] Sparsity at: 0.6438542449286251 Epoch 93/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8179e-05 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.38393655 0.37499595 -0.3320159 ] Sparsity at: 0.6438542449286251 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6538e-05 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.38550237 0.3766713 -0.33237547] Sparsity at: 0.6438542449286251 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0168e-05 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.38568348 0.37855065 -0.33460796] Sparsity at: 0.6438542449286251 Epoch 96/500 235/235 [==============================] - 4s 15ms/step - loss: 5.8844e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.39347598 0.36659467 -0.33213443] Sparsity at: 0.6438542449286251 Epoch 97/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 0.1802 - val_accuracy: 0.9688 [ 0.07077749 0. -0.06288844 ... 0.38148496 0.36149156 -0.29069728] Sparsity at: 0.6438542449286251 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0135 - accuracy: 0.9953 - val_loss: 0.1268 - val_accuracy: 0.9786 [ 0.07077749 0. -0.06288844 ... 0.36560258 0.38159183 -0.26631984] Sparsity at: 0.6438542449286251 Epoch 99/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 0.1021 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.37644556 0.3838251 -0.29156643] Sparsity at: 0.6438542449286251 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0051e-04 - accuracy: 0.9999 - val_loss: 0.0980 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.38106593 0.38486505 -0.29515108] Sparsity at: 0.6438542449286251 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.06722307808257177 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.10754248362987884 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.41623584383769696 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 213s 12ms/step - loss: 2.6341e-04 - accuracy: 0.9999 - val_loss: 0.0969 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.38368174 0.38197154 -0.29806238] Sparsity at: 0.6438542449286251 Epoch 102/500 235/235 [==============================] - 3s 12ms/step - loss: 1.7092e-04 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.3835571 0.38161218 -0.29628724] Sparsity at: 0.6438542449286251 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1827e-04 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.3831986 0.38107622 -0.29640132] Sparsity at: 0.6438542449286251 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 9.1059e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.38284224 0.38102615 -0.29576236] Sparsity at: 0.6438542449286251 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 8.4946e-05 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.38283852 0.38415575 -0.29781446] Sparsity at: 0.6438542449286251 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8894e-05 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.38297352 0.38233295 -0.29858777] Sparsity at: 0.6438542449286251 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9435e-04 - accuracy: 0.9999 - val_loss: 0.0964 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.3821489 0.39843214 -0.29721424] Sparsity at: 0.6438542449286251 Epoch 108/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3526e-04 - accuracy: 1.0000 - val_loss: 0.0957 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.38278812 0.39267573 -0.2981504 ] Sparsity at: 0.6438542449286251 Epoch 109/500 235/235 [==============================] - 3s 13ms/step - loss: 5.5648e-05 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.38365418 0.39176607 -0.29858717] Sparsity at: 0.6438542449286251 Epoch 110/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4113e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.38392642 0.39197212 -0.29836076] Sparsity at: 0.6438542449286251 Epoch 111/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6855e-04 - accuracy: 0.9999 - val_loss: 0.0997 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.39778674 0.39196154 -0.29647118] Sparsity at: 0.6438542449286251 Epoch 112/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0023e-05 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.3989083 0.3929235 -0.295818 ] Sparsity at: 0.6438542449286251 Epoch 113/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2178e-05 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.3986461 0.39465642 -0.3009746 ] Sparsity at: 0.6438542449286251 Epoch 114/500 235/235 [==============================] - 3s 15ms/step - loss: 5.3501e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.398333 0.39282727 -0.30284297] Sparsity at: 0.6438542449286251 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4629e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.39877027 0.39529106 -0.30762848] Sparsity at: 0.6438542449286251 Epoch 116/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9848e-05 - accuracy: 1.0000 - val_loss: 0.0953 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.39892462 0.39484367 -0.30829585] Sparsity at: 0.6438542449286251 Epoch 117/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7075e-04 - accuracy: 0.9999 - val_loss: 0.1215 - val_accuracy: 0.9804 [ 0.07077749 0. -0.06288844 ... 0.3930084 0.40372404 -0.28723773] Sparsity at: 0.6438542449286251 Epoch 118/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0101 - accuracy: 0.9967 - val_loss: 0.1163 - val_accuracy: 0.9787 [ 0.07077749 0. -0.06288844 ... 0.4539184 0.3638996 -0.2888744 ] Sparsity at: 0.6438542449286251 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1085 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.450893 0.36389714 -0.29295906] Sparsity at: 0.6438542449286251 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3369e-04 - accuracy: 0.9998 - val_loss: 0.1027 - val_accuracy: 0.9819 [ 0.07077749 0. -0.06288844 ... 0.44421515 0.36622354 -0.2998491 ] Sparsity at: 0.6438542449286251 Epoch 121/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5375e-04 - accuracy: 1.0000 - val_loss: 0.1011 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.44285455 0.365546 -0.29730177] Sparsity at: 0.6438542449286251 Epoch 122/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7793e-04 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.44159913 0.36866677 -0.2972661 ] Sparsity at: 0.6438542449286251 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0178e-04 - accuracy: 0.9999 - val_loss: 0.1001 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.44175816 0.3733503 -0.29826277] Sparsity at: 0.6438542449286251 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2276e-05 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.43919426 0.37602684 -0.30002812] Sparsity at: 0.6438542449286251 Epoch 125/500 235/235 [==============================] - 3s 13ms/step - loss: 5.7360e-05 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.44050744 0.377806 -0.30102202] Sparsity at: 0.6438542449286251 Epoch 126/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7491e-05 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.44049364 0.37859654 -0.30171373] Sparsity at: 0.6438542449286251 Epoch 127/500 235/235 [==============================] - 3s 15ms/step - loss: 4.6374e-05 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.4422707 0.37919235 -0.30189982] Sparsity at: 0.6438542449286251 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5321e-05 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.44147173 0.3815526 -0.30184686] Sparsity at: 0.6438542449286251 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4579e-05 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.4423621 0.3813606 -0.30428016] Sparsity at: 0.6438542449286251 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6901e-05 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.44255784 0.38156134 -0.30440918] Sparsity at: 0.6438542449286251 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4295e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.44270852 0.38216856 -0.30469728] Sparsity at: 0.6438542449286251 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2700e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.44351453 0.38172266 -0.30575353] Sparsity at: 0.6438542449286251 Epoch 133/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1284e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.44396913 0.3833651 -0.30564898] Sparsity at: 0.6438542449286251 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6412e-05 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.44347018 0.38505036 -0.3071321 ] Sparsity at: 0.6438542449286251 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1978e-05 - accuracy: 1.0000 - val_loss: 0.0999 - val_accuracy: 0.9844 [ 0.07077749 0. -0.06288844 ... 0.44429037 0.38456014 -0.30760732] Sparsity at: 0.6438542449286251 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4125e-04 - accuracy: 0.9998 - val_loss: 0.1464 - val_accuracy: 0.9758 [ 0.07077749 0. -0.06288844 ... 0.44508618 0.4040645 -0.32586768] Sparsity at: 0.6438542449286251 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.1246 - val_accuracy: 0.9795 [ 0.07077749 0. -0.06288844 ... 0.44897044 0.3873712 -0.2606492 ] Sparsity at: 0.6438542449286251 Epoch 138/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1100 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.44041184 0.3849763 -0.26310948] Sparsity at: 0.6438542449286251 Epoch 139/500 235/235 [==============================] - 3s 13ms/step - loss: 4.8657e-04 - accuracy: 0.9999 - val_loss: 0.1045 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.44067708 0.38881075 -0.2678539 ] Sparsity at: 0.6438542449286251 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0787e-04 - accuracy: 0.9999 - val_loss: 0.1061 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.43974125 0.3931303 -0.26572415] Sparsity at: 0.6438542449286251 Epoch 141/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0663e-04 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.4349618 0.3942723 -0.26509982] Sparsity at: 0.6438542449286251 Epoch 142/500 235/235 [==============================] - 3s 13ms/step - loss: 7.3384e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.43337438 0.3943986 -0.2653886 ] Sparsity at: 0.6438542449286251 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9849e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.4332794 0.39579764 -0.26680556] Sparsity at: 0.6438542449286251 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1499e-05 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.43298098 0.3946838 -0.26693466] Sparsity at: 0.6438542449286251 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4263e-05 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.4337667 0.39623588 -0.26789865] Sparsity at: 0.6438542449286251 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8312e-05 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.43387002 0.39568287 -0.2681821 ] Sparsity at: 0.6438542449286251 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9418e-05 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.43348068 0.39641082 -0.26838875] Sparsity at: 0.6438542449286251 Epoch 148/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6631e-05 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.4335408 0.39738753 -0.2704769 ] Sparsity at: 0.6438542449286251 Epoch 149/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9353e-05 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.4338756 0.40141135 -0.2711548 ] Sparsity at: 0.6438542449286251 Epoch 150/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2505e-05 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.43313697 0.4018448 -0.27027997] Sparsity at: 0.6438542449286251 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.12977274198082078 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.17684797241598638 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.5063692906655213 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 192s 12ms/step - loss: 1.9711e-05 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.4325562 0.4013169 -0.26893592] Sparsity at: 0.6438542449286251 Epoch 152/500 235/235 [==============================] - 3s 12ms/step - loss: 1.8089e-05 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.43177983 0.40173927 -0.26841098] Sparsity at: 0.6438542449286251 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5922e-05 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.43163145 0.4020789 -0.26981875] Sparsity at: 0.6438542449286251 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2846e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.43198344 0.40297526 -0.27056345] Sparsity at: 0.6438542449286251 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3548e-05 - accuracy: 1.0000 - val_loss: 0.1041 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.43220082 0.40511188 -0.27333638] Sparsity at: 0.6438542449286251 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5913e-05 - accuracy: 1.0000 - val_loss: 0.1038 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.42471343 0.41534978 -0.27358025] Sparsity at: 0.6438542449286251 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3397e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.42897046 0.41030964 -0.27387643] Sparsity at: 0.6438542449286251 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0133e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.42896685 0.4109889 -0.27502912] Sparsity at: 0.6438542449286251 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2323e-06 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.42870665 0.41161972 -0.2753282 ] Sparsity at: 0.6438542449286251 Epoch 160/500 235/235 [==============================] - 3s 13ms/step - loss: 9.0621e-06 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.42918026 0.41240942 -0.27772447] Sparsity at: 0.6438542449286251 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3317e-05 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.42979515 0.41485494 -0.29043663] Sparsity at: 0.6438542449286251 Epoch 162/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0100 - accuracy: 0.9969 - val_loss: 0.1270 - val_accuracy: 0.9804 [ 0.07077749 0. -0.06288844 ... 0.40569285 0.41812915 -0.28165516] Sparsity at: 0.6438542449286251 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1043 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.40988055 0.39671564 -0.27321884] Sparsity at: 0.6438542449286251 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9830e-04 - accuracy: 0.9998 - val_loss: 0.1013 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.41244492 0.40490937 -0.27132887] Sparsity at: 0.6438542449286251 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2412e-04 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.41109297 0.4161535 -0.27922297] Sparsity at: 0.6438542449286251 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6101e-04 - accuracy: 0.9999 - val_loss: 0.0995 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.41024056 0.4187012 -0.2800366 ] Sparsity at: 0.6438542449286251 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2589e-04 - accuracy: 1.0000 - val_loss: 0.1010 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.4107881 0.41571656 -0.2822485 ] Sparsity at: 0.6438542449286251 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7613e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.41041222 0.4169576 -0.28283638] Sparsity at: 0.6438542449286251 Epoch 169/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7240e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.41109186 0.41822127 -0.28227037] Sparsity at: 0.6438542449286251 Epoch 170/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0423e-05 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.411146 0.41837743 -0.28253308] Sparsity at: 0.6438542449286251 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0414e-04 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.40813342 0.41910687 -0.28150648] Sparsity at: 0.6438542449286251 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5365e-05 - accuracy: 1.0000 - val_loss: 0.1011 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.40879095 0.420546 -0.28306764] Sparsity at: 0.6438542449286251 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1052e-05 - accuracy: 1.0000 - val_loss: 0.1013 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.40963072 0.42073756 -0.28363436] Sparsity at: 0.6438542449286251 Epoch 174/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1077e-05 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.41035414 0.4203238 -0.28594634] Sparsity at: 0.6438542449286251 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2299e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.41620246 0.41979864 -0.28628877] Sparsity at: 0.6438542449286251 Epoch 176/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.1306 - val_accuracy: 0.9788 [ 0.07077749 0. -0.06288844 ... 0.4287261 0.43044674 -0.28454828] Sparsity at: 0.6438542449286251 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 0.1208 - val_accuracy: 0.9793 [ 0.07077749 0. -0.06288844 ... 0.41354305 0.44962642 -0.27653372] Sparsity at: 0.6438542449286251 Epoch 178/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1137 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.4202877 0.43841287 -0.30516267] Sparsity at: 0.6438542449286251 Epoch 179/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1182 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.41722384 0.44853187 -0.31679225] Sparsity at: 0.6438542449286251 Epoch 180/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1913e-04 - accuracy: 0.9999 - val_loss: 0.1046 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.41334632 0.44889298 -0.31659558] Sparsity at: 0.6438542449286251 Epoch 181/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2412e-04 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9843 [ 0.07077749 0. -0.06288844 ... 0.41383645 0.45054632 -0.31400543] Sparsity at: 0.6438542449286251 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2660e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.41461122 0.4525701 -0.31303558] Sparsity at: 0.6438542449286251 Epoch 183/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7119e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.4145497 0.4541315 -0.3129061 ] Sparsity at: 0.6438542449286251 Epoch 184/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9877e-05 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.41420752 0.4545273 -0.3133016 ] Sparsity at: 0.6438542449286251 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1548e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.4148391 0.45519605 -0.31269017] Sparsity at: 0.6438542449286251 Epoch 186/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0919e-05 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9843 [ 0.07077749 0. -0.06288844 ... 0.41411188 0.45500895 -0.31382322] Sparsity at: 0.6438542449286251 Epoch 187/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0594e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9842 [ 0.07077749 0. -0.06288844 ... 0.4154502 0.45551774 -0.3153795 ] Sparsity at: 0.6438542449286251 Epoch 188/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8583e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9842 [ 0.07077749 0. -0.06288844 ... 0.4158527 0.45643076 -0.31650525] Sparsity at: 0.6438542449286251 Epoch 189/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3416e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.41475824 0.45856366 -0.3165119 ] Sparsity at: 0.6438542449286251 Epoch 190/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6340e-05 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.414708 0.4591982 -0.31596485] Sparsity at: 0.6438542449286251 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3018e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.41418225 0.46035087 -0.31619483] Sparsity at: 0.6438542449286251 Epoch 192/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3030e-05 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9841 [ 0.07077749 0. -0.06288844 ... 0.41385266 0.46083876 -0.31634712] Sparsity at: 0.6438542449286251 Epoch 193/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1425 - val_accuracy: 0.9805 [ 0.07077749 0. -0.06288844 ... 0.4148868 0.50099397 -0.3484055 ] Sparsity at: 0.6438542449286251 Epoch 194/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0053 - accuracy: 0.9982 - val_loss: 0.1363 - val_accuracy: 0.9787 [ 0.07077749 0. -0.06288844 ... 0.3772815 0.48598397 -0.31020325] Sparsity at: 0.6438542449286251 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9990 - val_loss: 0.1171 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.3883453 0.4851859 -0.29018384] Sparsity at: 0.6438542449286251 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2102e-04 - accuracy: 0.9998 - val_loss: 0.1097 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.38811877 0.48641938 -0.27979183] Sparsity at: 0.6438542449286251 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9483e-04 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.3876079 0.4885252 -0.27400103] Sparsity at: 0.6438542449286251 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5846e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.38931623 0.49022585 -0.2766188 ] Sparsity at: 0.6438542449286251 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5873e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.39025378 0.49247432 -0.2790901 ] Sparsity at: 0.6438542449286251 Epoch 200/500 235/235 [==============================] - 3s 15ms/step - loss: 3.6910e-05 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.39047423 0.49197137 -0.2815152 ] Sparsity at: 0.6438542449286251 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.2130291442727632 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.2545684616607602 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.5939307740207767 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 200s 12ms/step - loss: 2.9212e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.3904692 0.49139494 -0.28238073] Sparsity at: 0.6438542449286251 Epoch 202/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4446e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.39039412 0.49206096 -0.28326333] Sparsity at: 0.6438542449286251 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5199e-05 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.39043218 0.49232802 -0.28503856] Sparsity at: 0.6438542449286251 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7138e-05 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.39009878 0.49146026 -0.2855671 ] Sparsity at: 0.6438542449286251 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1139e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9841 [ 0.07077749 0. -0.06288844 ... 0.3908654 0.49123633 -0.2874075 ] Sparsity at: 0.6438542449286251 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3712e-05 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.39103943 0.49085248 -0.28703398] Sparsity at: 0.6438542449286251 Epoch 207/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8747e-05 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.39125374 0.49175456 -0.28853452] Sparsity at: 0.6438542449286251 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0475e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.39157817 0.49462217 -0.2875069 ] Sparsity at: 0.6438542449286251 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4511e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.3915045 0.4949844 -0.28831527] Sparsity at: 0.6438542449286251 Epoch 210/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2441e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.39154157 0.49554268 -0.2888433 ] Sparsity at: 0.6438542449286251 Epoch 211/500 235/235 [==============================] - 3s 13ms/step - loss: 7.9455e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.40074992 0.49533212 -0.29753006] Sparsity at: 0.6438542449286251 Epoch 212/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.1548 - val_accuracy: 0.9769 [ 0.07077749 0. -0.06288844 ... 0.35701442 0.49861482 -0.24383967] Sparsity at: 0.6438542449286251 Epoch 213/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.1330 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.37795773 0.48154846 -0.26194903] Sparsity at: 0.6438542449286251 Epoch 214/500 235/235 [==============================] - 3s 13ms/step - loss: 8.7761e-04 - accuracy: 0.9998 - val_loss: 0.1193 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.3810571 0.4854263 -0.25863057] Sparsity at: 0.6438542449286251 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5911e-04 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.38132456 0.4853115 -0.2627085 ] Sparsity at: 0.6438542449286251 Epoch 216/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0843e-04 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.37695992 0.4856609 -0.2559537 ] Sparsity at: 0.6438542449286251 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4507e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.37550426 0.48566157 -0.25451946] Sparsity at: 0.6438542449286251 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7194e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.37568796 0.48612362 -0.2563477 ] Sparsity at: 0.6438542449286251 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7080e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.37734884 0.48647696 -0.2569758 ] Sparsity at: 0.6438542449286251 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3274e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.376986 0.48719323 -0.2579941 ] Sparsity at: 0.6438542449286251 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3128e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.37665427 0.48761666 -0.2581275 ] Sparsity at: 0.6438542449286251 Epoch 222/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8618e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.37653592 0.48648232 -0.25801423] Sparsity at: 0.6438542449286251 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4661e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.37647626 0.486854 -0.25826105] Sparsity at: 0.6438542449286251 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2345e-05 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.3750439 0.48698926 -0.25628045] Sparsity at: 0.6438542449286251 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7811e-04 - accuracy: 0.9999 - val_loss: 0.1197 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.3748217 0.4890028 -0.25552976] Sparsity at: 0.6438542449286251 Epoch 226/500 235/235 [==============================] - 4s 16ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 0.1434 - val_accuracy: 0.9778 [ 0.07077749 0. -0.06288844 ... 0.38103834 0.49142006 -0.26042166] Sparsity at: 0.6438542449286251 Epoch 227/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0021 - accuracy: 0.9992 - val_loss: 0.1317 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.3849364 0.47675553 -0.2551951 ] Sparsity at: 0.6438542449286251 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1214 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.38855556 0.47335616 -0.26963204] Sparsity at: 0.6438542449286251 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1327e-04 - accuracy: 0.9999 - val_loss: 0.1173 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.38839477 0.46733952 -0.27733812] Sparsity at: 0.6438542449286251 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4073e-04 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.39310628 0.45703343 -0.27612567] Sparsity at: 0.6438542449286251 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9560e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.38988966 0.4602137 -0.2755365 ] Sparsity at: 0.6438542449286251 Epoch 232/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3799e-05 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.39027438 0.45813772 -0.2751183 ] Sparsity at: 0.6438542449286251 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4211e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.39016247 0.45815465 -0.27438223] Sparsity at: 0.6438542449286251 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0846e-05 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.38945583 0.45795318 -0.27355203] Sparsity at: 0.6438542449286251 Epoch 235/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6701e-05 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.38970384 0.4577979 -0.27353522] Sparsity at: 0.6438542449286251 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4421e-05 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.38960564 0.4572641 -0.2729929 ] Sparsity at: 0.6438542449286251 Epoch 237/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7832e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.38979694 0.4543809 -0.27341324] Sparsity at: 0.6438542449286251 Epoch 238/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1619e-05 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.39020014 0.45506215 -0.2736133 ] Sparsity at: 0.6438542449286251 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0655e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.3893898 0.4552619 -0.27426147] Sparsity at: 0.6438542449286251 Epoch 240/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1301e-05 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9843 [ 0.07077749 0. -0.06288844 ... 0.39035955 0.45510164 -0.27393496] Sparsity at: 0.6438542449286251 Epoch 241/500 235/235 [==============================] - 3s 13ms/step - loss: 9.0875e-06 - accuracy: 1.0000 - val_loss: 0.1134 - val_accuracy: 0.9842 [ 0.07077749 0. -0.06288844 ... 0.3909178 0.4551764 -0.2743764 ] Sparsity at: 0.6438542449286251 Epoch 242/500 235/235 [==============================] - 3s 13ms/step - loss: 7.8639e-06 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.39118132 0.45605952 -0.27472818] Sparsity at: 0.6438542449286251 Epoch 243/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0926e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.39198694 0.45694414 -0.2849757 ] Sparsity at: 0.6438542449286251 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.1368 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.4253296 0.3963998 -0.26524222] Sparsity at: 0.6438542449286251 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1238 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.42534575 0.39230657 -0.26009858] Sparsity at: 0.6438542449286251 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4926e-04 - accuracy: 0.9998 - val_loss: 0.1205 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.4332269 0.3912942 -0.2585145 ] Sparsity at: 0.6438542449286251 Epoch 247/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4551e-04 - accuracy: 0.9998 - val_loss: 0.1217 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.43267626 0.39063585 -0.26539943] Sparsity at: 0.6438542449286251 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4157e-04 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.43384716 0.3947044 -0.2656532 ] Sparsity at: 0.6438542449286251 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 9.9042e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.4246617 0.3977846 -0.26689655] Sparsity at: 0.6438542449286251 Epoch 250/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8103e-05 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.42411664 0.39739355 -0.26750943] Sparsity at: 0.6438542449286251 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.30963061720410323 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.34061425999016137 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.6983748524054292 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 192s 12ms/step - loss: 4.9065e-05 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.42492878 0.3997808 -0.27013266] Sparsity at: 0.6438542449286251 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 2.8377e-05 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.4247327 0.40152106 -0.2691469 ] Sparsity at: 0.6438542449286251 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6001e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.42461964 0.40116614 -0.2688547 ] Sparsity at: 0.6438542449286251 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5745e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.42473522 0.40187883 -0.26851445] Sparsity at: 0.6438542449286251 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4912e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.42530847 0.40260896 -0.26883975] Sparsity at: 0.6438542449286251 Epoch 256/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0771e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.42974523 0.40367523 -0.26985252] Sparsity at: 0.6438542449286251 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4663e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.43160442 0.4052325 -0.27024984] Sparsity at: 0.6438542449286251 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1255e-05 - accuracy: 1.0000 - val_loss: 0.1128 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.43106678 0.4060231 -0.27058065] Sparsity at: 0.6438542449286251 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6011e-04 - accuracy: 0.9999 - val_loss: 0.1307 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.431349 0.40806052 -0.2707382 ] Sparsity at: 0.6438542449286251 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3792e-04 - accuracy: 0.9997 - val_loss: 0.1346 - val_accuracy: 0.9800 [ 0.07077749 0. -0.06288844 ... 0.42398202 0.36041996 -0.2828116 ] Sparsity at: 0.6438542449286251 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1278 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.43408975 0.37217593 -0.29092374] Sparsity at: 0.6438542449286251 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1302 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.42805 0.39060345 -0.26494518] Sparsity at: 0.6438542449286251 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3221e-04 - accuracy: 0.9999 - val_loss: 0.1175 - val_accuracy: 0.9844 [ 0.07077749 0. -0.06288844 ... 0.42064774 0.40313432 -0.2770736 ] Sparsity at: 0.6438542449286251 Epoch 264/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0638e-04 - accuracy: 0.9999 - val_loss: 0.1230 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.44053605 0.39917213 -0.28096694] Sparsity at: 0.6438542449286251 Epoch 265/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2431e-04 - accuracy: 0.9999 - val_loss: 0.1219 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.4387658 0.387657 -0.27950564] Sparsity at: 0.6438542449286251 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3016e-05 - accuracy: 1.0000 - val_loss: 0.1204 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.4402159 0.39112613 -0.28342667] Sparsity at: 0.6438542449286251 Epoch 267/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2334e-04 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9842 [ 0.07077749 0. -0.06288844 ... 0.44032708 0.39446315 -0.27689728] Sparsity at: 0.6438542449286251 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2466e-04 - accuracy: 0.9999 - val_loss: 0.1216 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.43648854 0.40442055 -0.25954935] Sparsity at: 0.6438542449286251 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5591e-04 - accuracy: 0.9999 - val_loss: 0.1209 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.4427551 0.39917445 -0.26554817] Sparsity at: 0.6438542449286251 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8256e-04 - accuracy: 0.9999 - val_loss: 0.1228 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.44433206 0.3933307 -0.26152053] Sparsity at: 0.6438542449286251 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7781e-05 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.4432832 0.3935386 -0.26007167] Sparsity at: 0.6438542449286251 Epoch 272/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0423e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.44272903 0.39539567 -0.2610859 ] Sparsity at: 0.6438542449286251 Epoch 273/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3203e-05 - accuracy: 1.0000 - val_loss: 0.1177 - val_accuracy: 0.9849 [ 0.07077749 0. -0.06288844 ... 0.44260526 0.3973002 -0.2608887 ] Sparsity at: 0.6438542449286251 Epoch 274/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2605e-05 - accuracy: 1.0000 - val_loss: 0.1170 - val_accuracy: 0.9845 [ 0.07077749 0. -0.06288844 ... 0.4423295 0.40323722 -0.26211402] Sparsity at: 0.6438542449286251 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0253e-05 - accuracy: 1.0000 - val_loss: 0.1189 - val_accuracy: 0.9837 [ 0.07077749 0. -0.06288844 ... 0.44157282 0.4069013 -0.26273268] Sparsity at: 0.6438542449286251 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2090e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9842 [ 0.07077749 0. -0.06288844 ... 0.44161204 0.40883613 -0.26385757] Sparsity at: 0.6438542449286251 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8449e-06 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9843 [ 0.07077749 0. -0.06288844 ... 0.44178417 0.41054335 -0.2646439 ] Sparsity at: 0.6438542449286251 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6454e-06 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9846 [ 0.07077749 0. -0.06288844 ... 0.44157726 0.4121817 -0.26400548] Sparsity at: 0.6438542449286251 Epoch 279/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0267e-05 - accuracy: 1.0000 - val_loss: 0.1197 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.44189206 0.4180744 -0.26107502] Sparsity at: 0.6438542449286251 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5266e-06 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.44111434 0.4190951 -0.26178277] Sparsity at: 0.6438542449286251 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6531e-06 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9840 [ 0.07077749 0. -0.06288844 ... 0.44118953 0.41788945 -0.26142076] Sparsity at: 0.6438542449286251 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4231e-06 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9843 [ 0.07077749 0. -0.06288844 ... 0.44148332 0.4184369 -0.26185936] Sparsity at: 0.6438542449286251 Epoch 283/500 235/235 [==============================] - 3s 13ms/step - loss: 6.6650e-06 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9841 [ 0.07077749 0. -0.06288844 ... 0.4423627 0.4198468 -0.2611119 ] Sparsity at: 0.6438542449286251 Epoch 284/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6349e-06 - accuracy: 1.0000 - val_loss: 0.1191 - val_accuracy: 0.9839 [ 0.07077749 0. -0.06288844 ... 0.44149214 0.4198811 -0.2614711 ] Sparsity at: 0.6438542449286251 Epoch 285/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4018e-06 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.44134834 0.42130676 -0.26364344] Sparsity at: 0.6438542449286251 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1644e-06 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.44182163 0.42254448 -0.26432937] Sparsity at: 0.6438542449286251 Epoch 287/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.1627 - val_accuracy: 0.9787 [ 0.07077749 0. -0.06288844 ... 0.45585608 0.42639595 -0.2728703 ] Sparsity at: 0.6438542449286251 Epoch 288/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.1417 - val_accuracy: 0.9801 [ 0.07077749 0. -0.06288844 ... 0.44064945 0.4511273 -0.2830773 ] Sparsity at: 0.6438542449286251 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9995 - val_loss: 0.1288 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.42771792 0.47134545 -0.2509048 ] Sparsity at: 0.6438542449286251 Epoch 290/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1385e-04 - accuracy: 0.9999 - val_loss: 0.1254 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.43214378 0.47052953 -0.2535617 ] Sparsity at: 0.6438542449286251 Epoch 291/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1395e-04 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.43160203 0.47604507 -0.25830805] Sparsity at: 0.6438542449286251 Epoch 292/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3283e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.4304092 0.47559768 -0.25930053] Sparsity at: 0.6438542449286251 Epoch 293/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4219e-05 - accuracy: 1.0000 - val_loss: 0.1239 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.43015033 0.475722 -0.25950754] Sparsity at: 0.6438542449286251 Epoch 294/500 235/235 [==============================] - 3s 13ms/step - loss: 3.2202e-05 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.42888868 0.47629115 -0.26025802] Sparsity at: 0.6438542449286251 Epoch 295/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3475e-05 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.42878622 0.47749227 -0.26406673] Sparsity at: 0.6438542449286251 Epoch 296/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7979e-05 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.4304275 0.47730762 -0.2643415 ] Sparsity at: 0.6438542449286251 Epoch 297/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4277e-05 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.43063882 0.4772587 -0.26320407] Sparsity at: 0.6438542449286251 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2698e-05 - accuracy: 1.0000 - val_loss: 0.1209 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.43029717 0.47591558 -0.26359656] Sparsity at: 0.6438542449286251 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2102e-05 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.43078616 0.4761026 -0.26447618] Sparsity at: 0.6438542449286251 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1792e-04 - accuracy: 0.9999 - val_loss: 0.1334 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.43216398 0.4799171 -0.2682751 ] Sparsity at: 0.6438542449286251 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.4106218309445744 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.42667934705536226 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.788631870266066 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 205s 12ms/step - loss: 7.3774e-04 - accuracy: 0.9998 - val_loss: 0.1181 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.4281677 0.48296604 -0.23433328] Sparsity at: 0.6438542449286251 Epoch 302/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1278 - val_accuracy: 0.9812 [ 0.07077749 0. -0.06288844 ... 0.43922102 0.4835326 -0.25486204] Sparsity at: 0.6438542449286251 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1371 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.4320869 0.477214 -0.24938615] Sparsity at: 0.6438542449286251 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0273e-04 - accuracy: 0.9998 - val_loss: 0.1253 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.4374423 0.46683407 -0.23109311] Sparsity at: 0.6438542449286251 Epoch 305/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7947e-04 - accuracy: 0.9999 - val_loss: 0.1196 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.43255687 0.4604224 -0.22897227] Sparsity at: 0.6438542449286251 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3083e-04 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.4339354 0.4588677 -0.22998253] Sparsity at: 0.6438542449286251 Epoch 307/500 235/235 [==============================] - 3s 13ms/step - loss: 2.4891e-05 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.4344025 0.46041006 -0.23076819] Sparsity at: 0.6438542449286251 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2725e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.43768525 0.4606189 -0.23310141] Sparsity at: 0.6438542449286251 Epoch 309/500 235/235 [==============================] - 4s 15ms/step - loss: 2.0548e-05 - accuracy: 1.0000 - val_loss: 0.1255 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.43832225 0.46108812 -0.233806 ] Sparsity at: 0.6438542449286251 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3083e-05 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.43887505 0.46095908 -0.23401919] Sparsity at: 0.6438542449286251 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0114e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.4389019 0.46178088 -0.2342692 ] Sparsity at: 0.6438542449286251 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0036e-05 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.43866858 0.4610992 -0.23464577] Sparsity at: 0.6438542449286251 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 9.1978e-06 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.43865892 0.4603495 -0.23470102] Sparsity at: 0.6438542449286251 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2866e-06 - accuracy: 1.0000 - val_loss: 0.1229 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.43725714 0.46107224 -0.23450291] Sparsity at: 0.6438542449286251 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0057e-06 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.43781543 0.46155992 -0.23507413] Sparsity at: 0.6438542449286251 Epoch 316/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0730e-06 - accuracy: 1.0000 - val_loss: 0.1220 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.43820152 0.4625052 -0.23585944] Sparsity at: 0.6438542449286251 Epoch 317/500 235/235 [==============================] - 3s 13ms/step - loss: 8.8828e-06 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.43813828 0.46444348 -0.23539029] Sparsity at: 0.6438542449286251 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1764e-05 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.4399239 0.4555099 -0.23476665] Sparsity at: 0.6438542449286251 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5185e-05 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9838 [ 0.07077749 0. -0.06288844 ... 0.43964484 0.46139845 -0.24377984] Sparsity at: 0.6438542449286251 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1327 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.44011313 0.46664482 -0.24851525] Sparsity at: 0.6438542449286251 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1407 - val_accuracy: 0.9808 [ 0.07077749 0. -0.06288844 ... 0.42823315 0.45817065 -0.23862125] Sparsity at: 0.6438542449286251 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8530e-04 - accuracy: 0.9999 - val_loss: 0.1338 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.4261958 0.4649532 -0.26518995] Sparsity at: 0.6438542449286251 Epoch 323/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6246e-04 - accuracy: 0.9999 - val_loss: 0.1324 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.42834544 0.46735 -0.2662315 ] Sparsity at: 0.6438542449286251 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 8.4722e-05 - accuracy: 1.0000 - val_loss: 0.1306 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.42952496 0.46396297 -0.271135 ] Sparsity at: 0.6438542449286251 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0491e-05 - accuracy: 1.0000 - val_loss: 0.1310 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.42987752 0.4656982 -0.26958302] Sparsity at: 0.6438542449286251 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4479e-05 - accuracy: 1.0000 - val_loss: 0.1305 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.4292314 0.46584547 -0.26956308] Sparsity at: 0.6438542449286251 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5489e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.42950842 0.46604738 -0.27050596] Sparsity at: 0.6438542449286251 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4445e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.4295939 0.46606135 -0.27090785] Sparsity at: 0.6438542449286251 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3499e-05 - accuracy: 1.0000 - val_loss: 0.1290 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.42977202 0.46649215 -0.2704137 ] Sparsity at: 0.6438542449286251 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1288e-05 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9821 [ 0.07077749 0. -0.06288844 ... 0.43013737 0.46660137 -0.26970643] Sparsity at: 0.6438542449286251 Epoch 331/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4900e-05 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.4300094 0.467166 -0.26899195] Sparsity at: 0.6438542449286251 Epoch 332/500 235/235 [==============================] - 3s 13ms/step - loss: 9.4384e-06 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.4301048 0.46699572 -0.26826382] Sparsity at: 0.6438542449286251 Epoch 333/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1563e-05 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.43032768 0.4659848 -0.26888448] Sparsity at: 0.6438542449286251 Epoch 334/500 235/235 [==============================] - 3s 13ms/step - loss: 7.3359e-06 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.43039677 0.4668266 -0.26918292] Sparsity at: 0.6438542449286251 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2065e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.42345542 0.46653143 -0.26314574] Sparsity at: 0.6438542449286251 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7870e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.42497975 0.46668223 -0.26682302] Sparsity at: 0.6438542449286251 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9646e-05 - accuracy: 1.0000 - val_loss: 0.1280 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.43275326 0.46780232 -0.26771256] Sparsity at: 0.6438542449286251 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5351e-06 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.43284553 0.46949047 -0.2696851 ] Sparsity at: 0.6438542449286251 Epoch 339/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5111e-04 - accuracy: 1.0000 - val_loss: 0.1278 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.42415938 0.46757483 -0.27210495] Sparsity at: 0.6438542449286251 Epoch 340/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1653 - val_accuracy: 0.9780 [ 0.07077749 0. -0.06288844 ... 0.4275187 0.47547105 -0.27360895] Sparsity at: 0.6438542449286251 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1450 - val_accuracy: 0.9803 [ 0.07077749 0. -0.06288844 ... 0.47187608 0.48324662 -0.2693155 ] Sparsity at: 0.6438542449286251 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1389 - val_accuracy: 0.9802 [ 0.07077749 0. -0.06288844 ... 0.4644055 0.48230964 -0.271811 ] Sparsity at: 0.6438542449286251 Epoch 343/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3541e-04 - accuracy: 0.9999 - val_loss: 0.1320 - val_accuracy: 0.9819 [ 0.07077749 0. -0.06288844 ... 0.46547836 0.47751367 -0.2646264 ] Sparsity at: 0.6438542449286251 Epoch 344/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5363e-04 - accuracy: 0.9999 - val_loss: 0.1337 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.46346977 0.48163694 -0.261578 ] Sparsity at: 0.6438542449286251 Epoch 345/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7867e-05 - accuracy: 1.0000 - val_loss: 0.1317 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.46416602 0.48120773 -0.26283646] Sparsity at: 0.6438542449286251 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3932e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.46824515 0.4789938 -0.2601951 ] Sparsity at: 0.6438542449286251 Epoch 347/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7060e-05 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.46632048 0.47913274 -0.26188332] Sparsity at: 0.6438542449286251 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2089e-04 - accuracy: 0.9999 - val_loss: 0.1315 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.46590853 0.48487112 -0.25576824] Sparsity at: 0.6438542449286251 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9970e-05 - accuracy: 1.0000 - val_loss: 0.1314 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.4661097 0.48450857 -0.2557809 ] Sparsity at: 0.6438542449286251 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9277e-05 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.46662214 0.48461914 -0.25185353] Sparsity at: 0.6438542449286251 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.5180378987403387 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.5247421684808558 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.8768825577847039 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 29s 12ms/step - loss: 1.3661e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9823 [ 0.07077749 0. -0.06288844 ... 0.46659395 0.48465043 -0.25360987] Sparsity at: 0.6438542449286251 Epoch 352/500 235/235 [==============================] - 3s 13ms/step - loss: 9.2237e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.46653965 0.4845622 -0.25327763] Sparsity at: 0.6438542449286251 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0798e-04 - accuracy: 0.9999 - val_loss: 0.1416 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.46509033 0.47266546 -0.22879739] Sparsity at: 0.6438542449286251 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0789e-04 - accuracy: 0.9999 - val_loss: 0.1388 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.47191072 0.46594515 -0.2337453 ] Sparsity at: 0.6438542449286251 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9816e-05 - accuracy: 1.0000 - val_loss: 0.1410 - val_accuracy: 0.9812 [ 0.07077749 0. -0.06288844 ... 0.48601577 0.46847975 -0.24111599] Sparsity at: 0.6438542449286251 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6786e-05 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.48619744 0.46819097 -0.23277113] Sparsity at: 0.6438542449286251 Epoch 357/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0178e-05 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.4877941 0.46550992 -0.23399201] Sparsity at: 0.6438542449286251 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2157e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.486053 0.46680996 -0.23518828] Sparsity at: 0.6438542449286251 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4202e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.466299 0.46591148 -0.23750442] Sparsity at: 0.6438542449286251 Epoch 360/500 235/235 [==============================] - 3s 13ms/step - loss: 8.8647e-06 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.46845478 0.46588182 -0.23798071] Sparsity at: 0.6438542449286251 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1211e-06 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.46868172 0.46605375 -0.23730879] Sparsity at: 0.6438542449286251 Epoch 362/500 235/235 [==============================] - 3s 13ms/step - loss: 9.4605e-06 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.46814448 0.46373022 -0.2369678 ] Sparsity at: 0.6438542449286251 Epoch 363/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4956e-04 - accuracy: 1.0000 - val_loss: 0.1447 - val_accuracy: 0.9819 [ 0.07077749 0. -0.06288844 ... 0.45318475 0.46331736 -0.23964566] Sparsity at: 0.6438542449286251 Epoch 364/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1704 - val_accuracy: 0.9777 [ 0.07077749 0. -0.06288844 ... 0.41916114 0.48248497 -0.16598772] Sparsity at: 0.6438542449286251 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1485 - val_accuracy: 0.9790 [ 0.07077749 0. -0.06288844 ... 0.41373438 0.48888516 -0.1709411 ] Sparsity at: 0.6438542449286251 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4066e-04 - accuracy: 0.9997 - val_loss: 0.1445 - val_accuracy: 0.9797 [ 0.07077749 0. -0.06288844 ... 0.42586762 0.47692442 -0.20348479] Sparsity at: 0.6438542449286251 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0443e-04 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.42785695 0.47527185 -0.20298952] Sparsity at: 0.6438542449286251 Epoch 368/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0094e-04 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9804 [ 0.07077749 0. -0.06288844 ... 0.42789787 0.47180867 -0.19704741] Sparsity at: 0.6438542449286251 Epoch 369/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4511e-05 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9802 [ 0.07077749 0. -0.06288844 ... 0.42751023 0.46833578 -0.19928248] Sparsity at: 0.6438542449286251 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2033e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9803 [ 0.07077749 0. -0.06288844 ... 0.42841038 0.46903673 -0.20228204] Sparsity at: 0.6438542449286251 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6653e-05 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9803 [ 0.07077749 0. -0.06288844 ... 0.428572 0.46981385 -0.20135759] Sparsity at: 0.6438542449286251 Epoch 372/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2338e-05 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9803 [ 0.07077749 0. -0.06288844 ... 0.42711174 0.47021908 -0.20005125] Sparsity at: 0.6438542449286251 Epoch 373/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9105e-05 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.43130633 0.46924168 -0.20078124] Sparsity at: 0.6438542449286251 Epoch 374/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0250e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9805 [ 0.07077749 0. -0.06288844 ... 0.43118167 0.46967822 -0.20062515] Sparsity at: 0.6438542449286251 Epoch 375/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1808e-05 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.4308049 0.4694221 -0.20060432] Sparsity at: 0.6438542449286251 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1038e-05 - accuracy: 1.0000 - val_loss: 0.1336 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.43051073 0.4698368 -0.20070715] Sparsity at: 0.6438542449286251 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0458e-05 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9803 [ 0.07077749 0. -0.06288844 ... 0.4305707 0.4701463 -0.20150976] Sparsity at: 0.6438542449286251 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1303e-05 - accuracy: 1.0000 - val_loss: 0.1333 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.43102202 0.46972138 -0.20182481] Sparsity at: 0.6438542449286251 Epoch 379/500 235/235 [==============================] - 3s 13ms/step - loss: 6.9515e-06 - accuracy: 1.0000 - val_loss: 0.1327 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.43160513 0.4700328 -0.20369792] Sparsity at: 0.6438542449286251 Epoch 380/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9113e-06 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.43151382 0.46998864 -0.20359817] Sparsity at: 0.6438542449286251 Epoch 381/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1813e-05 - accuracy: 1.0000 - val_loss: 0.1378 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.4322263 0.47535962 -0.20460725] Sparsity at: 0.6438542449286251 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7013e-04 - accuracy: 0.9999 - val_loss: 0.1725 - val_accuracy: 0.9782 [ 0.07077749 0. -0.06288844 ... 0.4302846 0.48686263 -0.20454825] Sparsity at: 0.6438542449286251 Epoch 383/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 0.1712 - val_accuracy: 0.9775 [ 0.07077749 0. -0.06288844 ... 0.39539206 0.43208456 -0.2051205 ] Sparsity at: 0.6438542449286251 Epoch 384/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1324 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.40214753 0.43324134 -0.1946687 ] Sparsity at: 0.6438542449286251 Epoch 385/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9623e-04 - accuracy: 0.9999 - val_loss: 0.1311 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.4049466 0.4399004 -0.20298763] Sparsity at: 0.6438542449286251 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0819e-04 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.40500405 0.44757605 -0.20154642] Sparsity at: 0.6438542449286251 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7583e-05 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.40602788 0.44834328 -0.20254017] Sparsity at: 0.6438542449286251 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2887e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.4065044 0.44870567 -0.2025381 ] Sparsity at: 0.6438542449286251 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5620e-05 - accuracy: 1.0000 - val_loss: 0.1297 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.40729937 0.44836706 -0.20325677] Sparsity at: 0.6438542449286251 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4521e-05 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.40679067 0.44526297 -0.20275857] Sparsity at: 0.6438542449286251 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0119e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.40689707 0.44598022 -0.20324397] Sparsity at: 0.6438542449286251 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4813e-05 - accuracy: 1.0000 - val_loss: 0.1269 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.40675643 0.44590604 -0.20351996] Sparsity at: 0.6438542449286251 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0206e-05 - accuracy: 1.0000 - val_loss: 0.1276 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.40645823 0.4469996 -0.2040516 ] Sparsity at: 0.6438542449286251 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6769e-05 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.4068002 0.44782612 -0.20421644] Sparsity at: 0.6438542449286251 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1687e-04 - accuracy: 0.9998 - val_loss: 0.1388 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.41350037 0.45085502 -0.20095164] Sparsity at: 0.6438542449286251 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1443 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.42426047 0.4495752 -0.21863194] Sparsity at: 0.6438542449286251 Epoch 397/500 235/235 [==============================] - 3s 13ms/step - loss: 3.6669e-04 - accuracy: 0.9999 - val_loss: 0.1444 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.41690397 0.44819644 -0.20617718] Sparsity at: 0.6438542449286251 Epoch 398/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1887e-04 - accuracy: 0.9999 - val_loss: 0.1432 - val_accuracy: 0.9800 [ 0.07077749 0. -0.06288844 ... 0.4240171 0.47011942 -0.19697481] Sparsity at: 0.6438542449286251 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 8.4680e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.42079747 0.4556221 -0.19788358] Sparsity at: 0.6438542449286251 Epoch 400/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2196e-04 - accuracy: 1.0000 - val_loss: 0.1459 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.42416036 0.45677388 -0.19431537] Sparsity at: 0.6438542449286251 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.6046858922077618 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [0. 1. 0. ... 1. 1. 0.] [0. 0. 1. ... 1. 0. 0.] ... [0. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 1. 1.] [0. 0. 0. ... 0. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.5921665441971413 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458667 tf.Tensor( [[1. 0. 0. ... 0. 0. 1.] [1. 0. 1. ... 1. 0. 0.] [0. 1. 0. ... 0. 1. 0.] ... [0. 0. 0. ... 0. 0. 1.] [0. 0. 0. ... 1. 0. 0.] [0. 1. 0. ... 1. 0. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.931536803178993 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.112 tf.Tensor( [[1. 1. 1. 0. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 0. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 0. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 0.] [0. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 0. 1. 0. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 0. 1. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [0. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 0. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 0. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 0. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 0. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 0. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 0. 0. 0. 0. 0. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 0. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 0. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 1. 1. 1. 1. 1. 0. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [0. 1. 1. 1. 0. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 0. 0. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 28s 12ms/step - loss: 1.7671e-04 - accuracy: 0.9999 - val_loss: 0.1450 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.42883876 0.460115 -0.20056944] Sparsity at: 0.6438542449286251 Epoch 402/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1376e-05 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.42334628 0.46117985 -0.19611774] Sparsity at: 0.6438542449286251 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4371e-04 - accuracy: 0.9999 - val_loss: 0.1562 - val_accuracy: 0.9799 [ 0.07077749 0. -0.06288844 ... 0.42438868 0.46356696 -0.18928646] Sparsity at: 0.6438542449286251 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1700 - val_accuracy: 0.9783 [ 0.07077749 0. -0.06288844 ... 0.42911965 0.47714487 -0.20401563] Sparsity at: 0.6438542449286251 Epoch 405/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1476 - val_accuracy: 0.9804 [ 0.07077749 0. -0.06288844 ... 0.41569626 0.46163276 -0.17767999] Sparsity at: 0.6438542449286251 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6914e-04 - accuracy: 0.9998 - val_loss: 0.1450 - val_accuracy: 0.9804 [ 0.07077749 0. -0.06288844 ... 0.42138007 0.4512545 -0.18764167] Sparsity at: 0.6438542449286251 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1204e-04 - accuracy: 0.9999 - val_loss: 0.1434 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.40479785 0.4521429 -0.18671976] Sparsity at: 0.6438542449286251 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0221e-05 - accuracy: 1.0000 - val_loss: 0.1415 - val_accuracy: 0.9803 [ 0.07077749 0. -0.06288844 ... 0.40421534 0.44902214 -0.18592589] Sparsity at: 0.6438542449286251 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4284e-05 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.40451697 0.44817033 -0.18559672] Sparsity at: 0.6438542449286251 Epoch 410/500 235/235 [==============================] - 3s 13ms/step - loss: 1.8784e-05 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.4057004 0.44790584 -0.18604806] Sparsity at: 0.6438542449286251 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5021e-05 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.40531087 0.44686997 -0.18296719] Sparsity at: 0.6438542449286251 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3355e-04 - accuracy: 0.9999 - val_loss: 0.1420 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.4057958 0.44341525 -0.19059905] Sparsity at: 0.6438542449286251 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0402e-05 - accuracy: 1.0000 - val_loss: 0.1453 - val_accuracy: 0.9805 [ 0.07077749 0. -0.06288844 ... 0.4055903 0.44498333 -0.19386262] Sparsity at: 0.6438542449286251 Epoch 414/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5241e-05 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.40623528 0.44614255 -0.19156435] Sparsity at: 0.6438542449286251 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 9.1947e-06 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.40646112 0.44667435 -0.19052126] Sparsity at: 0.6438542449286251 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7740e-05 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.40671727 0.4460235 -0.19220746] Sparsity at: 0.6438542449286251 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2593e-06 - accuracy: 1.0000 - val_loss: 0.1439 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.4070784 0.4466219 -0.19278365] Sparsity at: 0.6438542449286251 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4189e-06 - accuracy: 1.0000 - val_loss: 0.1432 - val_accuracy: 0.9812 [ 0.07077749 0. -0.06288844 ... 0.40830868 0.4472023 -0.19253796] Sparsity at: 0.6438542449286251 Epoch 419/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9361e-06 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.40846536 0.44806784 -0.19334066] Sparsity at: 0.6438542449286251 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4575e-04 - accuracy: 0.9999 - val_loss: 0.1505 - val_accuracy: 0.9799 [ 0.07077749 0. -0.06288844 ... 0.40843773 0.4544167 -0.1932362 ] Sparsity at: 0.6438542449286251 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6182e-04 - accuracy: 0.9997 - val_loss: 0.1614 - val_accuracy: 0.9793 [ 0.07077749 0. -0.06288844 ... 0.40887386 0.46649918 -0.18420753] Sparsity at: 0.6438542449286251 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1478 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.4075573 0.4631219 -0.234869 ] Sparsity at: 0.6438542449286251 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 9.9960e-04 - accuracy: 0.9997 - val_loss: 0.1415 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.42500836 0.48527256 -0.23199649] Sparsity at: 0.6438542449286251 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0986e-04 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.4240685 0.48286787 -0.23159134] Sparsity at: 0.6438542449286251 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8230e-05 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.42435187 0.47024783 -0.23339601] Sparsity at: 0.6438542449286251 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4382e-05 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.4254036 0.47141415 -0.23339866] Sparsity at: 0.6438542449286251 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9048e-05 - accuracy: 1.0000 - val_loss: 0.1370 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.42557776 0.47151312 -0.233947 ] Sparsity at: 0.6438542449286251 Epoch 428/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5389e-05 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.426025 0.47207215 -0.2338087 ] Sparsity at: 0.6438542449286251 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3780e-05 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.4258118 0.4734469 -0.23397428] Sparsity at: 0.6438542449286251 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9460e-04 - accuracy: 0.9999 - val_loss: 0.1471 - val_accuracy: 0.9812 [ 0.07077749 0. -0.06288844 ... 0.40847072 0.47838572 -0.24468292] Sparsity at: 0.6438542449286251 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6179e-04 - accuracy: 0.9999 - val_loss: 0.1464 - val_accuracy: 0.9805 [ 0.07077749 0. -0.06288844 ... 0.4202029 0.51112926 -0.26185438] Sparsity at: 0.6438542449286251 Epoch 432/500 235/235 [==============================] - 3s 13ms/step - loss: 9.7000e-05 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.4285372 0.50693166 -0.26521748] Sparsity at: 0.6438542449286251 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5968e-05 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9805 [ 0.07077749 0. -0.06288844 ... 0.42772502 0.5059608 -0.2641646 ] Sparsity at: 0.6438542449286251 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3589e-05 - accuracy: 1.0000 - val_loss: 0.1422 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.42882875 0.5068606 -0.26437652] Sparsity at: 0.6438542449286251 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7081e-06 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.42939907 0.50940645 -0.2664193 ] Sparsity at: 0.6438542449286251 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3343e-05 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.43300053 0.50744265 -0.26468062] Sparsity at: 0.6438542449286251 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7602e-06 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9818 [ 0.07077749 0. -0.06288844 ... 0.4330128 0.5102368 -0.2643186 ] Sparsity at: 0.6438542449286251 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 8.4737e-06 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.43244988 0.5100613 -0.26775876] Sparsity at: 0.6438542449286251 Epoch 439/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0817e-06 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.4324163 0.5092308 -0.26980644] Sparsity at: 0.6438542449286251 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0981e-06 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9806 [ 0.07077749 0. -0.06288844 ... 0.4332692 0.5097835 -0.27161813] Sparsity at: 0.6438542449286251 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3565e-06 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.43204084 0.5098135 -0.26897103] Sparsity at: 0.6438542449286251 Epoch 442/500 235/235 [==============================] - 3s 13ms/step - loss: 4.2442e-06 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.4317629 0.5095644 -0.26920965] Sparsity at: 0.6438542449286251 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7558e-06 - accuracy: 1.0000 - val_loss: 0.1387 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.4324276 0.5076834 -0.26943263] Sparsity at: 0.6438542449286251 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0799e-06 - accuracy: 1.0000 - val_loss: 0.1380 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.43212533 0.5075647 -0.27002066] Sparsity at: 0.6438542449286251 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4847e-06 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.43259212 0.50799346 -0.27029562] Sparsity at: 0.6438542449286251 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8583e-06 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.432728 0.5079018 -0.270239 ] Sparsity at: 0.6438542449286251 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5473e-04 - accuracy: 0.9999 - val_loss: 0.1573 - val_accuracy: 0.9802 [ 0.07077749 0. -0.06288844 ... 0.43423587 0.52683544 -0.28600466] Sparsity at: 0.6438542449286251 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1624 - val_accuracy: 0.9805 [ 0.07077749 0. -0.06288844 ... 0.4222902 0.5550759 -0.23964843] Sparsity at: 0.6438542449286251 Epoch 449/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1419 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.43641293 0.53332084 -0.23893799] Sparsity at: 0.6438542449286251 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5286e-04 - accuracy: 0.9999 - val_loss: 0.1427 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.43350193 0.53451693 -0.24491473] Sparsity at: 0.6438542449286251 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7702e-04 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.43703312 0.5321885 -0.24372284] Sparsity at: 0.6438542449286251 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5816e-05 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.43172985 0.53457236 -0.2358054 ] Sparsity at: 0.6438542449286251 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2468e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9826 [ 0.07077749 0. -0.06288844 ... 0.43833405 0.5338179 -0.24821232] Sparsity at: 0.6438542449286251 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8369e-05 - accuracy: 1.0000 - val_loss: 0.1400 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.43917143 0.5337681 -0.24877685] Sparsity at: 0.6438542449286251 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9688e-05 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.44050607 0.5342633 -0.24977319] Sparsity at: 0.6438542449286251 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2061e-05 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.4451866 0.53496736 -0.25838077] Sparsity at: 0.6438542449286251 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3137e-05 - accuracy: 1.0000 - val_loss: 0.1386 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.4448861 0.5349371 -0.2579346 ] Sparsity at: 0.6438542449286251 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4595e-05 - accuracy: 0.9999 - val_loss: 0.1403 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.46686864 0.53097445 -0.2587954 ] Sparsity at: 0.6438542449286251 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6467e-05 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.46903396 0.53116775 -0.25846112] Sparsity at: 0.6438542449286251 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5165e-05 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9829 [ 0.07077749 0. -0.06288844 ... 0.47039732 0.5387912 -0.2660806 ] Sparsity at: 0.6438542449286251 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6324e-05 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9827 [ 0.07077749 0. -0.06288844 ... 0.47311032 0.5427032 -0.26373452] Sparsity at: 0.6438542449286251 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2443e-04 - accuracy: 0.9999 - val_loss: 0.1487 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.48527578 0.5629291 -0.27793238] Sparsity at: 0.6438542449286251 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5323e-04 - accuracy: 0.9998 - val_loss: 0.1529 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.48391157 0.5419184 -0.26525414] Sparsity at: 0.6438542449286251 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0819e-04 - accuracy: 0.9999 - val_loss: 0.1535 - val_accuracy: 0.9822 [ 0.07077749 0. -0.06288844 ... 0.48714978 0.5409793 -0.27558827] Sparsity at: 0.6438542449286251 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7454e-04 - accuracy: 0.9999 - val_loss: 0.1499 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.48920706 0.54246724 -0.27865174] Sparsity at: 0.6438542449286251 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0713e-04 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9820 [ 0.07077749 0. -0.06288844 ... 0.48981076 0.5471252 -0.28176633] Sparsity at: 0.6438542449286251 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2876e-05 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9824 [ 0.07077749 0. -0.06288844 ... 0.4918682 0.54582596 -0.277838 ] Sparsity at: 0.6438542449286251 Epoch 468/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6254e-04 - accuracy: 0.9999 - val_loss: 0.1452 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.4890232 0.5469762 -0.27492696] Sparsity at: 0.6438542449286251 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9610e-04 - accuracy: 0.9999 - val_loss: 0.1432 - val_accuracy: 0.9825 [ 0.07077749 0. -0.06288844 ... 0.50556743 0.54385644 -0.30041164] Sparsity at: 0.6438542449286251 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9387e-05 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9830 [ 0.07077749 0. -0.06288844 ... 0.5069458 0.5450763 -0.29973534] Sparsity at: 0.6438542449286251 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1957e-05 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.50785327 0.5534707 -0.29901218] Sparsity at: 0.6438542449286251 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4169e-06 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9836 [ 0.07077749 0. -0.06288844 ... 0.5084165 0.5527857 -0.29904526] Sparsity at: 0.6438542449286251 Epoch 473/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3160e-06 - accuracy: 1.0000 - val_loss: 0.1413 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.51033986 0.5537472 -0.29901275] Sparsity at: 0.6438542449286251 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7215e-06 - accuracy: 1.0000 - val_loss: 0.1409 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.5107096 0.55317456 -0.29785022] Sparsity at: 0.6438542449286251 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6373e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9828 [ 0.07077749 0. -0.06288844 ... 0.51090926 0.5474651 -0.29760975] Sparsity at: 0.6438542449286251 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5527e-05 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9831 [ 0.07077749 0. -0.06288844 ... 0.51027447 0.5470878 -0.28574395] Sparsity at: 0.6438542449286251 Epoch 477/500 235/235 [==============================] - 3s 13ms/step - loss: 8.2826e-06 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.5091418 0.54859954 -0.28612548] Sparsity at: 0.6438542449286251 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8347e-06 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9832 [ 0.07077749 0. -0.06288844 ... 0.5088205 0.5439255 -0.28585672] Sparsity at: 0.6438542449286251 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5194e-06 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9833 [ 0.07077749 0. -0.06288844 ... 0.50934625 0.54437894 -0.28515556] Sparsity at: 0.6438542449286251 Epoch 480/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4359e-06 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.50964963 0.5447102 -0.2852954 ] Sparsity at: 0.6438542449286251 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3368e-06 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9835 [ 0.07077749 0. -0.06288844 ... 0.50971174 0.5436817 -0.28485602] Sparsity at: 0.6438542449286251 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3325e-06 - accuracy: 1.0000 - val_loss: 0.1377 - val_accuracy: 0.9834 [ 0.07077749 0. -0.06288844 ... 0.50753284 0.5417522 -0.28370687] Sparsity at: 0.6438542449286251 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6525e-04 - accuracy: 0.9999 - val_loss: 0.1501 - val_accuracy: 0.9810 [ 0.07077749 0. -0.06288844 ... 0.4957314 0.5467635 -0.27302957] Sparsity at: 0.6438542449286251 Epoch 484/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1854 - val_accuracy: 0.9790 [ 0.07077749 0. -0.06288844 ... 0.4920212 0.5643803 -0.31576407] Sparsity at: 0.6438542449286251 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1602 - val_accuracy: 0.9807 [ 0.07077749 0. -0.06288844 ... 0.47613388 0.5374216 -0.2904787 ] Sparsity at: 0.6438542449286251 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0086e-04 - accuracy: 0.9999 - val_loss: 0.1543 - val_accuracy: 0.9812 [ 0.07077749 0. -0.06288844 ... 0.47618225 0.5218313 -0.2959403 ] Sparsity at: 0.6438542449286251 Epoch 487/500 235/235 [==============================] - 3s 13ms/step - loss: 4.1989e-05 - accuracy: 1.0000 - val_loss: 0.1523 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.47708914 0.5256967 -0.29647672] Sparsity at: 0.6438542449286251 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0401e-05 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.47980717 0.52705085 -0.29965132] Sparsity at: 0.6438542449286251 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4800e-05 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9812 [ 0.07077749 0. -0.06288844 ... 0.4796992 0.52809614 -0.29820043] Sparsity at: 0.6438542449286251 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3399e-05 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9811 [ 0.07077749 0. -0.06288844 ... 0.47978666 0.52830267 -0.29754686] Sparsity at: 0.6438542449286251 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2346e-05 - accuracy: 1.0000 - val_loss: 0.1535 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.47993326 0.52759266 -0.29760388] Sparsity at: 0.6438542449286251 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6654e-06 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9816 [ 0.07077749 0. -0.06288844 ... 0.48044232 0.5275732 -0.3002672 ] Sparsity at: 0.6438542449286251 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3905e-06 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.48052284 0.5272193 -0.29992884] Sparsity at: 0.6438542449286251 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7102e-06 - accuracy: 1.0000 - val_loss: 0.1525 - val_accuracy: 0.9817 [ 0.07077749 0. -0.06288844 ... 0.48059866 0.52658325 -0.30104485] Sparsity at: 0.6438542449286251 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6726e-06 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.48027962 0.5266189 -0.30040017] Sparsity at: 0.6438542449286251 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9878e-05 - accuracy: 1.0000 - val_loss: 0.1538 - val_accuracy: 0.9813 [ 0.07077749 0. -0.06288844 ... 0.47981068 0.52152103 -0.29219696] Sparsity at: 0.6438542449286251 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4740e-05 - accuracy: 1.0000 - val_loss: 0.1570 - val_accuracy: 0.9815 [ 0.07077749 0. -0.06288844 ... 0.47968468 0.5151563 -0.2904566 ] Sparsity at: 0.6438542449286251 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0186e-06 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9808 [ 0.07077749 0. -0.06288844 ... 0.47794333 0.5176247 -0.29083908] Sparsity at: 0.6438542449286251 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9125e-06 - accuracy: 1.0000 - val_loss: 0.1559 - val_accuracy: 0.9814 [ 0.07077749 0. -0.06288844 ... 0.47900727 0.5184995 -0.29067016] Sparsity at: 0.6438542449286251 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1888e-06 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9809 [ 0.07077749 0. -0.06288844 ... 0.47838292 0.52062374 -0.28904247] Sparsity at: 0.6438542449286251 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.042060211300849915 Thresholhold 0.08002246171236038 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08791892230510712 Thresholhold 0.1589777022600174 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10110617056488991 Thresholhold 0.0191974937915802 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:03 - loss: 4.2897 - accuracy: 0.0742WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0094s vs `on_train_batch_begin` time: 2.4542s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 1.7465 - accuracy: 0.8285 - val_loss: 1.1040 - val_accuracy: 0.8917 [-7.4224058e-06 0.0000000e+00 2.2883007e-06 ... 9.1420211e-02 1.8523102e-01 2.6269946e-03] Sparsity at: 0.4915671942060086 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0418 - accuracy: 0.8901 - val_loss: 0.9803 - val_accuracy: 0.8950 [5.5615214e-11 0.0000000e+00 1.8172165e-12 ... 7.1047172e-02 1.9423500e-01 3.5472132e-02] Sparsity at: 0.4915671942060086 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9832 - accuracy: 0.8912 - val_loss: 0.9537 - val_accuracy: 0.8953 [-2.0745168e-16 0.0000000e+00 7.2925599e-17 ... 6.2335417e-02 2.0030734e-01 5.1703487e-02] Sparsity at: 0.4915671942060086 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9653 - accuracy: 0.8908 - val_loss: 0.9417 - val_accuracy: 0.8954 [1.9640492e-22 0.0000000e+00 1.8779860e-22 ... 6.1129581e-02 2.0704530e-01 6.1727054e-02] Sparsity at: 0.4915671942060086 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9558 - accuracy: 0.8909 - val_loss: 0.9346 - val_accuracy: 0.8957 [-4.5747796e-27 0.0000000e+00 4.8975609e-28 ... 6.3969858e-02 2.1535599e-01 7.0888698e-02] Sparsity at: 0.4915671942060086 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9499 - accuracy: 0.8909 - val_loss: 0.9286 - val_accuracy: 0.8968 [1.8158581e-32 0.0000000e+00 4.2263033e-33 ... 6.8268828e-02 2.2460929e-01 7.9503991e-02] Sparsity at: 0.4915671942060086 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9452 - accuracy: 0.8913 - val_loss: 0.9252 - val_accuracy: 0.8969 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.0614852e-02 2.3499019e-01 8.8251404e-02] Sparsity at: 0.4915671942060086 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9417 - accuracy: 0.8914 - val_loss: 0.9221 - val_accuracy: 0.8970 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.0677243e-02 2.4471071e-01 9.6470110e-02] Sparsity at: 0.4915671942060086 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9389 - accuracy: 0.8916 - val_loss: 0.9191 - val_accuracy: 0.8982 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 6.88669160e-02 2.53230780e-01 1.03944845e-01] Sparsity at: 0.4915671942060086 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9366 - accuracy: 0.8916 - val_loss: 0.9171 - val_accuracy: 0.8984 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.5937124e-02 2.6004481e-01 1.1106949e-01] Sparsity at: 0.4915671942060086 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9346 - accuracy: 0.8917 - val_loss: 0.9157 - val_accuracy: 0.8976 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 6.23908453e-02 2.65252769e-01 1.17580526e-01] Sparsity at: 0.4915671942060086 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9330 - accuracy: 0.8917 - val_loss: 0.9139 - val_accuracy: 0.8982 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 5.88212200e-02 2.68747896e-01 1.23253986e-01] Sparsity at: 0.4915671942060086 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9316 - accuracy: 0.8917 - val_loss: 0.9126 - val_accuracy: 0.8986 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4867674e-02 2.7098715e-01 1.2849534e-01] Sparsity at: 0.4915671942060086 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9302 - accuracy: 0.8922 - val_loss: 0.9113 - val_accuracy: 0.8987 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.0561082e-02 2.7167642e-01 1.3325092e-01] Sparsity at: 0.4915671942060086 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9292 - accuracy: 0.8919 - val_loss: 0.9103 - val_accuracy: 0.8989 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.6809115e-02 2.7069014e-01 1.3743837e-01] Sparsity at: 0.4915671942060086 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9282 - accuracy: 0.8920 - val_loss: 0.9094 - val_accuracy: 0.8992 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.3219432e-02 2.6908079e-01 1.4089060e-01] Sparsity at: 0.4915671942060086 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9273 - accuracy: 0.8921 - val_loss: 0.9085 - val_accuracy: 0.8991 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.0329464e-02 2.6668891e-01 1.4359517e-01] Sparsity at: 0.4915671942060086 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9265 - accuracy: 0.8922 - val_loss: 0.9076 - val_accuracy: 0.8994 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.7875161e-02 2.6358977e-01 1.4594245e-01] Sparsity at: 0.4915671942060086 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9257 - accuracy: 0.8925 - val_loss: 0.9073 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.6208257e-02 2.5999558e-01 1.4792636e-01] Sparsity at: 0.4915671942060086 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9251 - accuracy: 0.8924 - val_loss: 0.9067 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.4922738e-02 2.5580740e-01 1.4946622e-01] Sparsity at: 0.4915671942060086 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9248 - accuracy: 0.8927 - val_loss: 0.9064 - val_accuracy: 0.8992 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.3873215e-02 2.5143060e-01 1.5102518e-01] Sparsity at: 0.4915671942060086 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9242 - accuracy: 0.8929 - val_loss: 0.9057 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.3269815e-02 2.4688014e-01 1.5208051e-01] Sparsity at: 0.4915671942060086 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9237 - accuracy: 0.8928 - val_loss: 0.9056 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.2733459e-02 2.4219456e-01 1.5286732e-01] Sparsity at: 0.4915671942060086 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9235 - accuracy: 0.8929 - val_loss: 0.9049 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.2235209e-02 2.3766281e-01 1.5349254e-01] Sparsity at: 0.4915671942060086 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9230 - accuracy: 0.8929 - val_loss: 0.9047 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.1609174e-02 2.3317803e-01 1.5377440e-01] Sparsity at: 0.4915671942060086 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9226 - accuracy: 0.8929 - val_loss: 0.9044 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.0940700e-02 2.2863853e-01 1.5369570e-01] Sparsity at: 0.4915671942060086 Epoch 27/500 235/235 [==============================] - 2s 10ms/step - loss: 0.9223 - accuracy: 0.8929 - val_loss: 0.9042 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.0517349e-02 2.2397560e-01 1.5312962e-01] Sparsity at: 0.4915671942060086 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9218 - accuracy: 0.8931 - val_loss: 0.9035 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.9502772e-02 2.1947739e-01 1.5237187e-01] Sparsity at: 0.4915671942060086 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9214 - accuracy: 0.8931 - val_loss: 0.9030 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.8751884e-02 2.1532147e-01 1.5125379e-01] Sparsity at: 0.4915671942060086 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9212 - accuracy: 0.8928 - val_loss: 0.9029 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.7599463e-02 2.1130493e-01 1.5000422e-01] Sparsity at: 0.4915671942060086 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9209 - accuracy: 0.8930 - val_loss: 0.9024 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.6702497e-02 2.0790255e-01 1.4849132e-01] Sparsity at: 0.4915671942060086 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9205 - accuracy: 0.8928 - val_loss: 0.9022 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.5605327e-02 2.0461538e-01 1.4743079e-01] Sparsity at: 0.4915671942060086 Epoch 33/500 235/235 [==============================] - 2s 10ms/step - loss: 0.9204 - accuracy: 0.8930 - val_loss: 0.9021 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.4730243e-02 2.0169476e-01 1.4624277e-01] Sparsity at: 0.4915671942060086 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9201 - accuracy: 0.8930 - val_loss: 0.9017 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.3780614e-02 1.9894122e-01 1.4527246e-01] Sparsity at: 0.4915671942060086 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9198 - accuracy: 0.8931 - val_loss: 0.9019 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.3090370e-02 1.9644079e-01 1.4442804e-01] Sparsity at: 0.4915671942060086 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9196 - accuracy: 0.8933 - val_loss: 0.9014 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.2501219e-02 1.9398968e-01 1.4362805e-01] Sparsity at: 0.4915671942060086 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9195 - accuracy: 0.8931 - val_loss: 0.9017 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.2063103e-02 1.9154590e-01 1.4326109e-01] Sparsity at: 0.4915671942060086 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9194 - accuracy: 0.8931 - val_loss: 0.9012 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1750258e-02 1.8923843e-01 1.4279950e-01] Sparsity at: 0.4915671942060086 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9191 - accuracy: 0.8932 - val_loss: 0.9010 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1524100e-02 1.8683176e-01 1.4245489e-01] Sparsity at: 0.4915671942060086 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9189 - accuracy: 0.8934 - val_loss: 0.9009 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1551613e-02 1.8502434e-01 1.4205465e-01] Sparsity at: 0.4915671942060086 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9187 - accuracy: 0.8931 - val_loss: 0.9007 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1379322e-02 1.8288174e-01 1.4197750e-01] Sparsity at: 0.4915671942060086 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9184 - accuracy: 0.8931 - val_loss: 0.9007 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1247327e-02 1.8119808e-01 1.4184292e-01] Sparsity at: 0.4915671942060086 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9184 - accuracy: 0.8932 - val_loss: 0.9003 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1291398e-02 1.7937386e-01 1.4194813e-01] Sparsity at: 0.4915671942060086 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9182 - accuracy: 0.8934 - val_loss: 0.9004 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1375123e-02 1.7775756e-01 1.4197592e-01] Sparsity at: 0.4915671942060086 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9181 - accuracy: 0.8935 - val_loss: 0.9003 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1589611e-02 1.7608888e-01 1.4180830e-01] Sparsity at: 0.4915671942060086 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9180 - accuracy: 0.8934 - val_loss: 0.9001 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1664469e-02 1.7469986e-01 1.4188744e-01] Sparsity at: 0.4915671942060086 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9178 - accuracy: 0.8934 - val_loss: 0.9001 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.1791795e-02 1.7320327e-01 1.4204505e-01] Sparsity at: 0.4915671942060086 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9176 - accuracy: 0.8937 - val_loss: 0.9001 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.2034558e-02 1.7148156e-01 1.4219946e-01] Sparsity at: 0.4915671942060086 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9178 - accuracy: 0.8932 - val_loss: 0.8999 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.2316854e-02 1.7012836e-01 1.4213602e-01] Sparsity at: 0.4915671942060086 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9176 - accuracy: 0.8935 - val_loss: 0.8997 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.2561373e-02 1.6851225e-01 1.4229941e-01] Sparsity at: 0.4915671942060086 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.0005102794590691739 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.00805874168673859 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.1238041130145664 Thresholhold -0.10793383419513702 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 56s 7ms/step - loss: 0.9175 - accuracy: 0.8935 - val_loss: 0.8999 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.2841696e-02 1.6711740e-01 1.4248542e-01] Sparsity at: 0.4915671942060086 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9173 - accuracy: 0.8934 - val_loss: 0.8995 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.3355966e-02 1.6539448e-01 1.4270639e-01] Sparsity at: 0.4915671942060086 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9173 - accuracy: 0.8938 - val_loss: 0.8997 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.3922671e-02 1.6419682e-01 1.4265877e-01] Sparsity at: 0.4915671942060086 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9172 - accuracy: 0.8935 - val_loss: 0.8994 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.4451721e-02 1.6248077e-01 1.4277595e-01] Sparsity at: 0.4915671942060086 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9172 - accuracy: 0.8936 - val_loss: 0.8992 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.5058696e-02 1.6076016e-01 1.4284347e-01] Sparsity at: 0.4915671942060086 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9172 - accuracy: 0.8938 - val_loss: 0.8992 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.5745422e-02 1.5912677e-01 1.4298648e-01] Sparsity at: 0.4915671942060086 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9170 - accuracy: 0.8938 - val_loss: 0.8991 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.6421098e-02 1.5728304e-01 1.4314385e-01] Sparsity at: 0.4915671942060086 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9169 - accuracy: 0.8936 - val_loss: 0.8990 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.7213555e-02 1.5579453e-01 1.4329554e-01] Sparsity at: 0.4915671942060086 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9169 - accuracy: 0.8939 - val_loss: 0.8993 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.7971216e-02 1.5399654e-01 1.4318551e-01] Sparsity at: 0.4915671942060086 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9168 - accuracy: 0.8938 - val_loss: 0.8992 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.8614761e-02 1.5222545e-01 1.4332433e-01] Sparsity at: 0.4915671942060086 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9167 - accuracy: 0.8940 - val_loss: 0.8990 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 2.9336343e-02 1.5033232e-01 1.4333005e-01] Sparsity at: 0.4915671942060086 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9168 - accuracy: 0.8938 - val_loss: 0.8990 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.0037440e-02 1.4820041e-01 1.4336869e-01] Sparsity at: 0.4915671942060086 Epoch 63/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9167 - accuracy: 0.8940 - val_loss: 0.8991 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.0450746e-02 1.4645797e-01 1.4333220e-01] Sparsity at: 0.4915671942060086 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9166 - accuracy: 0.8937 - val_loss: 0.8987 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.0955391e-02 1.4440919e-01 1.4345954e-01] Sparsity at: 0.4915671942060086 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9166 - accuracy: 0.8938 - val_loss: 0.8989 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.1472702e-02 1.4255746e-01 1.4328423e-01] Sparsity at: 0.4915671942060086 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9166 - accuracy: 0.8937 - val_loss: 0.8990 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.1696469e-02 1.4054219e-01 1.4322244e-01] Sparsity at: 0.4915671942060086 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9165 - accuracy: 0.8942 - val_loss: 0.8989 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.1921782e-02 1.3864966e-01 1.4319298e-01] Sparsity at: 0.4915671942060086 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9165 - accuracy: 0.8939 - val_loss: 0.8987 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.2319475e-02 1.3678779e-01 1.4280958e-01] Sparsity at: 0.4915671942060086 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9165 - accuracy: 0.8940 - val_loss: 0.8985 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.2560021e-02 1.3485317e-01 1.4266312e-01] Sparsity at: 0.4915671942060086 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9163 - accuracy: 0.8942 - val_loss: 0.8987 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.2889519e-02 1.3281493e-01 1.4248903e-01] Sparsity at: 0.4915671942060086 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9163 - accuracy: 0.8942 - val_loss: 0.8984 - val_accuracy: 0.9000 [ 4.405955e-34 0.000000e+00 -5.467231e-34 ... 3.315715e-02 1.309211e-01 1.422019e-01] Sparsity at: 0.4915671942060086 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9164 - accuracy: 0.8941 - val_loss: 0.8985 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.3424366e-02 1.2922679e-01 1.4211817e-01] Sparsity at: 0.4915671942060086 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9163 - accuracy: 0.8942 - val_loss: 0.8987 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.3670563e-02 1.2736526e-01 1.4198361e-01] Sparsity at: 0.4915671942060086 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9162 - accuracy: 0.8943 - val_loss: 0.8983 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.4102872e-02 1.2522779e-01 1.4181899e-01] Sparsity at: 0.4915671942060086 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9161 - accuracy: 0.8943 - val_loss: 0.8984 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.4536675e-02 1.2339666e-01 1.4156477e-01] Sparsity at: 0.4915671942060086 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9161 - accuracy: 0.8940 - val_loss: 0.8986 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.5162527e-02 1.2148662e-01 1.4147080e-01] Sparsity at: 0.4915671942060086 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9162 - accuracy: 0.8942 - val_loss: 0.8981 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.5776358e-02 1.1968360e-01 1.4125264e-01] Sparsity at: 0.4915671942060086 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8942 - val_loss: 0.8980 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.6509026e-02 1.1783002e-01 1.4099756e-01] Sparsity at: 0.4915671942060086 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8940 - val_loss: 0.8984 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 3.7313934e-02 1.1603254e-01 1.4076975e-01] Sparsity at: 0.4915671942060086 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8938 - val_loss: 0.8984 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 3.83617580e-02 1.14589624e-01 1.40581444e-01] Sparsity at: 0.4915671942060086 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9160 - accuracy: 0.8940 - val_loss: 0.8983 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 3.92619260e-02 1.12972766e-01 1.40384123e-01] Sparsity at: 0.4915671942060086 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9161 - accuracy: 0.8940 - val_loss: 0.8980 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.0344629e-02 1.1153361e-01 1.3995382e-01] Sparsity at: 0.4915671942060086 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9158 - accuracy: 0.8943 - val_loss: 0.8981 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.1570574e-02 1.1008904e-01 1.3963601e-01] Sparsity at: 0.4915671942060086 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9160 - accuracy: 0.8938 - val_loss: 0.8982 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.2847008e-02 1.0862957e-01 1.3937561e-01] Sparsity at: 0.4915671942060086 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9159 - accuracy: 0.8939 - val_loss: 0.8981 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.4043295e-02 1.0718821e-01 1.3918826e-01] Sparsity at: 0.4915671942060086 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8941 - val_loss: 0.8981 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.5151532e-02 1.0569327e-01 1.3914025e-01] Sparsity at: 0.4915671942060086 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8941 - val_loss: 0.8981 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.6313912e-02 1.0428562e-01 1.3902450e-01] Sparsity at: 0.4915671942060086 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8942 - val_loss: 0.8980 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.7505684e-02 1.0255472e-01 1.3881934e-01] Sparsity at: 0.4915671942060086 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8940 - val_loss: 0.8979 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.8411474e-02 1.0088680e-01 1.3875610e-01] Sparsity at: 0.4915671942060086 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9157 - accuracy: 0.8942 - val_loss: 0.8981 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 4.9720909e-02 9.9165075e-02 1.3855000e-01] Sparsity at: 0.4915671942060086 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9158 - accuracy: 0.8942 - val_loss: 0.8980 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.0411943e-02 9.7206965e-02 1.3853054e-01] Sparsity at: 0.4915671942060086 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9157 - accuracy: 0.8940 - val_loss: 0.8979 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.1577576e-02 9.5272563e-02 1.3853586e-01] Sparsity at: 0.4915671942060086 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9157 - accuracy: 0.8942 - val_loss: 0.8981 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2476063e-02 9.3192004e-02 1.3842435e-01] Sparsity at: 0.4915671942060086 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9155 - accuracy: 0.8942 - val_loss: 0.8979 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3037953e-02 9.0897918e-02 1.3851030e-01] Sparsity at: 0.4915671942060086 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9155 - accuracy: 0.8944 - val_loss: 0.8977 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3738654e-02 8.8585638e-02 1.3832237e-01] Sparsity at: 0.4915671942060086 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9156 - accuracy: 0.8942 - val_loss: 0.8978 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4518390e-02 8.5926108e-02 1.3840148e-01] Sparsity at: 0.4915671942060086 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9155 - accuracy: 0.8940 - val_loss: 0.8978 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5047587e-02 8.3290830e-02 1.3842909e-01] Sparsity at: 0.4915671942060086 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9154 - accuracy: 0.8943 - val_loss: 0.8978 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5457834e-02 8.0594443e-02 1.3855600e-01] Sparsity at: 0.4915671942060086 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9153 - accuracy: 0.8943 - val_loss: 0.8976 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5932533e-02 7.7634662e-02 1.3872516e-01] Sparsity at: 0.4915671942060086 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9152 - accuracy: 0.8941 - val_loss: 0.8977 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6139152e-02 7.4969448e-02 1.3880105e-01] Sparsity at: 0.4915671942060086 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.003496363039474343 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.026177641198420032 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.14994421318460383 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 64s 7ms/step - loss: 0.9154 - accuracy: 0.8942 - val_loss: 0.8976 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6228217e-02 7.2127163e-02 1.3891976e-01] Sparsity at: 0.4915671942060086 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9153 - accuracy: 0.8941 - val_loss: 0.8978 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6473296e-02 6.9552585e-02 1.3908984e-01] Sparsity at: 0.4915671942060086 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9153 - accuracy: 0.8939 - val_loss: 0.8974 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6642517e-02 6.6670500e-02 1.3917950e-01] Sparsity at: 0.4915671942060086 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9150 - accuracy: 0.8945 - val_loss: 0.8974 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6675714e-02 6.4042822e-02 1.3935557e-01] Sparsity at: 0.4915671942060086 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9151 - accuracy: 0.8941 - val_loss: 0.8974 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6579717e-02 6.1519664e-02 1.3959299e-01] Sparsity at: 0.4915671942060086 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9151 - accuracy: 0.8943 - val_loss: 0.8974 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6554638e-02 5.9072301e-02 1.3962172e-01] Sparsity at: 0.4915671942060086 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8942 - val_loss: 0.8974 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6636218e-02 5.6812480e-02 1.3992283e-01] Sparsity at: 0.4915671942060086 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9150 - accuracy: 0.8942 - val_loss: 0.8976 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6323677e-02 5.4395251e-02 1.4008930e-01] Sparsity at: 0.4915671942060086 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8945 - val_loss: 0.8977 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6222729e-02 5.1991522e-02 1.4038815e-01] Sparsity at: 0.4915671942060086 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8943 - val_loss: 0.8973 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6267820e-02 4.9938705e-02 1.4042601e-01] Sparsity at: 0.4915671942060086 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8942 - val_loss: 0.8974 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6054946e-02 4.7927070e-02 1.4041717e-01] Sparsity at: 0.4915671942060086 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9149 - accuracy: 0.8943 - val_loss: 0.8973 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5910639e-02 4.5790393e-02 1.4071932e-01] Sparsity at: 0.4915671942060086 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8944 - val_loss: 0.8974 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5817746e-02 4.3888167e-02 1.4085016e-01] Sparsity at: 0.4915671942060086 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9148 - accuracy: 0.8941 - val_loss: 0.8973 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5553839e-02 4.2069864e-02 1.4092915e-01] Sparsity at: 0.4915671942060086 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9148 - accuracy: 0.8946 - val_loss: 0.8972 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5578399e-02 4.0228777e-02 1.4111276e-01] Sparsity at: 0.4915671942060086 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9148 - accuracy: 0.8943 - val_loss: 0.8974 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5304684e-02 3.8610008e-02 1.4145373e-01] Sparsity at: 0.4915671942060086 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9148 - accuracy: 0.8942 - val_loss: 0.8973 - val_accuracy: 0.9007 [ 4.405955e-34 0.000000e+00 -5.467231e-34 ... 5.525994e-02 3.685068e-02 1.415453e-01] Sparsity at: 0.4915671942060086 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9148 - accuracy: 0.8944 - val_loss: 0.8973 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5075899e-02 3.5304777e-02 1.4175044e-01] Sparsity at: 0.4915671942060086 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8942 - val_loss: 0.8975 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4805469e-02 3.3861585e-02 1.4188705e-01] Sparsity at: 0.4915671942060086 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9149 - accuracy: 0.8942 - val_loss: 0.8974 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4606687e-02 3.2525089e-02 1.4203407e-01] Sparsity at: 0.4915671942060086 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8945 - val_loss: 0.8973 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4283284e-02 3.0963762e-02 1.4215362e-01] Sparsity at: 0.4915671942060086 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8944 - val_loss: 0.8974 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4055661e-02 2.9866846e-02 1.4221992e-01] Sparsity at: 0.4915671942060086 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9147 - accuracy: 0.8945 - val_loss: 0.8972 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3807549e-02 2.8449036e-02 1.4236511e-01] Sparsity at: 0.4915671942060086 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9147 - accuracy: 0.8946 - val_loss: 0.8971 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3616352e-02 2.7272563e-02 1.4242846e-01] Sparsity at: 0.4915671942060086 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8943 - val_loss: 0.8969 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3571083e-02 2.6139388e-02 1.4234842e-01] Sparsity at: 0.4915671942060086 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9146 - accuracy: 0.8945 - val_loss: 0.8973 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3282775e-02 2.5216475e-02 1.4247350e-01] Sparsity at: 0.4915671942060086 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8950 - val_loss: 0.8972 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3037070e-02 2.4307204e-02 1.4261262e-01] Sparsity at: 0.4915671942060086 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9147 - accuracy: 0.8946 - val_loss: 0.8973 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2930929e-02 2.3172643e-02 1.4243282e-01] Sparsity at: 0.4915671942060086 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8947 - val_loss: 0.8971 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2724473e-02 2.2376856e-02 1.4244777e-01] Sparsity at: 0.4915671942060086 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8945 - val_loss: 0.8973 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2686278e-02 2.1544902e-02 1.4248012e-01] Sparsity at: 0.4915671942060086 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8944 - val_loss: 0.8971 - val_accuracy: 0.9015 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2656170e-02 2.0726932e-02 1.4237295e-01] Sparsity at: 0.4915671942060086 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9145 - accuracy: 0.8948 - val_loss: 0.8972 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2697714e-02 2.0059209e-02 1.4218418e-01] Sparsity at: 0.4915671942060086 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8946 - val_loss: 0.8973 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2635152e-02 1.9424204e-02 1.4221667e-01] Sparsity at: 0.4915671942060086 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9146 - accuracy: 0.8943 - val_loss: 0.8971 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2809089e-02 1.8891726e-02 1.4193559e-01] Sparsity at: 0.4915671942060086 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8946 - val_loss: 0.8972 - val_accuracy: 0.9019 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2871533e-02 1.8544432e-02 1.4174108e-01] Sparsity at: 0.4915671942060086 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9144 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.2870534e-02 1.7880075e-02 1.4165451e-01] Sparsity at: 0.4915671942060086 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9145 - accuracy: 0.8947 - val_loss: 0.8973 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3061351e-02 1.7460840e-02 1.4147684e-01] Sparsity at: 0.4915671942060086 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8969 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3087540e-02 1.7038411e-02 1.4126882e-01] Sparsity at: 0.4915671942060086 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8949 - val_loss: 0.8972 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3198859e-02 1.6827943e-02 1.4101893e-01] Sparsity at: 0.4915671942060086 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9016 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3431567e-02 1.6531272e-02 1.4078459e-01] Sparsity at: 0.4915671942060086 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9144 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3468995e-02 1.6275905e-02 1.4055875e-01] Sparsity at: 0.4915671942060086 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9143 - accuracy: 0.8949 - val_loss: 0.8970 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3741660e-02 1.6146135e-02 1.4027785e-01] Sparsity at: 0.4915671942060086 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9142 - accuracy: 0.8948 - val_loss: 0.8972 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.3963501e-02 1.6009208e-02 1.3991924e-01] Sparsity at: 0.4915671942060086 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9144 - accuracy: 0.8948 - val_loss: 0.8971 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4272965e-02 1.5897870e-02 1.3955982e-01] Sparsity at: 0.4915671942060086 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4359101e-02 1.6110793e-02 1.3910906e-01] Sparsity at: 0.4915671942060086 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9143 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4433070e-02 1.6081057e-02 1.3880812e-01] Sparsity at: 0.4915671942060086 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8950 - val_loss: 0.8968 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.4956954e-02 1.6246514e-02 1.3822816e-01] Sparsity at: 0.4915671942060086 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8949 - val_loss: 0.8971 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5231649e-02 1.6460659e-02 1.3785164e-01] Sparsity at: 0.4915671942060086 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8948 - val_loss: 0.8972 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5452947e-02 1.6793819e-02 1.3730325e-01] Sparsity at: 0.4915671942060086 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9142 - accuracy: 0.8949 - val_loss: 0.8971 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.5724651e-02 1.7057544e-02 1.3676870e-01] Sparsity at: 0.4915671942060086 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.011272059238444654 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.047124729318109404 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.17600121149900705 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 62s 7ms/step - loss: 0.9141 - accuracy: 0.8947 - val_loss: 0.8968 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6054145e-02 1.7686464e-02 1.3615209e-01] Sparsity at: 0.4915671942060086 Epoch 152/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9142 - accuracy: 0.8947 - val_loss: 0.8972 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6345563e-02 1.8396201e-02 1.3549916e-01] Sparsity at: 0.4915671942060086 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9141 - accuracy: 0.8947 - val_loss: 0.8968 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.6786802e-02 1.8911283e-02 1.3490120e-01] Sparsity at: 0.4915671942060086 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9140 - accuracy: 0.8948 - val_loss: 0.8970 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.7108004e-02 1.9623943e-02 1.3436319e-01] Sparsity at: 0.4915671942060086 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9141 - accuracy: 0.8947 - val_loss: 0.8970 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.7645764e-02 2.0516187e-02 1.3354596e-01] Sparsity at: 0.4915671942060086 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.8133692e-02 2.1392504e-02 1.3299420e-01] Sparsity at: 0.4915671942060086 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9141 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.8592647e-02 2.2362266e-02 1.3245267e-01] Sparsity at: 0.4915671942060086 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8969 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.9214044e-02 2.3606356e-02 1.3185970e-01] Sparsity at: 0.4915671942060086 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 5.9788074e-02 2.4953702e-02 1.3130470e-01] Sparsity at: 0.4915671942060086 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8967 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.0508434e-02 2.6316468e-02 1.3071729e-01] Sparsity at: 0.4915671942060086 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8947 - val_loss: 0.8970 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.1161157e-02 2.7862763e-02 1.3014981e-01] Sparsity at: 0.4915671942060086 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.1715826e-02 2.9348249e-02 1.2966530e-01] Sparsity at: 0.4915671942060086 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8949 - val_loss: 0.8969 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.2499858e-02 3.1057177e-02 1.2923661e-01] Sparsity at: 0.4915671942060086 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8947 - val_loss: 0.8967 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.3296326e-02 3.2548439e-02 1.2889753e-01] Sparsity at: 0.4915671942060086 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8947 - val_loss: 0.8966 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.4017601e-02 3.4157705e-02 1.2861647e-01] Sparsity at: 0.4915671942060086 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9139 - accuracy: 0.8950 - val_loss: 0.8969 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.4807944e-02 3.5969306e-02 1.2825690e-01] Sparsity at: 0.4915671942060086 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9139 - accuracy: 0.8948 - val_loss: 0.8971 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.5431245e-02 3.7737332e-02 1.2796830e-01] Sparsity at: 0.4915671942060086 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9140 - accuracy: 0.8947 - val_loss: 0.8969 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.6317514e-02 3.9380427e-02 1.2777643e-01] Sparsity at: 0.4915671942060086 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8949 - val_loss: 0.8968 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.7055143e-02 4.1161571e-02 1.2774237e-01] Sparsity at: 0.4915671942060086 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8951 - val_loss: 0.8965 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.7728989e-02 4.2971790e-02 1.2749957e-01] Sparsity at: 0.4915671942060086 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9139 - accuracy: 0.8948 - val_loss: 0.8969 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.8571813e-02 4.4474203e-02 1.2742308e-01] Sparsity at: 0.4915671942060086 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8950 - val_loss: 0.8967 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 6.9410764e-02 4.6099026e-02 1.2742662e-01] Sparsity at: 0.4915671942060086 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8950 - val_loss: 0.8968 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.0111036e-02 4.7776178e-02 1.2732854e-01] Sparsity at: 0.4915671942060086 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9137 - accuracy: 0.8949 - val_loss: 0.8966 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.0877142e-02 4.9108785e-02 1.2724498e-01] Sparsity at: 0.4915671942060086 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8952 - val_loss: 0.8963 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.1631581e-02 5.0725736e-02 1.2713771e-01] Sparsity at: 0.4915671942060086 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9138 - accuracy: 0.8949 - val_loss: 0.8966 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.2293870e-02 5.2178476e-02 1.2712021e-01] Sparsity at: 0.4915671942060086 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8949 - val_loss: 0.8964 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.3063195e-02 5.3607959e-02 1.2722716e-01] Sparsity at: 0.4915671942060086 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9138 - accuracy: 0.8947 - val_loss: 0.8964 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.3784754e-02 5.4916345e-02 1.2724178e-01] Sparsity at: 0.4915671942060086 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9137 - accuracy: 0.8951 - val_loss: 0.8965 - val_accuracy: 0.9005 [ 4.405955e-34 0.000000e+00 -5.467231e-34 ... 7.458435e-02 5.620430e-02 1.273499e-01] Sparsity at: 0.4915671942060086 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8964 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.5433187e-02 5.7797886e-02 1.2731324e-01] Sparsity at: 0.4915671942060086 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8949 - val_loss: 0.8964 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.6093622e-02 5.8917698e-02 1.2761958e-01] Sparsity at: 0.4915671942060086 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8949 - val_loss: 0.8966 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.6895744e-02 6.0469348e-02 1.2761772e-01] Sparsity at: 0.4915671942060086 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8964 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.7709392e-02 6.1416328e-02 1.2761122e-01] Sparsity at: 0.4915671942060086 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8949 - val_loss: 0.8963 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.8367203e-02 6.2364485e-02 1.2780271e-01] Sparsity at: 0.4915671942060086 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8949 - val_loss: 0.8965 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 7.9302244e-02 6.3584983e-02 1.2775308e-01] Sparsity at: 0.4915671942060086 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8963 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.0140516e-02 6.4463928e-02 1.2790546e-01] Sparsity at: 0.4915671942060086 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9135 - accuracy: 0.8949 - val_loss: 0.8963 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.0962680e-02 6.5393746e-02 1.2793745e-01] Sparsity at: 0.4915671942060086 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9136 - accuracy: 0.8946 - val_loss: 0.8964 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.1871778e-02 6.6387676e-02 1.2778683e-01] Sparsity at: 0.4915671942060086 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9135 - accuracy: 0.8948 - val_loss: 0.8964 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.2790025e-02 6.7185014e-02 1.2786651e-01] Sparsity at: 0.4915671942060086 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9134 - accuracy: 0.8948 - val_loss: 0.8961 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.3899997e-02 6.7903578e-02 1.2774087e-01] Sparsity at: 0.4915671942060086 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9134 - accuracy: 0.8946 - val_loss: 0.8961 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.4890626e-02 6.8697125e-02 1.2766592e-01] Sparsity at: 0.4915671942060086 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9134 - accuracy: 0.8947 - val_loss: 0.8960 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.5895844e-02 6.9202006e-02 1.2756491e-01] Sparsity at: 0.4915671942060086 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9133 - accuracy: 0.8946 - val_loss: 0.8960 - val_accuracy: 0.9011 [ 4.405955e-34 0.000000e+00 -5.467231e-34 ... 8.709497e-02 6.971825e-02 1.274270e-01] Sparsity at: 0.4915671942060086 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9133 - accuracy: 0.8948 - val_loss: 0.8962 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.8182107e-02 7.0219204e-02 1.2749338e-01] Sparsity at: 0.4915671942060086 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9132 - accuracy: 0.8946 - val_loss: 0.8960 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 8.9228794e-02 7.0718624e-02 1.2731257e-01] Sparsity at: 0.4915671942060086 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9133 - accuracy: 0.8947 - val_loss: 0.8959 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.0439849e-02 7.0813298e-02 1.2718016e-01] Sparsity at: 0.4915671942060086 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9131 - accuracy: 0.8949 - val_loss: 0.8958 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.1443099e-02 7.1120374e-02 1.2709697e-01] Sparsity at: 0.4915671942060086 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9132 - accuracy: 0.8946 - val_loss: 0.8959 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.2552729e-02 7.1526386e-02 1.2699056e-01] Sparsity at: 0.4915671942060086 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8951 - val_loss: 0.8960 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.3717016e-02 7.1717285e-02 1.2683336e-01] Sparsity at: 0.4915671942060086 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9132 - accuracy: 0.8948 - val_loss: 0.8959 - val_accuracy: 0.9008 [ 4.405955e-34 0.000000e+00 -5.467231e-34 ... 9.467285e-02 7.201058e-02 1.266109e-01] Sparsity at: 0.4915671942060086 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.02142395043733636 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.07333939304789183 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.20022020719093092 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 65s 7ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.5643237e-02 7.2388552e-02 1.2626694e-01] Sparsity at: 0.4915671942060086 Epoch 202/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9131 - accuracy: 0.8948 - val_loss: 0.8960 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.6175291e-02 7.2803080e-02 1.2633914e-01] Sparsity at: 0.4915671942060086 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9131 - accuracy: 0.8947 - val_loss: 0.8959 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.6814089e-02 7.3377401e-02 1.2611035e-01] Sparsity at: 0.4915671942060086 Epoch 204/500 235/235 [==============================] - 2s 10ms/step - loss: 0.9130 - accuracy: 0.8946 - val_loss: 0.8960 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.7325146e-02 7.3983535e-02 1.2609383e-01] Sparsity at: 0.4915671942060086 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.8106608e-02 7.4429415e-02 1.2567812e-01] Sparsity at: 0.4915671942060086 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9012 [ 4.405955e-34 0.000000e+00 -5.467231e-34 ... 9.853930e-02 7.494602e-02 1.254700e-01] Sparsity at: 0.4915671942060086 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9016 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.8895095e-02 7.5431503e-02 1.2535639e-01] Sparsity at: 0.4915671942060086 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.9103086e-02 7.5900957e-02 1.2523769e-01] Sparsity at: 0.4915671942060086 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.9522829e-02 7.6486997e-02 1.2509894e-01] Sparsity at: 0.4915671942060086 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 9.9636301e-02 7.6907367e-02 1.2499323e-01] Sparsity at: 0.4915671942060086 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9130 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9016 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 9.98834074e-02 7.73103684e-02 1.24766596e-01] Sparsity at: 0.4915671942060086 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9013 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 9.99359787e-02 7.76677951e-02 1.24696344e-01] Sparsity at: 0.4915671942060086 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9129 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0002642e-01 7.8020290e-02 1.2447584e-01] Sparsity at: 0.4915671942060086 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8958 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0027917e-01 7.8364708e-02 1.2443490e-01] Sparsity at: 0.4915671942060086 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8957 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.00300886e-01 7.87917227e-02 1.24289595e-01] Sparsity at: 0.4915671942060086 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9015 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0065176e-01 7.9192631e-02 1.2408627e-01] Sparsity at: 0.4915671942060086 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.00588962e-01 7.93458298e-02 1.24147326e-01] Sparsity at: 0.4915671942060086 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9015 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0052017e-01 7.9670958e-02 1.2412000e-01] Sparsity at: 0.4915671942060086 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0066597e-01 8.0094285e-02 1.2397536e-01] Sparsity at: 0.4915671942060086 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.00808665e-01 8.05042386e-02 1.23935439e-01] Sparsity at: 0.4915671942060086 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9129 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9016 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0085063e-01 8.0734372e-02 1.2387672e-01] Sparsity at: 0.4915671942060086 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9011 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.00862525e-01 8.12308267e-02 1.23742156e-01] Sparsity at: 0.4915671942060086 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0105536e-01 8.1420235e-02 1.2378503e-01] Sparsity at: 0.4915671942060086 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8945 - val_loss: 0.8956 - val_accuracy: 0.9016 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0093520e-01 8.1815019e-02 1.2359646e-01] Sparsity at: 0.4915671942060086 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0098380e-01 8.2336642e-02 1.2359604e-01] Sparsity at: 0.4915671942060086 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0101691e-01 8.2467802e-02 1.2371648e-01] Sparsity at: 0.4915671942060086 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9012 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01043351e-01 8.28581974e-02 1.23542406e-01] Sparsity at: 0.4915671942060086 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0108567e-01 8.3382219e-02 1.2341244e-01] Sparsity at: 0.4915671942060086 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8945 - val_loss: 0.8953 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0112931e-01 8.3609983e-02 1.2342407e-01] Sparsity at: 0.4915671942060086 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0124653e-01 8.4036008e-02 1.2346753e-01] Sparsity at: 0.4915671942060086 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9015 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0099008e-01 8.4256522e-02 1.2343711e-01] Sparsity at: 0.4915671942060086 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0116735e-01 8.4691726e-02 1.2329346e-01] Sparsity at: 0.4915671942060086 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0120549e-01 8.5029110e-02 1.2320056e-01] Sparsity at: 0.4915671942060086 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9013 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01291597e-01 8.54547918e-02 1.23075925e-01] Sparsity at: 0.4915671942060086 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8954 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0128099e-01 8.5901454e-02 1.2312485e-01] Sparsity at: 0.4915671942060086 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9012 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01410605e-01 8.63594040e-02 1.23156317e-01] Sparsity at: 0.4915671942060086 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0127239e-01 8.6523212e-02 1.2310546e-01] Sparsity at: 0.4915671942060086 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8950 - val_loss: 0.8954 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0131819e-01 8.7081045e-02 1.2302668e-01] Sparsity at: 0.4915671942060086 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01451673e-01 8.74959975e-02 1.23043574e-01] Sparsity at: 0.4915671942060086 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9129 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9015 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0131886e-01 8.7783560e-02 1.2300963e-01] Sparsity at: 0.4915671942060086 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0142950e-01 8.8123880e-02 1.2293459e-01] Sparsity at: 0.4915671942060086 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8954 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0151721e-01 8.8582717e-02 1.2286842e-01] Sparsity at: 0.4915671942060086 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9011 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01410329e-01 8.89054686e-02 1.22809015e-01] Sparsity at: 0.4915671942060086 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01508334e-01 8.91791210e-02 1.22680791e-01] Sparsity at: 0.4915671942060086 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9011 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01475626e-01 8.96460265e-02 1.22691609e-01] Sparsity at: 0.4915671942060086 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0157552e-01 8.9875020e-02 1.2268874e-01] Sparsity at: 0.4915671942060086 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0180477e-01 9.0367652e-02 1.2257202e-01] Sparsity at: 0.4915671942060086 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8944 - val_loss: 0.8957 - val_accuracy: 0.9015 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01682864e-01 9.07195807e-02 1.22651353e-01] Sparsity at: 0.4915671942060086 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8958 - val_accuracy: 0.9011 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01685569e-01 9.11051631e-02 1.22737736e-01] Sparsity at: 0.4915671942060086 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.01910874e-01 9.16255713e-02 1.22500703e-01] Sparsity at: 0.4915671942060086 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.03567400540859422 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.10840489712948642 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.2288291585369837 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 62s 7ms/step - loss: 0.9129 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0193768e-01 9.1981292e-02 1.2230841e-01] Sparsity at: 0.4915671942060086 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0206948e-01 9.2079706e-02 1.2229652e-01] Sparsity at: 0.4915671942060086 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9013 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.02129921e-01 9.26473439e-02 1.22345895e-01] Sparsity at: 0.4915671942060086 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0225308e-01 9.2909552e-02 1.2225984e-01] Sparsity at: 0.4915671942060086 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9130 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0231085e-01 9.3630522e-02 1.2209938e-01] Sparsity at: 0.4915671942060086 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.02329940e-01 9.38648432e-02 1.22281715e-01] Sparsity at: 0.4915671942060086 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0247041e-01 9.4468139e-02 1.2212482e-01] Sparsity at: 0.4915671942060086 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0255352e-01 9.4962858e-02 1.2207687e-01] Sparsity at: 0.4915671942060086 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9014 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0262582e-01 9.5325835e-02 1.2199159e-01] Sparsity at: 0.4915671942060086 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8953 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0286401e-01 9.5675878e-02 1.2184068e-01] Sparsity at: 0.4915671942060086 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9012 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.02975592e-01 9.63489413e-02 1.21847324e-01] Sparsity at: 0.4915671942060086 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0299555e-01 9.6655533e-02 1.2181367e-01] Sparsity at: 0.4915671942060086 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9012 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0316072e-01 9.7041480e-02 1.2177449e-01] Sparsity at: 0.4915671942060086 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0326288e-01 9.7469062e-02 1.2172752e-01] Sparsity at: 0.4915671942060086 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0324152e-01 9.7889498e-02 1.2173858e-01] Sparsity at: 0.4915671942060086 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.03435636e-01 9.84170362e-02 1.21615730e-01] Sparsity at: 0.4915671942060086 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0370015e-01 9.8744400e-02 1.2147219e-01] Sparsity at: 0.4915671942060086 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8958 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.03738651e-01 9.92396399e-02 1.21449396e-01] Sparsity at: 0.4915671942060086 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0385159e-01 9.9807292e-02 1.2134983e-01] Sparsity at: 0.4915671942060086 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8957 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.04043342e-01 1.00297436e-01 1.21235147e-01] Sparsity at: 0.4915671942060086 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.04139872e-01 1.00725181e-01 1.21209964e-01] Sparsity at: 0.4915671942060086 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9011 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.04165480e-01 1.01247355e-01 1.21191636e-01] Sparsity at: 0.4915671942060086 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0432126e-01 1.0163329e-01 1.2100831e-01] Sparsity at: 0.4915671942060086 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0444409e-01 1.0228456e-01 1.2087474e-01] Sparsity at: 0.4915671942060086 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0457735e-01 1.0251811e-01 1.2098795e-01] Sparsity at: 0.4915671942060086 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0482736e-01 1.0305520e-01 1.2075957e-01] Sparsity at: 0.4915671942060086 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.04805402e-01 1.03636585e-01 1.20822832e-01] Sparsity at: 0.4915671942060086 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.05027564e-01 1.04056209e-01 1.20698892e-01] Sparsity at: 0.4915671942060086 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0508188e-01 1.0448680e-01 1.2060012e-01] Sparsity at: 0.4915671942060086 Epoch 280/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8958 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0514281e-01 1.0482825e-01 1.2069760e-01] Sparsity at: 0.4915671942060086 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.05385736e-01 1.05611682e-01 1.20322876e-01] Sparsity at: 0.4915671942060086 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9012 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.05653800e-01 1.06095769e-01 1.20253384e-01] Sparsity at: 0.4915671942060086 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0561259e-01 1.0639681e-01 1.2029745e-01] Sparsity at: 0.4915671942060086 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8953 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.05671056e-01 1.07204944e-01 1.20195039e-01] Sparsity at: 0.4915671942060086 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.05923966e-01 1.07656084e-01 1.20007195e-01] Sparsity at: 0.4915671942060086 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0596972e-01 1.0785523e-01 1.2013594e-01] Sparsity at: 0.4915671942060086 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0609634e-01 1.0846401e-01 1.1990883e-01] Sparsity at: 0.4915671942060086 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.06263541e-01 1.09087124e-01 1.19762152e-01] Sparsity at: 0.4915671942060086 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9128 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0631212e-01 1.0936940e-01 1.1964775e-01] Sparsity at: 0.4915671942060086 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.06415585e-01 1.09851293e-01 1.19658023e-01] Sparsity at: 0.4915671942060086 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.06537998e-01 1.10336587e-01 1.19548656e-01] Sparsity at: 0.4915671942060086 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.06684104e-01 1.10930219e-01 1.19353995e-01] Sparsity at: 0.4915671942060086 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0679175e-01 1.1107905e-01 1.1925575e-01] Sparsity at: 0.4915671942060086 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.06935956e-01 1.11754924e-01 1.19206458e-01] Sparsity at: 0.4915671942060086 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0706040e-01 1.1210678e-01 1.1920955e-01] Sparsity at: 0.4915671942060086 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07080966e-01 1.12800702e-01 1.19147345e-01] Sparsity at: 0.4915671942060086 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07189715e-01 1.13190606e-01 1.19042031e-01] Sparsity at: 0.4915671942060086 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8957 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0742079e-01 1.1358772e-01 1.1894304e-01] Sparsity at: 0.4915671942060086 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0737643e-01 1.1401457e-01 1.1899469e-01] Sparsity at: 0.4915671942060086 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8950 - val_loss: 0.8956 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0752595e-01 1.1449818e-01 1.1894162e-01] Sparsity at: 0.4915671942060086 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.051622290096579704 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.147530256526327 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.2550637599594445 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 60s 7ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8954 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07758895e-01 1.15061432e-01 1.18676923e-01] Sparsity at: 0.4915671942060086 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9127 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07817464e-01 1.15632489e-01 1.18681155e-01] Sparsity at: 0.4915671942060086 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0803751e-01 1.1613247e-01 1.1854644e-01] Sparsity at: 0.4915671942060086 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07999966e-01 1.16644174e-01 1.18464328e-01] Sparsity at: 0.4915671942060086 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08318292e-01 1.17018491e-01 1.18299626e-01] Sparsity at: 0.4915671942060086 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0838743e-01 1.1762427e-01 1.1830912e-01] Sparsity at: 0.4915671942060086 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0837681e-01 1.1816705e-01 1.1830547e-01] Sparsity at: 0.4915671942060086 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0871011e-01 1.1861624e-01 1.1824350e-01] Sparsity at: 0.4915671942060086 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08843051e-01 1.19321309e-01 1.18006304e-01] Sparsity at: 0.4915671942060086 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08809650e-01 1.19755886e-01 1.17996745e-01] Sparsity at: 0.4915671942060086 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8956 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0885896e-01 1.2025493e-01 1.1791449e-01] Sparsity at: 0.4915671942060086 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09169096e-01 1.20747305e-01 1.17802642e-01] Sparsity at: 0.4915671942060086 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09158628e-01 1.21064760e-01 1.17603354e-01] Sparsity at: 0.4915671942060086 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09486565e-01 1.21527143e-01 1.17681272e-01] Sparsity at: 0.4915671942060086 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0946308e-01 1.2224540e-01 1.1752497e-01] Sparsity at: 0.4915671942060086 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8958 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09518081e-01 1.22784026e-01 1.17358431e-01] Sparsity at: 0.4915671942060086 Epoch 317/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9127 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09624304e-01 1.23014234e-01 1.17214233e-01] Sparsity at: 0.4915671942060086 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09744117e-01 1.23686805e-01 1.17059000e-01] Sparsity at: 0.4915671942060086 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9127 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0969191e-01 1.2420504e-01 1.1713395e-01] Sparsity at: 0.4915671942060086 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09801918e-01 1.24639109e-01 1.16956644e-01] Sparsity at: 0.4915671942060086 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1000070e-01 1.2523420e-01 1.1696241e-01] Sparsity at: 0.4915671942060086 Epoch 322/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8948 - val_loss: 0.8953 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1020854e-01 1.2572798e-01 1.1684162e-01] Sparsity at: 0.4915671942060086 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9124 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9013 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1023498e-01 1.2618518e-01 1.1676814e-01] Sparsity at: 0.4915671942060086 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8949 - val_loss: 0.8953 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1037652e-01 1.2673737e-01 1.1659724e-01] Sparsity at: 0.4915671942060086 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8945 - val_loss: 0.8956 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1037357e-01 1.2729448e-01 1.1649353e-01] Sparsity at: 0.4915671942060086 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1064157e-01 1.2759124e-01 1.1649286e-01] Sparsity at: 0.4915671942060086 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8945 - val_loss: 0.8956 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10592104e-01 1.28157154e-01 1.16475947e-01] Sparsity at: 0.4915671942060086 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9010 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1068795e-01 1.2858152e-01 1.1633609e-01] Sparsity at: 0.4915671942060086 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8956 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1090289e-01 1.2913407e-01 1.1637783e-01] Sparsity at: 0.4915671942060086 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1086357e-01 1.2948421e-01 1.1623690e-01] Sparsity at: 0.4915671942060086 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10820495e-01 1.29991978e-01 1.16205022e-01] Sparsity at: 0.4915671942060086 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10960357e-01 1.30195558e-01 1.15987465e-01] Sparsity at: 0.4915671942060086 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8945 - val_loss: 0.8957 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1086346e-01 1.3063511e-01 1.1604190e-01] Sparsity at: 0.4915671942060086 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11039855e-01 1.31049052e-01 1.15849726e-01] Sparsity at: 0.4915671942060086 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8956 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11139596e-01 1.31488129e-01 1.15869932e-01] Sparsity at: 0.4915671942060086 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8953 - val_accuracy: 0.9009 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11132160e-01 1.31560966e-01 1.15874745e-01] Sparsity at: 0.4915671942060086 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1119732e-01 1.3194750e-01 1.1579317e-01] Sparsity at: 0.4915671942060086 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8947 - val_loss: 0.8955 - val_accuracy: 0.9011 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1132523e-01 1.3218549e-01 1.1586948e-01] Sparsity at: 0.4915671942060086 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1147601e-01 1.3257197e-01 1.1571090e-01] Sparsity at: 0.4915671942060086 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9124 - accuracy: 0.8948 - val_loss: 0.8957 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1138895e-01 1.3285929e-01 1.1575279e-01] Sparsity at: 0.4915671942060086 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8957 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1134855e-01 1.3309039e-01 1.1576089e-01] Sparsity at: 0.4915671942060086 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1156576e-01 1.3345060e-01 1.1556832e-01] Sparsity at: 0.4915671942060086 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1170391e-01 1.3358210e-01 1.1569683e-01] Sparsity at: 0.4915671942060086 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1187138e-01 1.3370660e-01 1.1573568e-01] Sparsity at: 0.4915671942060086 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9010 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11819856e-01 1.34035230e-01 1.15782276e-01] Sparsity at: 0.4915671942060086 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9124 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11917645e-01 1.34417862e-01 1.15639441e-01] Sparsity at: 0.4915671942060086 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8954 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1198719e-01 1.3452066e-01 1.1570860e-01] Sparsity at: 0.4915671942060086 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8956 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1218482e-01 1.3460460e-01 1.1563653e-01] Sparsity at: 0.4915671942060086 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9126 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12264901e-01 1.34759083e-01 1.15506575e-01] Sparsity at: 0.4915671942060086 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1247416e-01 1.3507682e-01 1.1557629e-01] Sparsity at: 0.4915671942060086 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.06464418657801829 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.17392093214396276 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.2729203704960028 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 61s 7ms/step - loss: 0.9126 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12512529e-01 1.35305703e-01 1.15505144e-01] Sparsity at: 0.4915671942060086 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9124 - accuracy: 0.8949 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1249515e-01 1.3555111e-01 1.1568865e-01] Sparsity at: 0.4915671942060086 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8955 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1251154e-01 1.3596642e-01 1.1554975e-01] Sparsity at: 0.4915671942060086 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8947 - val_loss: 0.8953 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11760825e-01 1.36150450e-01 1.15658097e-01] Sparsity at: 0.4915671942060086 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9125 - accuracy: 0.8948 - val_loss: 0.8953 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1092494e-01 1.3685745e-01 1.1561877e-01] Sparsity at: 0.4915671942060086 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9124 - accuracy: 0.8945 - val_loss: 0.8955 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1037842e-01 1.3755278e-01 1.1556570e-01] Sparsity at: 0.4915671942060086 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8947 - val_loss: 0.8952 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0954408e-01 1.3820693e-01 1.1617465e-01] Sparsity at: 0.4915671942060086 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8946 - val_loss: 0.8955 - val_accuracy: 0.9009 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0918721e-01 1.3900797e-01 1.1646126e-01] Sparsity at: 0.4915671942060086 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9124 - accuracy: 0.8945 - val_loss: 0.8953 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0886470e-01 1.3915515e-01 1.1706115e-01] Sparsity at: 0.4915671942060086 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8944 - val_loss: 0.8953 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0861417e-01 1.3923559e-01 1.1778208e-01] Sparsity at: 0.4915671942060086 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9123 - accuracy: 0.8949 - val_loss: 0.8954 - val_accuracy: 0.9003 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08452991e-01 1.39544755e-01 1.18494906e-01] Sparsity at: 0.4915671942060086 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8946 - val_loss: 0.8952 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08494103e-01 1.39522269e-01 1.19152054e-01] Sparsity at: 0.4915671942060086 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08533636e-01 1.39399260e-01 1.19898595e-01] Sparsity at: 0.4915671942060086 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8950 - val_loss: 0.8953 - val_accuracy: 0.9002 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08377568e-01 1.39133841e-01 1.20760426e-01] Sparsity at: 0.4915671942060086 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0847210e-01 1.3908175e-01 1.2122020e-01] Sparsity at: 0.4915671942060086 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9003 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08458422e-01 1.38681337e-01 1.21884786e-01] Sparsity at: 0.4915671942060086 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8948 - val_loss: 0.8951 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0864188e-01 1.3845906e-01 1.2266414e-01] Sparsity at: 0.4915671942060086 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0867066e-01 1.3789645e-01 1.2318372e-01] Sparsity at: 0.4915671942060086 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9008 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08703814e-01 1.37773827e-01 1.23803139e-01] Sparsity at: 0.4915671942060086 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9123 - accuracy: 0.8944 - val_loss: 0.8951 - val_accuracy: 0.9006 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0864734e-01 1.3748807e-01 1.2438256e-01] Sparsity at: 0.4915671942060086 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8948 - val_loss: 0.8950 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0881177e-01 1.3706860e-01 1.2507282e-01] Sparsity at: 0.4915671942060086 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0877953e-01 1.3662153e-01 1.2551926e-01] Sparsity at: 0.4915671942060086 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08853444e-01 1.36136845e-01 1.26017019e-01] Sparsity at: 0.4915671942060086 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0883986e-01 1.3574101e-01 1.2662289e-01] Sparsity at: 0.4915671942060086 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8948 - val_loss: 0.8952 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0883737e-01 1.3554654e-01 1.2709263e-01] Sparsity at: 0.4915671942060086 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8945 - val_loss: 0.8951 - val_accuracy: 0.9008 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0889638e-01 1.3493958e-01 1.2757799e-01] Sparsity at: 0.4915671942060086 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0902181e-01 1.3455978e-01 1.2791066e-01] Sparsity at: 0.4915671942060086 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8948 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0900174e-01 1.3396764e-01 1.2835996e-01] Sparsity at: 0.4915671942060086 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8951 - val_accuracy: 0.9007 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08929954e-01 1.33672997e-01 1.28733471e-01] Sparsity at: 0.4915671942060086 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8949 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0900377e-01 1.3313754e-01 1.2911968e-01] Sparsity at: 0.4915671942060086 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8943 - val_loss: 0.8949 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0926257e-01 1.3266259e-01 1.2954485e-01] Sparsity at: 0.4915671942060086 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0911893e-01 1.3231327e-01 1.2982981e-01] Sparsity at: 0.4915671942060086 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0913721e-01 1.3168851e-01 1.3027328e-01] Sparsity at: 0.4915671942060086 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08965725e-01 1.31370366e-01 1.30558357e-01] Sparsity at: 0.4915671942060086 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8947 - val_loss: 0.8948 - val_accuracy: 0.9007 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0908806e-01 1.3100716e-01 1.3083160e-01] Sparsity at: 0.4915671942060086 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8947 - val_loss: 0.8950 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09178655e-01 1.30139247e-01 1.31272107e-01] Sparsity at: 0.4915671942060086 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8948 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09355226e-01 1.29827857e-01 1.31579965e-01] Sparsity at: 0.4915671942060086 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8948 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0929737e-01 1.2937720e-01 1.3174893e-01] Sparsity at: 0.4915671942060086 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8948 - val_loss: 0.8951 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09133005e-01 1.29096195e-01 1.32233441e-01] Sparsity at: 0.4915671942060086 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8944 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0928974e-01 1.2864800e-01 1.3234875e-01] Sparsity at: 0.4915671942060086 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8943 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09321244e-01 1.28218085e-01 1.32692069e-01] Sparsity at: 0.4915671942060086 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8949 - val_loss: 0.8950 - val_accuracy: 0.9005 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0941839e-01 1.2815791e-01 1.3303331e-01] Sparsity at: 0.4915671942060086 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9005 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09406024e-01 1.27596900e-01 1.33089453e-01] Sparsity at: 0.4915671942060086 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8951 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0937726e-01 1.2706323e-01 1.3348898e-01] Sparsity at: 0.4915671942060086 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0944096e-01 1.2684163e-01 1.3357668e-01] Sparsity at: 0.4915671942060086 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9122 - accuracy: 0.8948 - val_loss: 0.8950 - val_accuracy: 0.9001 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09369345e-01 1.26328841e-01 1.33864865e-01] Sparsity at: 0.4915671942060086 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8952 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0933044e-01 1.2582973e-01 1.3420562e-01] Sparsity at: 0.4915671942060086 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0932508e-01 1.2561536e-01 1.3422437e-01] Sparsity at: 0.4915671942060086 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8949 - val_loss: 0.8948 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0955639e-01 1.2529141e-01 1.3427687e-01] Sparsity at: 0.4915671942060086 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8944 - val_loss: 0.8950 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0962675e-01 1.2508957e-01 1.3459891e-01] Sparsity at: 0.4915671942060086 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.07383941830103513 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.18945745993156926 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.28026493361681304 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 60s 7ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0950876e-01 1.2468032e-01 1.3476837e-01] Sparsity at: 0.4915671942060086 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8947 - val_accuracy: 0.9001 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09496504e-01 1.24136597e-01 1.35057911e-01] Sparsity at: 0.4915671942060086 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8948 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0942746e-01 1.2391216e-01 1.3511725e-01] Sparsity at: 0.4915671942060086 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0947028e-01 1.2344939e-01 1.3551536e-01] Sparsity at: 0.4915671942060086 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09548874e-01 1.23096377e-01 1.35503218e-01] Sparsity at: 0.4915671942060086 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0957327e-01 1.2285333e-01 1.3573907e-01] Sparsity at: 0.4915671942060086 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0974342e-01 1.2252984e-01 1.3586804e-01] Sparsity at: 0.4915671942060086 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8950 - val_accuracy: 0.9002 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09529182e-01 1.22167945e-01 1.36147320e-01] Sparsity at: 0.4915671942060086 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8950 - val_accuracy: 0.9001 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09413795e-01 1.21915489e-01 1.36264831e-01] Sparsity at: 0.4915671942060086 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8944 - val_loss: 0.8950 - val_accuracy: 0.9004 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0943275e-01 1.2133632e-01 1.3632475e-01] Sparsity at: 0.4915671942060086 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0952730e-01 1.2110749e-01 1.3671531e-01] Sparsity at: 0.4915671942060086 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8949 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0959575e-01 1.2089787e-01 1.3675423e-01] Sparsity at: 0.4915671942060086 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8945 - val_loss: 0.8951 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0964599e-01 1.2085825e-01 1.3678238e-01] Sparsity at: 0.4915671942060086 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.8996 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09560207e-01 1.20630205e-01 1.37018502e-01] Sparsity at: 0.4915671942060086 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8942 - val_loss: 0.8949 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0963194e-01 1.2019518e-01 1.3712516e-01] Sparsity at: 0.4915671942060086 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09601811e-01 1.19841814e-01 1.37319714e-01] Sparsity at: 0.4915671942060086 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8943 - val_loss: 0.8949 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09643474e-01 1.19666681e-01 1.37418300e-01] Sparsity at: 0.4915671942060086 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8946 - val_loss: 0.8950 - val_accuracy: 0.9003 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09552771e-01 1.19338654e-01 1.37683675e-01] Sparsity at: 0.4915671942060086 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.9006 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09576553e-01 1.19024254e-01 1.37618184e-01] Sparsity at: 0.4915671942060086 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8947 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0933009e-01 1.1889714e-01 1.3758585e-01] Sparsity at: 0.4915671942060086 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9121 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0902565e-01 1.1865483e-01 1.3757011e-01] Sparsity at: 0.4915671942060086 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.9003 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08818509e-01 1.18420124e-01 1.37709722e-01] Sparsity at: 0.4915671942060086 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8950 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0855886e-01 1.1823586e-01 1.3765867e-01] Sparsity at: 0.4915671942060086 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.9002 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08361080e-01 1.17880754e-01 1.37751326e-01] Sparsity at: 0.4915671942060086 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8947 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0831153e-01 1.1752823e-01 1.3778915e-01] Sparsity at: 0.4915671942060086 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8944 - val_loss: 0.8947 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0801212e-01 1.1744972e-01 1.3776982e-01] Sparsity at: 0.4915671942060086 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8949 - val_accuracy: 0.8998 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08038604e-01 1.17239520e-01 1.37915060e-01] Sparsity at: 0.4915671942060086 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8945 - val_loss: 0.8946 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0797276e-01 1.1678814e-01 1.3793471e-01] Sparsity at: 0.4915671942060086 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8942 - val_loss: 0.8947 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0784755e-01 1.1667929e-01 1.3808033e-01] Sparsity at: 0.4915671942060086 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8947 - val_accuracy: 0.8996 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07891709e-01 1.16176106e-01 1.38276950e-01] Sparsity at: 0.4915671942060086 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9120 - accuracy: 0.8941 - val_loss: 0.8947 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0777372e-01 1.1622778e-01 1.3832834e-01] Sparsity at: 0.4915671942060086 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8947 - val_loss: 0.8949 - val_accuracy: 0.8997 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.07734695e-01 1.15933731e-01 1.38471559e-01] Sparsity at: 0.4915671942060086 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8943 - val_loss: 0.8946 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0767491e-01 1.1549077e-01 1.3848276e-01] Sparsity at: 0.4915671942060086 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8944 - val_loss: 0.8946 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0776126e-01 1.1534305e-01 1.3877386e-01] Sparsity at: 0.4915671942060086 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8947 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0782883e-01 1.1485077e-01 1.3878444e-01] Sparsity at: 0.4915671942060086 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.9000 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0795109e-01 1.1452932e-01 1.3872072e-01] Sparsity at: 0.4915671942060086 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9120 - accuracy: 0.8943 - val_loss: 0.8943 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0794974e-01 1.1419043e-01 1.3889796e-01] Sparsity at: 0.4915671942060086 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0776338e-01 1.1398324e-01 1.3911685e-01] Sparsity at: 0.4915671942060086 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0796861e-01 1.1360106e-01 1.3928226e-01] Sparsity at: 0.4915671942060086 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.9002 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08137324e-01 1.13366283e-01 1.39259845e-01] Sparsity at: 0.4915671942060086 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8946 - val_loss: 0.8946 - val_accuracy: 0.8996 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08071759e-01 1.13258064e-01 1.39275387e-01] Sparsity at: 0.4915671942060086 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8944 - val_loss: 0.8945 - val_accuracy: 0.8991 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0808915e-01 1.1299156e-01 1.3927774e-01] Sparsity at: 0.4915671942060086 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9118 - accuracy: 0.8945 - val_loss: 0.8946 - val_accuracy: 0.9003 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0823208e-01 1.1257302e-01 1.3942721e-01] Sparsity at: 0.4915671942060086 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.8998 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08243436e-01 1.12515368e-01 1.39504388e-01] Sparsity at: 0.4915671942060086 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9119 - accuracy: 0.8945 - val_loss: 0.8948 - val_accuracy: 0.8995 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08294249e-01 1.12046905e-01 1.39681369e-01] Sparsity at: 0.4915671942060086 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.8996 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08254306e-01 1.12071477e-01 1.39662579e-01] Sparsity at: 0.4915671942060086 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8947 - val_loss: 0.8947 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0854838e-01 1.1184277e-01 1.3973156e-01] Sparsity at: 0.4915671942060086 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0864089e-01 1.1178386e-01 1.3980819e-01] Sparsity at: 0.4915671942060086 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0882367e-01 1.1164524e-01 1.3964541e-01] Sparsity at: 0.4915671942060086 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0877126e-01 1.1150129e-01 1.3984288e-01] Sparsity at: 0.4915671942060086 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8947 - val_loss: 0.8946 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0871526e-01 1.1149391e-01 1.3994870e-01] Sparsity at: 0.4915671942060086 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.9000 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.08914539e-01 1.11582175e-01 1.39858678e-01] Sparsity at: 0.4915671942060086 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0904176e-01 1.1136688e-01 1.4003323e-01] Sparsity at: 0.4915671942060086 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9118 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.8998 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09146819e-01 1.11343555e-01 1.40185088e-01] Sparsity at: 0.4915671942060086 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8944 - val_loss: 0.8943 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0924067e-01 1.1119902e-01 1.4008564e-01] Sparsity at: 0.4915671942060086 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8945 - val_loss: 0.8944 - val_accuracy: 0.9000 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09411955e-01 1.11466885e-01 1.40002072e-01] Sparsity at: 0.4915671942060086 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.9000 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09446436e-01 1.11576110e-01 1.40069053e-01] Sparsity at: 0.4915671942060086 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0947785e-01 1.1183558e-01 1.4019898e-01] Sparsity at: 0.4915671942060086 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.9002 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0984030e-01 1.1168176e-01 1.4006375e-01] Sparsity at: 0.4915671942060086 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8944 - val_loss: 0.8948 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.0979279e-01 1.1197060e-01 1.4004262e-01] Sparsity at: 0.4915671942060086 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8948 - val_loss: 0.8947 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09773651e-01 1.12278104e-01 1.39997974e-01] Sparsity at: 0.4915671942060086 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.9001 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09965064e-01 1.12150334e-01 1.39866471e-01] Sparsity at: 0.4915671942060086 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8947 - val_loss: 0.8946 - val_accuracy: 0.8995 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.09918565e-01 1.12313867e-01 1.39846548e-01] Sparsity at: 0.4915671942060086 Epoch 464/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9117 - accuracy: 0.8945 - val_loss: 0.8945 - val_accuracy: 0.9001 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1017323e-01 1.1265886e-01 1.3969204e-01] Sparsity at: 0.4915671942060086 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8946 - val_loss: 0.8946 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1024426e-01 1.1284759e-01 1.3957731e-01] Sparsity at: 0.4915671942060086 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8946 - val_loss: 0.8944 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10267915e-01 1.13078974e-01 1.39402643e-01] Sparsity at: 0.4915671942060086 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9116 - accuracy: 0.8945 - val_loss: 0.8946 - val_accuracy: 0.8997 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10373557e-01 1.13316454e-01 1.39337704e-01] Sparsity at: 0.4915671942060086 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8944 - val_loss: 0.8945 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1035977e-01 1.1347250e-01 1.3934255e-01] Sparsity at: 0.4915671942060086 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8945 - val_loss: 0.8943 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1046026e-01 1.1387812e-01 1.3936655e-01] Sparsity at: 0.4915671942060086 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9116 - accuracy: 0.8945 - val_loss: 0.8942 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1065581e-01 1.1401110e-01 1.3887058e-01] Sparsity at: 0.4915671942060086 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8948 - val_loss: 0.8944 - val_accuracy: 0.8997 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10689074e-01 1.14183478e-01 1.38991609e-01] Sparsity at: 0.4915671942060086 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8999 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1066421e-01 1.1480149e-01 1.3878663e-01] Sparsity at: 0.4915671942060086 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8949 - val_loss: 0.8945 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1086204e-01 1.1478627e-01 1.3863562e-01] Sparsity at: 0.4915671942060086 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8949 - val_loss: 0.8943 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1096316e-01 1.1493969e-01 1.3853990e-01] Sparsity at: 0.4915671942060086 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8943 - val_loss: 0.8942 - val_accuracy: 0.8996 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.10968165e-01 1.15184277e-01 1.38480559e-01] Sparsity at: 0.4915671942060086 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8945 - val_accuracy: 0.8992 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11093074e-01 1.15326643e-01 1.38397470e-01] Sparsity at: 0.4915671942060086 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9117 - accuracy: 0.8945 - val_loss: 0.8943 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1120243e-01 1.1535202e-01 1.3834369e-01] Sparsity at: 0.4915671942060086 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9116 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.9004 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11401595e-01 1.15486659e-01 1.38229296e-01] Sparsity at: 0.4915671942060086 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9113 - accuracy: 0.8948 - val_loss: 0.8943 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1129933e-01 1.1570214e-01 1.3817190e-01] Sparsity at: 0.4915671942060086 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8941 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1154682e-01 1.1579528e-01 1.3817000e-01] Sparsity at: 0.4915671942060086 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8945 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1166912e-01 1.1582640e-01 1.3809885e-01] Sparsity at: 0.4915671942060086 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8947 - val_loss: 0.8942 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1177162e-01 1.1585528e-01 1.3792144e-01] Sparsity at: 0.4915671942060086 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8949 - val_loss: 0.8944 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1153146e-01 1.1595198e-01 1.3794558e-01] Sparsity at: 0.4915671942060086 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8949 - val_loss: 0.8946 - val_accuracy: 0.8995 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1186957e-01 1.1604665e-01 1.3788256e-01] Sparsity at: 0.4915671942060086 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8943 - val_accuracy: 0.8995 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.11905605e-01 1.16103694e-01 1.37739092e-01] Sparsity at: 0.4915671942060086 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8943 - val_loss: 0.8944 - val_accuracy: 0.8996 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1206704e-01 1.1605492e-01 1.3771498e-01] Sparsity at: 0.4915671942060086 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9115 - accuracy: 0.8946 - val_loss: 0.8943 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1225508e-01 1.1603306e-01 1.3756871e-01] Sparsity at: 0.4915671942060086 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8942 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1232754e-01 1.1599693e-01 1.3772751e-01] Sparsity at: 0.4915671942060086 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8943 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1223139e-01 1.1623563e-01 1.3775641e-01] Sparsity at: 0.4915671942060086 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8948 - val_loss: 0.8945 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12159371e-01 1.16325974e-01 1.37708455e-01] Sparsity at: 0.4915671942060086 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8941 - val_accuracy: 0.8997 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12295575e-01 1.16299257e-01 1.37415767e-01] Sparsity at: 0.4915671942060086 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8948 - val_loss: 0.8945 - val_accuracy: 0.8997 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12389036e-01 1.16058737e-01 1.37529224e-01] Sparsity at: 0.4915671942060086 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8946 - val_loss: 0.8942 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12513818e-01 1.16293915e-01 1.37334928e-01] Sparsity at: 0.4915671942060086 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8945 - val_accuracy: 0.8999 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12554282e-01 1.16330415e-01 1.37311578e-01] Sparsity at: 0.4915671942060086 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.8997 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1249406e-01 1.1613330e-01 1.3743313e-01] Sparsity at: 0.4915671942060086 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9114 - accuracy: 0.8947 - val_loss: 0.8944 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1252463e-01 1.1630807e-01 1.3735846e-01] Sparsity at: 0.4915671942060086 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9114 - accuracy: 0.8949 - val_loss: 0.8944 - val_accuracy: 0.8997 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12515874e-01 1.16349913e-01 1.37368605e-01] Sparsity at: 0.4915671942060086 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9113 - accuracy: 0.8946 - val_loss: 0.8942 - val_accuracy: 0.8995 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12613365e-01 1.16213813e-01 1.37095481e-01] Sparsity at: 0.4915671942060086 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9113 - accuracy: 0.8949 - val_loss: 0.8943 - val_accuracy: 0.8998 [ 4.4059549e-34 0.0000000e+00 -5.4672311e-34 ... 1.1262289e-01 1.1653216e-01 1.3706270e-01] Sparsity at: 0.4915671942060086 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9115 - accuracy: 0.8947 - val_loss: 0.8943 - val_accuracy: 0.8998 [ 4.40595486e-34 0.00000000e+00 -5.46723111e-34 ... 1.12605706e-01 1.16307721e-01 1.37032583e-01] Sparsity at: 0.4915671942060086 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.042060211300849915 Thresholhold 0.08002246171236038 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08791892230510712 Thresholhold 0.1589777022600174 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 0. 1. 0.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 0. 1. 1.] ... [0. 1. 0. ... 0. 1. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 1. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10110617056488991 Thresholhold 0.0191974937915802 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:22 - loss: 2.3111 - accuracy: 0.0742WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0053s vs `on_train_batch_begin` time: 2.4754s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 0.5980 - accuracy: 0.8456 - val_loss: 0.2966 - val_accuracy: 0.9151 [ 0.08002246 0. -0.07110295 ... 0.15403095 0.22507241 -0.04287057] Sparsity at: 0.4915671942060086 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2695 - accuracy: 0.9220 - val_loss: 0.2337 - val_accuracy: 0.9325 [ 0.08002246 0. -0.07110295 ... 0.18079707 0.2367594 -0.03975654] Sparsity at: 0.4915671942060086 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2170 - accuracy: 0.9369 - val_loss: 0.1958 - val_accuracy: 0.9406 [ 0.08002246 0. -0.07110295 ... 0.19941992 0.24121058 -0.04119756] Sparsity at: 0.4915671942060086 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1823 - accuracy: 0.9467 - val_loss: 0.1699 - val_accuracy: 0.9495 [ 0.08002246 0. -0.07110295 ... 0.21053828 0.2435588 -0.04173013] Sparsity at: 0.4915671942060086 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1569 - accuracy: 0.9541 - val_loss: 0.1508 - val_accuracy: 0.9551 [ 0.08002246 0. -0.07110295 ... 0.2170271 0.24542262 -0.04122256] Sparsity at: 0.4915671942060086 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1375 - accuracy: 0.9602 - val_loss: 0.1370 - val_accuracy: 0.9578 [ 0.08002246 0. -0.07110295 ... 0.22122398 0.24742967 -0.04063053] Sparsity at: 0.4915671942060086 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1219 - accuracy: 0.9647 - val_loss: 0.1263 - val_accuracy: 0.9614 [ 0.08002246 0. -0.07110295 ... 0.22468105 0.25019264 -0.0401718 ] Sparsity at: 0.4915671942060086 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1089 - accuracy: 0.9682 - val_loss: 0.1181 - val_accuracy: 0.9639 [ 0.08002246 0. -0.07110295 ... 0.22849211 0.25324175 -0.04001823] Sparsity at: 0.4915671942060086 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0981 - accuracy: 0.9715 - val_loss: 0.1116 - val_accuracy: 0.9654 [ 0.08002246 0. -0.07110295 ... 0.23292 0.25692385 -0.0407346 ] Sparsity at: 0.4915671942060086 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0889 - accuracy: 0.9740 - val_loss: 0.1065 - val_accuracy: 0.9675 [ 0.08002246 0. -0.07110295 ... 0.23783354 0.26033744 -0.04115634] Sparsity at: 0.4915671942060086 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0806 - accuracy: 0.9766 - val_loss: 0.1024 - val_accuracy: 0.9689 [ 0.08002246 0. -0.07110295 ... 0.24293017 0.2644032 -0.04193686] Sparsity at: 0.4915671942060086 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0737 - accuracy: 0.9788 - val_loss: 0.0994 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 0.24819818 0.26818782 -0.04247066] Sparsity at: 0.4915671942060086 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0675 - accuracy: 0.9807 - val_loss: 0.0971 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.253902 0.27221018 -0.04262 ] Sparsity at: 0.4915671942060086 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0620 - accuracy: 0.9824 - val_loss: 0.0955 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.25923094 0.27618933 -0.04229878] Sparsity at: 0.4915671942060086 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0569 - accuracy: 0.9836 - val_loss: 0.0944 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.26531258 0.28025642 -0.04180551] Sparsity at: 0.4915671942060086 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0523 - accuracy: 0.9850 - val_loss: 0.0938 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.27090555 0.28489769 -0.04055597] Sparsity at: 0.4915671942060086 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0480 - accuracy: 0.9860 - val_loss: 0.0938 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.27674052 0.29051438 -0.03956193] Sparsity at: 0.4915671942060086 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0442 - accuracy: 0.9877 - val_loss: 0.0941 - val_accuracy: 0.9718 [ 0.08002246 0. -0.07110295 ... 0.28304893 0.2961979 -0.03777971] Sparsity at: 0.4915671942060086 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0406 - accuracy: 0.9890 - val_loss: 0.0943 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.28919673 0.3019713 -0.03591988] Sparsity at: 0.4915671942060086 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0373 - accuracy: 0.9900 - val_loss: 0.0953 - val_accuracy: 0.9718 [ 0.08002246 0. -0.07110295 ... 0.29516745 0.308648 -0.03338408] Sparsity at: 0.4915671942060086 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0342 - accuracy: 0.9913 - val_loss: 0.0961 - val_accuracy: 0.9720 [ 0.08002246 0. -0.07110295 ... 0.3017227 0.31511888 -0.030502 ] Sparsity at: 0.4915671942060086 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0313 - accuracy: 0.9922 - val_loss: 0.0973 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.308174 0.32197502 -0.02749309] Sparsity at: 0.4915671942060086 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0285 - accuracy: 0.9930 - val_loss: 0.0986 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.31502944 0.32921323 -0.02383372] Sparsity at: 0.4915671942060086 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0261 - accuracy: 0.9940 - val_loss: 0.1003 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.32229623 0.33611634 -0.0204662 ] Sparsity at: 0.4915671942060086 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0237 - accuracy: 0.9948 - val_loss: 0.1017 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.32807782 0.343554 -0.01632345] Sparsity at: 0.4915671942060086 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0216 - accuracy: 0.9954 - val_loss: 0.1033 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.33514717 0.35138047 -0.01284597] Sparsity at: 0.4915671942060086 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0196 - accuracy: 0.9959 - val_loss: 0.1054 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.34274822 0.3586219 -0.00920124] Sparsity at: 0.4915671942060086 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0177 - accuracy: 0.9966 - val_loss: 0.1067 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.34905848 0.36611503 -0.00547521] Sparsity at: 0.4915671942060086 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.1085 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.3567542 0.37312636 -0.0015449 ] Sparsity at: 0.4915671942060086 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0144 - accuracy: 0.9976 - val_loss: 0.1105 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 0.36298195 0.38019937 0.00272609] Sparsity at: 0.4915671942060086 Epoch 31/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0130 - accuracy: 0.9982 - val_loss: 0.1131 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 0.37064958 0.38756934 0.00717586] Sparsity at: 0.4915671942060086 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0116 - accuracy: 0.9985 - val_loss: 0.1156 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.37758613 0.39457095 0.01137318] Sparsity at: 0.4915671942060086 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0105 - accuracy: 0.9987 - val_loss: 0.1178 - val_accuracy: 0.9699 [ 0.08002246 0. -0.07110295 ... 0.38473612 0.4013706 0.01473436] Sparsity at: 0.4915671942060086 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0095 - accuracy: 0.9990 - val_loss: 0.1205 - val_accuracy: 0.9697 [ 0.08002246 0. -0.07110295 ... 0.39330837 0.40836734 0.01899385] Sparsity at: 0.4915671942060086 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0085 - accuracy: 0.9992 - val_loss: 0.1236 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 0.40130016 0.4156744 0.02317327] Sparsity at: 0.4915671942060086 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0076 - accuracy: 0.9994 - val_loss: 0.1263 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 0.40796375 0.42280975 0.02714473] Sparsity at: 0.4915671942060086 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0068 - accuracy: 0.9995 - val_loss: 0.1302 - val_accuracy: 0.9701 [ 0.08002246 0. -0.07110295 ... 0.41650733 0.4315623 0.03071887] Sparsity at: 0.4915671942060086 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0061 - accuracy: 0.9996 - val_loss: 0.1345 - val_accuracy: 0.9702 [ 0.08002246 0. -0.07110295 ... 0.42501035 0.4403443 0.03356542] Sparsity at: 0.4915671942060086 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9997 - val_loss: 0.1387 - val_accuracy: 0.9697 [ 0.08002246 0. -0.07110295 ... 0.43323556 0.44742095 0.0359411 ] Sparsity at: 0.4915671942060086 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0051 - accuracy: 0.9997 - val_loss: 0.1397 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.44202086 0.4532475 0.03814758] Sparsity at: 0.4915671942060086 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0049 - accuracy: 0.9995 - val_loss: 0.1436 - val_accuracy: 0.9695 [ 0.08002246 0. -0.07110295 ... 0.4509198 0.45937124 0.04281923] Sparsity at: 0.4915671942060086 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0049 - accuracy: 0.9995 - val_loss: 0.1422 - val_accuracy: 0.9702 [ 0.08002246 0. -0.07110295 ... 0.46041158 0.46822894 0.0464743 ] Sparsity at: 0.4915671942060086 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0045 - accuracy: 0.9996 - val_loss: 0.1527 - val_accuracy: 0.9671 [ 0.08002246 0. -0.07110295 ... 0.46471274 0.46575364 0.05543198] Sparsity at: 0.4915671942060086 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9995 - val_loss: 0.1563 - val_accuracy: 0.9679 [ 0.08002246 0. -0.07110295 ... 0.47271535 0.467069 0.05703241] Sparsity at: 0.4915671942060086 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0045 - accuracy: 0.9994 - val_loss: 0.1625 - val_accuracy: 0.9688 [ 0.08002246 0. -0.07110295 ... 0.47844338 0.47392547 0.04568287] Sparsity at: 0.4915671942060086 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9994 - val_loss: 0.1774 - val_accuracy: 0.9656 [ 0.08002246 0. -0.07110295 ... 0.48285082 0.47367832 0.05655241] Sparsity at: 0.4915671942060086 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.1754 - val_accuracy: 0.9645 [ 0.08002246 0. -0.07110295 ... 0.49553707 0.47806528 0.05026842] Sparsity at: 0.4915671942060086 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0038 - accuracy: 0.9995 - val_loss: 0.1563 - val_accuracy: 0.9681 [ 0.08002246 0. -0.07110295 ... 0.49351195 0.47230536 0.05889468] Sparsity at: 0.4915671942060086 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0026 - accuracy: 0.9998 - val_loss: 0.1564 - val_accuracy: 0.9701 [ 0.08002246 0. -0.07110295 ... 0.49957392 0.47800484 0.0625774 ] Sparsity at: 0.4915671942060086 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.1501 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 0.5012927 0.47947577 0.06831842] Sparsity at: 0.4915671942060086 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.06554365864303513 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.13429796234413516 Thresholhold 0.3532998859882355 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.5239421559155915 Thresholhold -0.0007533457246609032 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 50s 7ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 0.50778925 0.48476535 0.06988464] Sparsity at: 0.5116013948497854 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 0.5114411 0.48745537 0.07546558] Sparsity at: 0.5116013948497854 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 0.51805764 0.49229187 0.08141425] Sparsity at: 0.5116013948497854 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.5221192 0.50078255 0.08632594] Sparsity at: 0.5116013948497854 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.5253793 0.50655377 0.09191508] Sparsity at: 0.5116013948497854 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5513e-04 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.5312357 0.5126764 0.09732085] Sparsity at: 0.5116013948497854 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3535e-04 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.53652817 0.5181625 0.10230128] Sparsity at: 0.5116013948497854 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 7.3349e-04 - accuracy: 1.0000 - val_loss: 0.1605 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.54235137 0.5236728 0.10680497] Sparsity at: 0.5116013948497854 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 6.4975e-04 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 0.54803544 0.52893156 0.11131476] Sparsity at: 0.5116013948497854 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7474e-04 - accuracy: 1.0000 - val_loss: 0.1635 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 0.5543503 0.53421384 0.11474145] Sparsity at: 0.5116013948497854 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1033e-04 - accuracy: 1.0000 - val_loss: 0.1651 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 0.5610127 0.53964263 0.11801929] Sparsity at: 0.5116013948497854 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5406e-04 - accuracy: 1.0000 - val_loss: 0.1666 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 0.5668852 0.54505545 0.12125406] Sparsity at: 0.5116013948497854 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0410e-04 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.5729607 0.550516 0.12463471] Sparsity at: 0.5116013948497854 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6155e-04 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 0.58009094 0.55629396 0.12719645] Sparsity at: 0.5116013948497854 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2144e-04 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 0.5867501 0.5620342 0.1299306 ] Sparsity at: 0.5116013948497854 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8675e-04 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.59356534 0.5676849 0.13260476] Sparsity at: 0.5116013948497854 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5653e-04 - accuracy: 1.0000 - val_loss: 0.1767 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.6006754 0.5733052 0.13497421] Sparsity at: 0.5116013948497854 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3003e-04 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 0.6070699 0.5795346 0.1372382 ] Sparsity at: 0.5116013948497854 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0508e-04 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 0.61442304 0.5850866 0.13928041] Sparsity at: 0.5116013948497854 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8378e-04 - accuracy: 1.0000 - val_loss: 0.1825 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 0.6211853 0.5907173 0.1409962 ] Sparsity at: 0.5116013948497854 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6437e-04 - accuracy: 1.0000 - val_loss: 0.1853 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.6286395 0.5968385 0.14332537] Sparsity at: 0.5116013948497854 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4672e-04 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.6361831 0.6029562 0.14485931] Sparsity at: 0.5116013948497854 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3159e-04 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.64391124 0.60869485 0.14657554] Sparsity at: 0.5116013948497854 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1872e-04 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.6505655 0.61451125 0.14875573] Sparsity at: 0.5116013948497854 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0560e-04 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 0.657837 0.620975 0.15077709] Sparsity at: 0.5116013948497854 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3634e-05 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.6653088 0.62722665 0.15233386] Sparsity at: 0.5116013948497854 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3785e-05 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 0.6724663 0.63315266 0.15393059] Sparsity at: 0.5116013948497854 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4851e-05 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 0.68032986 0.63895845 0.15584745] Sparsity at: 0.5116013948497854 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6860e-05 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.68747383 0.6452678 0.15746363] Sparsity at: 0.5116013948497854 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9809e-05 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.6952295 0.65169376 0.15860666] Sparsity at: 0.5116013948497854 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3311e-05 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 0.70221764 0.65778714 0.16044375] Sparsity at: 0.5116013948497854 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7491e-05 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 0.7097111 0.66405624 0.16228974] Sparsity at: 0.5116013948497854 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2425e-05 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 0.7176315 0.67037684 0.16367453] Sparsity at: 0.5116013948497854 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7760e-05 - accuracy: 1.0000 - val_loss: 0.2157 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.7250413 0.67672855 0.1650769 ] Sparsity at: 0.5116013948497854 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3581e-05 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 0.73262995 0.6834498 0.16692896] Sparsity at: 0.5116013948497854 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9862e-05 - accuracy: 1.0000 - val_loss: 0.2211 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.74092406 0.68995535 0.16816261] Sparsity at: 0.5116013948497854 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6664e-05 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 0.7484569 0.69624 0.16927885] Sparsity at: 0.5116013948497854 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3840e-05 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.75560665 0.70245093 0.17066649] Sparsity at: 0.5116013948497854 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 2.1244e-05 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.7634249 0.7090837 0.17212826] Sparsity at: 0.5116013948497854 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8752e-05 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.77092737 0.7158673 0.17326224] Sparsity at: 0.5116013948497854 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6699e-05 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.7786712 0.7223492 0.1749871 ] Sparsity at: 0.5116013948497854 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4937e-05 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 0.7859144 0.72874236 0.1763072 ] Sparsity at: 0.5116013948497854 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3169e-05 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 0.7936984 0.7352431 0.17779565] Sparsity at: 0.5116013948497854 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1734e-05 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 0.80148876 0.74207425 0.17868333] Sparsity at: 0.5116013948497854 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0447e-05 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 0.80902225 0.74880123 0.17986132] Sparsity at: 0.5116013948497854 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3253e-06 - accuracy: 1.0000 - val_loss: 0.2467 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 0.8164374 0.754989 0.18077394] Sparsity at: 0.5116013948497854 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3214e-06 - accuracy: 1.0000 - val_loss: 0.2497 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 0.82393587 0.7616106 0.1824721 ] Sparsity at: 0.5116013948497854 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3960e-06 - accuracy: 1.0000 - val_loss: 0.2529 - val_accuracy: 0.9702 [ 0.08002246 0. -0.07110295 ... 0.8313027 0.76837736 0.18384735] Sparsity at: 0.5116013948497854 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5732e-06 - accuracy: 1.0000 - val_loss: 0.2552 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 0.8388487 0.77439874 0.18466987] Sparsity at: 0.5116013948497854 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8743e-06 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9701 [ 0.08002246 0. -0.07110295 ... 0.8455984 0.78082407 0.18604793] Sparsity at: 0.5116013948497854 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.14119782226718947 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.2951456754509607 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.9428782204331725 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 5.2214e-06 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9701 [ 0.08002246 0. -0.07110295 ... 0.8535893 0.7869235 0.1867662 ] Sparsity at: 0.5116013948497854 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 4.6418e-06 - accuracy: 1.0000 - val_loss: 0.2637 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 0.8598836 0.7933987 0.18839444] Sparsity at: 0.5116013948497854 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1878e-06 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 0.86643565 0.79961586 0.1906055 ] Sparsity at: 0.5116013948497854 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7254e-06 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9701 [ 0.08002246 0. -0.07110295 ... 0.87315595 0.80611664 0.19233303] Sparsity at: 0.5116013948497854 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3195e-06 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.8804346 0.8124888 0.19401895] Sparsity at: 0.5116013948497854 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0109e-06 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9701 [ 0.08002246 0. -0.07110295 ... 0.88775516 0.8187922 0.19613324] Sparsity at: 0.5116013948497854 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6619e-06 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 0.89448494 0.8246865 0.19801609] Sparsity at: 0.5116013948497854 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0271 - accuracy: 0.9938 - val_loss: 0.2829 - val_accuracy: 0.9679 [ 0.08002246 0. -0.07110295 ... 0.9267318 0.8015158 0.14386694] Sparsity at: 0.5116013948497854 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9990 - val_loss: 0.2737 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.9299757 0.79526436 0.14478196] Sparsity at: 0.5116013948497854 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2106e-04 - accuracy: 0.9998 - val_loss: 0.2736 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 0.9338108 0.7981049 0.1419741 ] Sparsity at: 0.5116013948497854 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5939e-04 - accuracy: 0.9999 - val_loss: 0.2693 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.94307846 0.7971686 0.13921237] Sparsity at: 0.5116013948497854 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1742e-04 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 0.94218606 0.7976946 0.14185008] Sparsity at: 0.5116013948497854 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0115e-05 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9719 [ 0.08002246 0. -0.07110295 ... 0.9395287 0.7991111 0.14276811] Sparsity at: 0.5116013948497854 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7200e-05 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9387769 0.7995772 0.14285561] Sparsity at: 0.5116013948497854 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1744e-05 - accuracy: 1.0000 - val_loss: 0.2691 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.93819857 0.79975736 0.14293657] Sparsity at: 0.5116013948497854 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8436e-05 - accuracy: 1.0000 - val_loss: 0.2690 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.93768185 0.7998711 0.14300315] Sparsity at: 0.5116013948497854 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5915e-05 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93719524 0.7999583 0.14305682] Sparsity at: 0.5116013948497854 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3831e-05 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.93673116 0.8000387 0.1431102 ] Sparsity at: 0.5116013948497854 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2076e-05 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93628556 0.80011916 0.14315969] Sparsity at: 0.5116013948497854 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0527e-05 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.9358458 0.8002066 0.14321253] Sparsity at: 0.5116013948497854 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9156e-05 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.93542665 0.80030555 0.14327054] Sparsity at: 0.5116013948497854 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7923e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93500954 0.80041975 0.14333855] Sparsity at: 0.5116013948497854 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6817e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.9346228 0.8005531 0.14340968] Sparsity at: 0.5116013948497854 Epoch 124/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5788e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9342495 0.8007027 0.14349501] Sparsity at: 0.5116013948497854 Epoch 125/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4842e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93390036 0.80087936 0.14359081] Sparsity at: 0.5116013948497854 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3980e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.9335786 0.8010782 0.14369519] Sparsity at: 0.5116013948497854 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3155e-05 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.93325156 0.80130696 0.1438232 ] Sparsity at: 0.5116013948497854 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2396e-05 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9329664 0.8015576 0.1439584 ] Sparsity at: 0.5116013948497854 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1682e-05 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9327074 0.80185014 0.1441163 ] Sparsity at: 0.5116013948497854 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1014e-05 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93249404 0.8021757 0.14428182] Sparsity at: 0.5116013948497854 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0388e-05 - accuracy: 1.0000 - val_loss: 0.2689 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.9323132 0.80254304 0.1444581 ] Sparsity at: 0.5116013948497854 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7966e-06 - accuracy: 1.0000 - val_loss: 0.2690 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9321492 0.8029356 0.1446675 ] Sparsity at: 0.5116013948497854 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 9.2398e-06 - accuracy: 1.0000 - val_loss: 0.2692 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.9320471 0.80336416 0.14489844] Sparsity at: 0.5116013948497854 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7020e-06 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.93198174 0.8038309 0.14515086] Sparsity at: 0.5116013948497854 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2041e-06 - accuracy: 1.0000 - val_loss: 0.2695 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9319843 0.80432844 0.14541622] Sparsity at: 0.5116013948497854 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7269e-06 - accuracy: 1.0000 - val_loss: 0.2697 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9320231 0.8048629 0.14571424] Sparsity at: 0.5116013948497854 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2813e-06 - accuracy: 1.0000 - val_loss: 0.2699 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.9320844 0.8054465 0.14604397] Sparsity at: 0.5116013948497854 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8543e-06 - accuracy: 1.0000 - val_loss: 0.2702 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9321975 0.80607814 0.14639887] Sparsity at: 0.5116013948497854 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4584e-06 - accuracy: 1.0000 - val_loss: 0.2704 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9323722 0.8067439 0.14677651] Sparsity at: 0.5116013948497854 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0757e-06 - accuracy: 1.0000 - val_loss: 0.2707 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9326314 0.80745 0.14717764] Sparsity at: 0.5116013948497854 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7205e-06 - accuracy: 1.0000 - val_loss: 0.2710 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93291247 0.8082084 0.14764148] Sparsity at: 0.5116013948497854 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3746e-06 - accuracy: 1.0000 - val_loss: 0.2713 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93325144 0.8090063 0.14810437] Sparsity at: 0.5116013948497854 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0533e-06 - accuracy: 1.0000 - val_loss: 0.2717 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.9336556 0.809865 0.1486166 ] Sparsity at: 0.5116013948497854 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7488e-06 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.9340755 0.81080574 0.14916632] Sparsity at: 0.5116013948497854 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4558e-06 - accuracy: 1.0000 - val_loss: 0.2724 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.9346113 0.81177986 0.14973277] Sparsity at: 0.5116013948497854 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1818e-06 - accuracy: 1.0000 - val_loss: 0.2728 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.93520796 0.81283444 0.15031265] Sparsity at: 0.5116013948497854 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9265e-06 - accuracy: 1.0000 - val_loss: 0.2732 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 0.9358406 0.81393677 0.15097463] Sparsity at: 0.5116013948497854 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6872e-06 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.93653786 0.8151299 0.1516893 ] Sparsity at: 0.5116013948497854 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4513e-06 - accuracy: 1.0000 - val_loss: 0.2740 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9372964 0.81636536 0.15243332] Sparsity at: 0.5116013948497854 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2351e-06 - accuracy: 1.0000 - val_loss: 0.2745 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9382226 0.8176982 0.15318665] Sparsity at: 0.5116013948497854 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.23946041399091111 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.42941963463627175 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 1.170436173033778 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 3.0310e-06 - accuracy: 1.0000 - val_loss: 0.2750 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.9391556 0.81912357 0.15400468] Sparsity at: 0.5116013948497854 Epoch 152/500 235/235 [==============================] - 2s 7ms/step - loss: 2.8373e-06 - accuracy: 1.0000 - val_loss: 0.2755 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.94017947 0.8206563 0.1548881 ] Sparsity at: 0.5116013948497854 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6569e-06 - accuracy: 1.0000 - val_loss: 0.2761 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.94130427 0.8222832 0.15577006] Sparsity at: 0.5116013948497854 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4858e-06 - accuracy: 1.0000 - val_loss: 0.2767 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.9423819 0.82402843 0.15677132] Sparsity at: 0.5116013948497854 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3227e-06 - accuracy: 1.0000 - val_loss: 0.2772 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.94367987 0.8258381 0.15772903] Sparsity at: 0.5116013948497854 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1712e-06 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.9450337 0.8278068 0.15874985] Sparsity at: 0.5116013948497854 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0256e-06 - accuracy: 1.0000 - val_loss: 0.2786 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.94649535 0.82989043 0.1598039 ] Sparsity at: 0.5116013948497854 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8905e-06 - accuracy: 1.0000 - val_loss: 0.2794 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.94806296 0.8320509 0.16090116] Sparsity at: 0.5116013948497854 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7644e-06 - accuracy: 1.0000 - val_loss: 0.2802 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.9497346 0.8343659 0.16204314] Sparsity at: 0.5116013948497854 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6440e-06 - accuracy: 1.0000 - val_loss: 0.2810 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 0.9515984 0.83680654 0.1631748 ] Sparsity at: 0.5116013948497854 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5311e-06 - accuracy: 1.0000 - val_loss: 0.2819 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.95351386 0.8394058 0.16434117] Sparsity at: 0.5116013948497854 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4255e-06 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9554875 0.8420406 0.16552164] Sparsity at: 0.5116013948497854 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3258e-06 - accuracy: 1.0000 - val_loss: 0.2838 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.95776176 0.844837 0.16675669] Sparsity at: 0.5116013948497854 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2314e-06 - accuracy: 1.0000 - val_loss: 0.2848 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9601047 0.8477735 0.1679596 ] Sparsity at: 0.5116013948497854 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1423e-06 - accuracy: 1.0000 - val_loss: 0.2858 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.96256465 0.8507928 0.16923088] Sparsity at: 0.5116013948497854 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0607e-06 - accuracy: 1.0000 - val_loss: 0.2870 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9652188 0.8539904 0.17048721] Sparsity at: 0.5116013948497854 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 9.8366e-07 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9678429 0.8572516 0.17173943] Sparsity at: 0.5116013948497854 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0995e-07 - accuracy: 1.0000 - val_loss: 0.2893 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.97074324 0.8606325 0.17297731] Sparsity at: 0.5116013948497854 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 8.4401e-07 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9736803 0.864152 0.17425396] Sparsity at: 0.5116013948497854 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8079e-07 - accuracy: 1.0000 - val_loss: 0.2919 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.9768014 0.86780673 0.1754415 ] Sparsity at: 0.5116013948497854 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2241e-07 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9800908 0.87148595 0.17674567] Sparsity at: 0.5116013948497854 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6570e-07 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.98353326 0.8753691 0.17802022] Sparsity at: 0.5116013948497854 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1541e-07 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 0.9868986 0.87927747 0.17916375] Sparsity at: 0.5116013948497854 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7008e-07 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 0.99047256 0.8833411 0.18042998] Sparsity at: 0.5116013948497854 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2609e-07 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 0.9940986 0.8874302 0.18169056] Sparsity at: 0.5116013948497854 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8362e-07 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 0.99780285 0.89153147 0.18288834] Sparsity at: 0.5116013948497854 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4767e-07 - accuracy: 1.0000 - val_loss: 0.3014 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 1.0014399 0.89560425 0.1840831 ] Sparsity at: 0.5116013948497854 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1291e-07 - accuracy: 1.0000 - val_loss: 0.3029 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 1.0052856 0.8998426 0.18532866] Sparsity at: 0.5116013948497854 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7990e-07 - accuracy: 1.0000 - val_loss: 0.3042 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 1.0090594 0.904079 0.18654358] Sparsity at: 0.5116013948497854 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5086e-07 - accuracy: 1.0000 - val_loss: 0.3056 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 1.0128978 0.90836895 0.18773304] Sparsity at: 0.5116013948497854 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2338e-07 - accuracy: 1.0000 - val_loss: 0.3070 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 1.0165602 0.9124882 0.18892589] Sparsity at: 0.5116013948497854 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9908e-07 - accuracy: 1.0000 - val_loss: 0.3085 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 1.0203651 0.9167627 0.19019082] Sparsity at: 0.5116013948497854 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7626e-07 - accuracy: 1.0000 - val_loss: 0.3099 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 1.0241971 0.92095083 0.19137993] Sparsity at: 0.5116013948497854 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5542e-07 - accuracy: 1.0000 - val_loss: 0.3112 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 1.02799 0.92511165 0.1925311 ] Sparsity at: 0.5116013948497854 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3658e-07 - accuracy: 1.0000 - val_loss: 0.3126 - val_accuracy: 0.9716 [ 0.08002246 0. -0.07110295 ... 1.031684 0.9293551 0.19356833] Sparsity at: 0.5116013948497854 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1825e-07 - accuracy: 1.0000 - val_loss: 0.3139 - val_accuracy: 0.9717 [ 0.08002246 0. -0.07110295 ... 1.0353853 0.9333732 0.19463752] Sparsity at: 0.5116013948497854 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0267e-07 - accuracy: 1.0000 - val_loss: 0.3150 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 1.0389435 0.9374391 0.19565596] Sparsity at: 0.5116013948497854 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8800e-07 - accuracy: 1.0000 - val_loss: 0.3163 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 1.0424333 0.9413828 0.19680512] Sparsity at: 0.5116013948497854 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7409e-07 - accuracy: 1.0000 - val_loss: 0.3176 - val_accuracy: 0.9715 [ 0.08002246 0. -0.07110295 ... 1.0459361 0.945142 0.19775382] Sparsity at: 0.5116013948497854 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6225e-07 - accuracy: 1.0000 - val_loss: 0.3189 - val_accuracy: 0.9713 [ 0.08002246 0. -0.07110295 ... 1.0492002 0.94897187 0.1987091 ] Sparsity at: 0.5116013948497854 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5074e-07 - accuracy: 1.0000 - val_loss: 0.3202 - val_accuracy: 0.9714 [ 0.08002246 0. -0.07110295 ... 1.0526073 0.9526499 0.19962764] Sparsity at: 0.5116013948497854 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4107e-07 - accuracy: 1.0000 - val_loss: 0.3213 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.0558199 0.9561634 0.20049673] Sparsity at: 0.5116013948497854 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3153e-07 - accuracy: 1.0000 - val_loss: 0.3224 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.0589414 0.9597063 0.20133002] Sparsity at: 0.5116013948497854 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2293e-07 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.0620043 0.9630236 0.2021836 ] Sparsity at: 0.5116013948497854 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1511e-07 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.0648497 0.96642584 0.20299056] Sparsity at: 0.5116013948497854 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0807e-07 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.0675446 0.9695994 0.20372798] Sparsity at: 0.5116013948497854 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0171e-07 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.0703744 0.97263885 0.20444918] Sparsity at: 0.5116013948497854 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5594e-08 - accuracy: 1.0000 - val_loss: 0.3272 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.0730124 0.97574115 0.20511667] Sparsity at: 0.5116013948497854 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0005e-08 - accuracy: 1.0000 - val_loss: 0.3281 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.0756495 0.97860163 0.20574787] Sparsity at: 0.5116013948497854 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5078e-08 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.078089 0.98135614 0.2064157 ] Sparsity at: 0.5116013948497854 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.344600686924764 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.5727802309721852 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 1.54758833033614 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 8.0526e-08 - accuracy: 1.0000 - val_loss: 0.3301 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.0805283 0.9840386 0.20703177] Sparsity at: 0.5116013948497854 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 7.6006e-08 - accuracy: 1.0000 - val_loss: 0.3309 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.0828108 0.9865489 0.20759048] Sparsity at: 0.5116013948497854 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2247e-08 - accuracy: 1.0000 - val_loss: 0.3317 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.0850298 0.98894155 0.20825376] Sparsity at: 0.5116013948497854 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8913e-08 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.08705 0.9913236 0.20887177] Sparsity at: 0.5116013948497854 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5237e-08 - accuracy: 1.0000 - val_loss: 0.3330 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.0891968 0.99357665 0.20931752] Sparsity at: 0.5116013948497854 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2372e-08 - accuracy: 1.0000 - val_loss: 0.3335 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.0913095 0.9958616 0.20984542] Sparsity at: 0.5116013948497854 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9698e-08 - accuracy: 1.0000 - val_loss: 0.3344 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.0931442 0.9980131 0.21035486] Sparsity at: 0.5116013948497854 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7024e-08 - accuracy: 1.0000 - val_loss: 0.3351 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.0949134 1.0000789 0.21077304] Sparsity at: 0.5116013948497854 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4586e-08 - accuracy: 1.0000 - val_loss: 0.3357 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.0967585 1.0020877 0.21117713] Sparsity at: 0.5116013948497854 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2241e-08 - accuracy: 1.0000 - val_loss: 0.3363 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.098568 1.0040693 0.21163705] Sparsity at: 0.5116013948497854 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0231e-08 - accuracy: 1.0000 - val_loss: 0.3369 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1002375 1.0059892 0.21212219] Sparsity at: 0.5116013948497854 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8280e-08 - accuracy: 1.0000 - val_loss: 0.3373 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1018791 1.007808 0.21259817] Sparsity at: 0.5116013948497854 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6511e-08 - accuracy: 1.0000 - val_loss: 0.3380 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1034169 1.0095576 0.21301037] Sparsity at: 0.5116013948497854 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4795e-08 - accuracy: 1.0000 - val_loss: 0.3387 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1048678 1.0112716 0.21335705] Sparsity at: 0.5116013948497854 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3227e-08 - accuracy: 1.0000 - val_loss: 0.3392 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1063703 1.0129441 0.21372066] Sparsity at: 0.5116013948497854 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1707e-08 - accuracy: 1.0000 - val_loss: 0.3397 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1078496 1.0145354 0.21414009] Sparsity at: 0.5116013948497854 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0279e-08 - accuracy: 1.0000 - val_loss: 0.3401 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1092386 1.0160819 0.21459222] Sparsity at: 0.5116013948497854 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9128e-08 - accuracy: 1.0000 - val_loss: 0.3407 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1105025 1.017619 0.21491283] Sparsity at: 0.5116013948497854 Epoch 219/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7944e-08 - accuracy: 1.0000 - val_loss: 0.3411 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1118143 1.019124 0.21521968] Sparsity at: 0.5116013948497854 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6730e-08 - accuracy: 1.0000 - val_loss: 0.3415 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1130732 1.0205246 0.21555029] Sparsity at: 0.5116013948497854 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5640e-08 - accuracy: 1.0000 - val_loss: 0.3420 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1143157 1.0219153 0.21584727] Sparsity at: 0.5116013948497854 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4634e-08 - accuracy: 1.0000 - val_loss: 0.3423 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.11549 1.0233265 0.2160833 ] Sparsity at: 0.5116013948497854 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3673e-08 - accuracy: 1.0000 - val_loss: 0.3426 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1166044 1.0246931 0.2164341 ] Sparsity at: 0.5116013948497854 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2781e-08 - accuracy: 1.0000 - val_loss: 0.3431 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1176683 1.0259467 0.21675147] Sparsity at: 0.5116013948497854 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1938e-08 - accuracy: 1.0000 - val_loss: 0.3435 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1187712 1.0271915 0.21707678] Sparsity at: 0.5116013948497854 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1012e-08 - accuracy: 1.0000 - val_loss: 0.3439 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.119807 1.0284107 0.21735293] Sparsity at: 0.5116013948497854 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0265e-08 - accuracy: 1.0000 - val_loss: 0.3443 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1207848 1.0295526 0.21773078] Sparsity at: 0.5116013948497854 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9576e-08 - accuracy: 1.0000 - val_loss: 0.3448 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1217548 1.0306932 0.21808322] Sparsity at: 0.5116013948497854 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8843e-08 - accuracy: 1.0000 - val_loss: 0.3452 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.122732 1.0317628 0.2184578 ] Sparsity at: 0.5116013948497854 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8199e-08 - accuracy: 1.0000 - val_loss: 0.3456 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1236588 1.0328342 0.21874437] Sparsity at: 0.5116013948497854 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7643e-08 - accuracy: 1.0000 - val_loss: 0.3458 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1246171 1.0338436 0.21902342] Sparsity at: 0.5116013948497854 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6900e-08 - accuracy: 1.0000 - val_loss: 0.3462 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1254479 1.0348175 0.2193446 ] Sparsity at: 0.5116013948497854 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6361e-08 - accuracy: 1.0000 - val_loss: 0.3467 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1262771 1.0358253 0.21963872] Sparsity at: 0.5116013948497854 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 2.5837e-08 - accuracy: 1.0000 - val_loss: 0.3471 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1271682 1.0367985 0.2199002 ] Sparsity at: 0.5116013948497854 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5384e-08 - accuracy: 1.0000 - val_loss: 0.3474 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1279825 1.037672 0.2201489 ] Sparsity at: 0.5116013948497854 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4867e-08 - accuracy: 1.0000 - val_loss: 0.3477 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1287953 1.0385947 0.22049552] Sparsity at: 0.5116013948497854 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4412e-08 - accuracy: 1.0000 - val_loss: 0.3480 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1296275 1.0394884 0.22080153] Sparsity at: 0.5116013948497854 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3899e-08 - accuracy: 1.0000 - val_loss: 0.3485 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1303983 1.0403681 0.2210858 ] Sparsity at: 0.5116013948497854 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3488e-08 - accuracy: 1.0000 - val_loss: 0.3487 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1311212 1.0412334 0.22144732] Sparsity at: 0.5116013948497854 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 2.3045e-08 - accuracy: 1.0000 - val_loss: 0.3490 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1319119 1.042071 0.2217155 ] Sparsity at: 0.5116013948497854 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2688e-08 - accuracy: 1.0000 - val_loss: 0.3492 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1326574 1.0429013 0.22208643] Sparsity at: 0.5116013948497854 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2153e-08 - accuracy: 1.0000 - val_loss: 0.3497 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1334141 1.043686 0.22234629] Sparsity at: 0.5116013948497854 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1793e-08 - accuracy: 1.0000 - val_loss: 0.3501 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1341418 1.0444715 0.22267023] Sparsity at: 0.5116013948497854 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1398e-08 - accuracy: 1.0000 - val_loss: 0.3504 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1348488 1.0452358 0.2229607 ] Sparsity at: 0.5116013948497854 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0999e-08 - accuracy: 1.0000 - val_loss: 0.3506 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1356156 1.0459969 0.22321673] Sparsity at: 0.5116013948497854 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0607e-08 - accuracy: 1.0000 - val_loss: 0.3509 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.136392 1.046743 0.22343148] Sparsity at: 0.5116013948497854 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0289e-08 - accuracy: 1.0000 - val_loss: 0.3511 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1371075 1.0474757 0.22366564] Sparsity at: 0.5116013948497854 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0023e-08 - accuracy: 1.0000 - val_loss: 0.3516 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1378107 1.0482478 0.22384568] Sparsity at: 0.5116013948497854 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9618e-08 - accuracy: 1.0000 - val_loss: 0.3520 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.13852 1.0489675 0.22405006] Sparsity at: 0.5116013948497854 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9294e-08 - accuracy: 1.0000 - val_loss: 0.3520 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.1392056 1.0496923 0.224286 ] Sparsity at: 0.5116013948497854 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.46331068865162095 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.7079917295219857 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 1.8481063288886617 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 1.8974e-08 - accuracy: 1.0000 - val_loss: 0.3524 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1398543 1.0504013 0.2245038 ] Sparsity at: 0.5116013948497854 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 1.8587e-08 - accuracy: 1.0000 - val_loss: 0.3528 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.1405212 1.0511165 0.2246761 ] Sparsity at: 0.5116013948497854 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8295e-08 - accuracy: 1.0000 - val_loss: 0.3533 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.141181 1.0517938 0.22482622] Sparsity at: 0.5116013948497854 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7889e-08 - accuracy: 1.0000 - val_loss: 0.3534 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.1418693 1.0524273 0.22497855] Sparsity at: 0.5116013948497854 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7687e-08 - accuracy: 1.0000 - val_loss: 0.3539 - val_accuracy: 0.9712 [ 0.08002246 0. -0.07110295 ... 1.1424868 1.0530823 0.22512934] Sparsity at: 0.5116013948497854 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7349e-08 - accuracy: 1.0000 - val_loss: 0.3541 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1430708 1.0536879 0.22526829] Sparsity at: 0.5116013948497854 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7073e-08 - accuracy: 1.0000 - val_loss: 0.3546 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1436889 1.0543123 0.22537068] Sparsity at: 0.5116013948497854 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6755e-08 - accuracy: 1.0000 - val_loss: 0.3546 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1442436 1.0548899 0.22551091] Sparsity at: 0.5116013948497854 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6437e-08 - accuracy: 1.0000 - val_loss: 0.3551 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1448274 1.0555041 0.22562212] Sparsity at: 0.5116013948497854 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6250e-08 - accuracy: 1.0000 - val_loss: 0.3554 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1454196 1.056139 0.22576065] Sparsity at: 0.5116013948497854 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6057e-08 - accuracy: 1.0000 - val_loss: 0.3555 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1460092 1.056765 0.22587511] Sparsity at: 0.5116013948497854 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5769e-08 - accuracy: 1.0000 - val_loss: 0.3559 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1465688 1.0573351 0.22598183] Sparsity at: 0.5116013948497854 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5489e-08 - accuracy: 1.0000 - val_loss: 0.3559 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1471224 1.0579317 0.22611773] Sparsity at: 0.5116013948497854 Epoch 264/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5263e-08 - accuracy: 1.0000 - val_loss: 0.3562 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1476395 1.0584978 0.22617161] Sparsity at: 0.5116013948497854 Epoch 265/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5086e-08 - accuracy: 1.0000 - val_loss: 0.3565 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1481807 1.059052 0.22631523] Sparsity at: 0.5116013948497854 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4897e-08 - accuracy: 1.0000 - val_loss: 0.3567 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1487033 1.0595977 0.22646254] Sparsity at: 0.5116013948497854 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4629e-08 - accuracy: 1.0000 - val_loss: 0.3568 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1492277 1.0601337 0.22659662] Sparsity at: 0.5116013948497854 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4377e-08 - accuracy: 1.0000 - val_loss: 0.3569 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1497445 1.0606531 0.22675298] Sparsity at: 0.5116013948497854 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4140e-08 - accuracy: 1.0000 - val_loss: 0.3571 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1502373 1.0611734 0.22686619] Sparsity at: 0.5116013948497854 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3985e-08 - accuracy: 1.0000 - val_loss: 0.3572 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1507502 1.0617042 0.2270409 ] Sparsity at: 0.5116013948497854 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3814e-08 - accuracy: 1.0000 - val_loss: 0.3574 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1512592 1.0621785 0.22721252] Sparsity at: 0.5116013948497854 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3592e-08 - accuracy: 1.0000 - val_loss: 0.3573 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1517406 1.0626566 0.22742122] Sparsity at: 0.5116013948497854 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3361e-08 - accuracy: 1.0000 - val_loss: 0.3577 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1522075 1.0631145 0.22755119] Sparsity at: 0.5116013948497854 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3159e-08 - accuracy: 1.0000 - val_loss: 0.3577 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1526642 1.0635879 0.22772576] Sparsity at: 0.5116013948497854 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3008e-08 - accuracy: 1.0000 - val_loss: 0.3576 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1530999 1.0640544 0.22791645] Sparsity at: 0.5116013948497854 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2855e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1535734 1.0645125 0.22805011] Sparsity at: 0.5116013948497854 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2716e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1540412 1.064975 0.2281984 ] Sparsity at: 0.5116013948497854 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2483e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1544703 1.065438 0.22834817] Sparsity at: 0.5116013948497854 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2300e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1549065 1.065873 0.22848694] Sparsity at: 0.5116013948497854 Epoch 280/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2137e-08 - accuracy: 1.0000 - val_loss: 0.3580 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1553357 1.0662625 0.2285925 ] Sparsity at: 0.5116013948497854 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2026e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1557691 1.0666908 0.22869377] Sparsity at: 0.5116013948497854 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1869e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1561841 1.0671219 0.22881536] Sparsity at: 0.5116013948497854 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1667e-08 - accuracy: 1.0000 - val_loss: 0.3578 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1565789 1.0675455 0.22889107] Sparsity at: 0.5116013948497854 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1535e-08 - accuracy: 1.0000 - val_loss: 0.3579 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1569626 1.0679674 0.22898288] Sparsity at: 0.5116013948497854 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1377e-08 - accuracy: 1.0000 - val_loss: 0.3579 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1573323 1.0683653 0.22908865] Sparsity at: 0.5116013948497854 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1192e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1577159 1.0687848 0.22916555] Sparsity at: 0.5116013948497854 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1112e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1581126 1.0691932 0.22928175] Sparsity at: 0.5116013948497854 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0943e-08 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1585153 1.0696371 0.22935446] Sparsity at: 0.5116013948497854 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0798e-08 - accuracy: 1.0000 - val_loss: 0.3581 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1589208 1.07005 0.22944085] Sparsity at: 0.5116013948497854 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0647e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1592777 1.0704864 0.22951971] Sparsity at: 0.5116013948497854 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0540e-08 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1596526 1.0708917 0.22960599] Sparsity at: 0.5116013948497854 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0399e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1600301 1.0712835 0.22967878] Sparsity at: 0.5116013948497854 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0286e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1604029 1.0717083 0.22971834] Sparsity at: 0.5116013948497854 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0125e-08 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1607882 1.0720965 0.2297882 ] Sparsity at: 0.5116013948497854 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0010e-08 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1611373 1.0724416 0.22984193] Sparsity at: 0.5116013948497854 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 9.8864e-09 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1614971 1.0728202 0.22986944] Sparsity at: 0.5116013948497854 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7255e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1618346 1.0731399 0.22988464] Sparsity at: 0.5116013948497854 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 9.6639e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1621814 1.0734906 0.22993895] Sparsity at: 0.5116013948497854 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5089e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1625262 1.0738602 0.22993574] Sparsity at: 0.5116013948497854 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 9.4354e-09 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.162815 1.0741893 0.22995822] Sparsity at: 0.5116013948497854 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.5862329343481534 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.8287602642377081 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 2.051610510989633 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 9.3341e-09 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1631196 1.0745474 0.22998029] Sparsity at: 0.5116013948497854 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 9.2645e-09 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1634215 1.0748631 0.2300257 ] Sparsity at: 0.5116013948497854 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 9.1811e-09 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1637248 1.0751971 0.23003697] Sparsity at: 0.5116013948497854 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0659e-09 - accuracy: 1.0000 - val_loss: 0.3589 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.164049 1.0755434 0.2300626 ] Sparsity at: 0.5116013948497854 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0082e-09 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1643308 1.0759168 0.23008688] Sparsity at: 0.5116013948497854 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 8.8771e-09 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1646442 1.0762503 0.2300713 ] Sparsity at: 0.5116013948497854 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7877e-09 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1649318 1.0765939 0.23008683] Sparsity at: 0.5116013948497854 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7380e-09 - accuracy: 1.0000 - val_loss: 0.3592 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1651964 1.0769114 0.23008506] Sparsity at: 0.5116013948497854 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 8.6248e-09 - accuracy: 1.0000 - val_loss: 0.3592 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1654812 1.0772346 0.23009798] Sparsity at: 0.5116013948497854 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5950e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.165768 1.0775476 0.23010552] Sparsity at: 0.5116013948497854 Epoch 311/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5135e-09 - accuracy: 1.0000 - val_loss: 0.3593 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.16606 1.0778714 0.23012757] Sparsity at: 0.5116013948497854 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 8.4519e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1663413 1.0781776 0.23012309] Sparsity at: 0.5116013948497854 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3427e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1666454 1.0784657 0.2301197 ] Sparsity at: 0.5116013948497854 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2652e-09 - accuracy: 1.0000 - val_loss: 0.3596 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1669067 1.0787494 0.23011936] Sparsity at: 0.5116013948497854 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 8.1937e-09 - accuracy: 1.0000 - val_loss: 0.3594 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.167183 1.0790354 0.23013493] Sparsity at: 0.5116013948497854 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 8.1460e-09 - accuracy: 1.0000 - val_loss: 0.3596 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1674542 1.0793331 0.23012003] Sparsity at: 0.5116013948497854 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0665e-09 - accuracy: 1.0000 - val_loss: 0.3597 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1677039 1.0795984 0.23011766] Sparsity at: 0.5116013948497854 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0287e-09 - accuracy: 1.0000 - val_loss: 0.3597 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1679814 1.0798817 0.23008497] Sparsity at: 0.5116013948497854 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9453e-09 - accuracy: 1.0000 - val_loss: 0.3597 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1682395 1.0801301 0.23010512] Sparsity at: 0.5116013948497854 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8777e-09 - accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1685014 1.0804079 0.23010372] Sparsity at: 0.5116013948497854 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8241e-09 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.168765 1.0806571 0.23010702] Sparsity at: 0.5116013948497854 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7287e-09 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.169017 1.0808926 0.23012121] Sparsity at: 0.5116013948497854 Epoch 323/500 235/235 [==============================] - 2s 9ms/step - loss: 7.6890e-09 - accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1692684 1.0811584 0.23013347] Sparsity at: 0.5116013948497854 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6214e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.169515 1.0814061 0.23012125] Sparsity at: 0.5116013948497854 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 7.5916e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1697769 1.0816578 0.23014799] Sparsity at: 0.5116013948497854 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5301e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1700308 1.0818783 0.23013666] Sparsity at: 0.5116013948497854 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4844e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1702859 1.0821365 0.23009565] Sparsity at: 0.5116013948497854 Epoch 328/500 235/235 [==============================] - 2s 10ms/step - loss: 7.4089e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1705389 1.0823873 0.23011215] Sparsity at: 0.5116013948497854 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 7.3393e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1707792 1.0826021 0.2301243 ] Sparsity at: 0.5116013948497854 Epoch 330/500 235/235 [==============================] - 2s 10ms/step - loss: 7.3234e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1710247 1.0828388 0.23013248] Sparsity at: 0.5116013948497854 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 7.2459e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1712635 1.0830818 0.23015608] Sparsity at: 0.5116013948497854 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 7.1883e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1714829 1.083314 0.23014392] Sparsity at: 0.5116013948497854 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 7.1526e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1717483 1.0835366 0.23013908] Sparsity at: 0.5116013948497854 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0532e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1720021 1.083758 0.23015961] Sparsity at: 0.5116013948497854 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 6.9896e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1722404 1.0839764 0.23014547] Sparsity at: 0.5116013948497854 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0254e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1724933 1.0842116 0.23015943] Sparsity at: 0.5116013948497854 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9638e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1727191 1.0844092 0.23015836] Sparsity at: 0.5116013948497854 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 6.8804e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1729624 1.0846213 0.23015898] Sparsity at: 0.5116013948497854 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8426e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1731757 1.0848234 0.23015562] Sparsity at: 0.5116013948497854 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8128e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.173391 1.0850374 0.23016551] Sparsity at: 0.5116013948497854 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7234e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1735957 1.085208 0.23016433] Sparsity at: 0.5116013948497854 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6916e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1738055 1.0853877 0.23018746] Sparsity at: 0.5116013948497854 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6241e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1740375 1.0855465 0.23017073] Sparsity at: 0.5116013948497854 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5843e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1742513 1.0857666 0.23018725] Sparsity at: 0.5116013948497854 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5049e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1744566 1.0859461 0.23016134] Sparsity at: 0.5116013948497854 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5108e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1746689 1.086118 0.23016985] Sparsity at: 0.5116013948497854 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4214e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1748586 1.0862857 0.23013805] Sparsity at: 0.5116013948497854 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 6.4492e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1750917 1.0864719 0.23011047] Sparsity at: 0.5116013948497854 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3837e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1752926 1.0866395 0.23011269] Sparsity at: 0.5116013948497854 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3499e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1754985 1.086781 0.23008804] Sparsity at: 0.5116013948497854 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.6985140141575528 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.9118689464259191 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 2.2427521612542023 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 45s 7ms/step - loss: 6.3022e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9703 [ 0.08002246 0. -0.07110295 ... 1.1756686 1.086932 0.23009032] Sparsity at: 0.5116013948497854 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 6.2625e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1758631 1.0870941 0.23010021] Sparsity at: 0.5116013948497854 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2188e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1760731 1.0872737 0.23007955] Sparsity at: 0.5116013948497854 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1731e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1762825 1.0874262 0.23004282] Sparsity at: 0.5116013948497854 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1552e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1764884 1.0876025 0.23002936] Sparsity at: 0.5116013948497854 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0916e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.176682 1.0877767 0.22998 ] Sparsity at: 0.5116013948497854 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0757e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1768727 1.0879391 0.22997256] Sparsity at: 0.5116013948497854 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0558e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1770712 1.088086 0.22995155] Sparsity at: 0.5116013948497854 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9843e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1772169 1.0882255 0.22990614] Sparsity at: 0.5116013948497854 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 5.9625e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1774082 1.0883776 0.22988926] Sparsity at: 0.5116013948497854 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9326e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1775806 1.088529 0.22986946] Sparsity at: 0.5116013948497854 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9009e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.1777604 1.0886816 0.22983423] Sparsity at: 0.5116013948497854 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8532e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9704 [ 0.08002246 0. -0.07110295 ... 1.177925 1.0888423 0.2297981 ] Sparsity at: 0.5116013948497854 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 5.8115e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1780928 1.0889885 0.2297826 ] Sparsity at: 0.5116013948497854 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7975e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.178279 1.0891893 0.22976871] Sparsity at: 0.5116013948497854 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7161e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1784364 1.089288 0.22973473] Sparsity at: 0.5116013948497854 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7121e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1786097 1.0894145 0.22971025] Sparsity at: 0.5116013948497854 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6962e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1787666 1.0895773 0.22966279] Sparsity at: 0.5116013948497854 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6664e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1789384 1.0897207 0.22962297] Sparsity at: 0.5116013948497854 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5949e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1790723 1.0898528 0.22957551] Sparsity at: 0.5116013948497854 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5730e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1792345 1.0900047 0.22954822] Sparsity at: 0.5116013948497854 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5631e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1793876 1.0901343 0.22952662] Sparsity at: 0.5116013948497854 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5373e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1795535 1.0902618 0.22950345] Sparsity at: 0.5116013948497854 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5273e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1797327 1.0903895 0.2294904 ] Sparsity at: 0.5116013948497854 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5015e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1798908 1.090522 0.22947064] Sparsity at: 0.5116013948497854 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3883e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1800247 1.0906402 0.2294463 ] Sparsity at: 0.5116013948497854 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3883e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1801739 1.0907829 0.22942409] Sparsity at: 0.5116013948497854 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3763e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1803253 1.090896 0.22938801] Sparsity at: 0.5116013948497854 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3922e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9705 [ 0.08002246 0. -0.07110295 ... 1.1804901 1.0910399 0.2293874 ] Sparsity at: 0.5116013948497854 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3028e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1806077 1.0911883 0.2293565 ] Sparsity at: 0.5116013948497854 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 5.2651e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1807554 1.0913001 0.22934125] Sparsity at: 0.5116013948497854 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2253e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1808928 1.0914189 0.2293167 ] Sparsity at: 0.5116013948497854 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2333e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1810277 1.091544 0.22930881] Sparsity at: 0.5116013948497854 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1876e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1811461 1.0916592 0.22930853] Sparsity at: 0.5116013948497854 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1637e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1812891 1.0917836 0.22929932] Sparsity at: 0.5116013948497854 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 5.1379e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1814337 1.091906 0.22928692] Sparsity at: 0.5116013948497854 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1379e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1815466 1.0920157 0.22926933] Sparsity at: 0.5116013948497854 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0962e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1816723 1.0921186 0.2292446 ] Sparsity at: 0.5116013948497854 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0803e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1818118 1.0922443 0.22923039] Sparsity at: 0.5116013948497854 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0684e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1819423 1.0923587 0.22922897] Sparsity at: 0.5116013948497854 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9909e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1820474 1.0924473 0.22919482] Sparsity at: 0.5116013948497854 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0068e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.182177 1.0925411 0.22916345] Sparsity at: 0.5116013948497854 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9770e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.182284 1.0926626 0.22914757] Sparsity at: 0.5116013948497854 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9253e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1824272 1.092766 0.22911125] Sparsity at: 0.5116013948497854 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9531e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1825545 1.092882 0.22909163] Sparsity at: 0.5116013948497854 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8916e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1826692 1.0930057 0.22906953] Sparsity at: 0.5116013948497854 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8856e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.182783 1.0931149 0.22906645] Sparsity at: 0.5116013948497854 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8498e-09 - accuracy: 1.0000 - val_loss: 0.3612 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1828873 1.0931897 0.22905661] Sparsity at: 0.5116013948497854 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8161e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1829892 1.0932798 0.2290029 ] Sparsity at: 0.5116013948497854 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8081e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1831163 1.093383 0.22900146] Sparsity at: 0.5116013948497854 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.7700023260681235 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 0. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 0. 0. ... 0. 1. 1.] ... [0. 0. 1. ... 1. 1. 1.] [0. 0. 1. ... 0. 0. 0.] [0. 0. 1. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.9622702994533086 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 1. ... 0. 0. 0.] [0. 0. 0. ... 1. 0. 1.] [0. 1. 0. ... 0. 0. 0.] ... [0. 1. 0. ... 0. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 2.3538345517443133 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.10703125 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 0. ... 0. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 45s 7ms/step - loss: 4.8021e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1832502 1.093481 0.2289683 ] Sparsity at: 0.5116013948497854 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 4.8280e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1833553 1.0935773 0.2289609 ] Sparsity at: 0.5116013948497854 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7723e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1834666 1.0936794 0.22891815] Sparsity at: 0.5116013948497854 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6949e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1835827 1.093777 0.22885376] Sparsity at: 0.5116013948497854 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6889e-09 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1836919 1.0938524 0.22885163] Sparsity at: 0.5116013948497854 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6293e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1837903 1.0939678 0.22879697] Sparsity at: 0.5116013948497854 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6810e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1838828 1.0940553 0.22876023] Sparsity at: 0.5116013948497854 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6035e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1839659 1.0941321 0.22870722] Sparsity at: 0.5116013948497854 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6293e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1840618 1.0942456 0.2286833 ] Sparsity at: 0.5116013948497854 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6035e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1841618 1.0943286 0.2286565 ] Sparsity at: 0.5116013948497854 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5776e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1842655 1.0944079 0.22860484] Sparsity at: 0.5116013948497854 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5061e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1843518 1.0944543 0.22857326] Sparsity at: 0.5116013948497854 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5955e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1844593 1.09454 0.22852835] Sparsity at: 0.5116013948497854 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4942e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1845502 1.0946108 0.22848126] Sparsity at: 0.5116013948497854 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 4.5220e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1846448 1.0946907 0.22844957] Sparsity at: 0.5116013948497854 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4723e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1847351 1.094755 0.22841242] Sparsity at: 0.5116013948497854 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4684e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1848236 1.0948414 0.22838114] Sparsity at: 0.5116013948497854 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4346e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1849036 1.0949341 0.22833762] Sparsity at: 0.5116013948497854 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3968e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1849825 1.0950074 0.22828466] Sparsity at: 0.5116013948497854 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4286e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1850585 1.0950658 0.2282313 ] Sparsity at: 0.5116013948497854 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3889e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1851456 1.0951359 0.22818317] Sparsity at: 0.5116013948497854 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3432e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1852196 1.095184 0.22812746] Sparsity at: 0.5116013948497854 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3690e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1853039 1.0952542 0.22809878] Sparsity at: 0.5116013948497854 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3710e-09 - accuracy: 1.0000 - val_loss: 0.3610 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1854053 1.0953146 0.22806346] Sparsity at: 0.5116013948497854 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3015e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1854706 1.0954005 0.2280058 ] Sparsity at: 0.5116013948497854 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3273e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.185539 1.0954604 0.22794688] Sparsity at: 0.5116013948497854 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2637e-09 - accuracy: 1.0000 - val_loss: 0.3609 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1856053 1.0955254 0.22790879] Sparsity at: 0.5116013948497854 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2359e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1856817 1.0955987 0.22786526] Sparsity at: 0.5116013948497854 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2657e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.185754 1.0956591 0.22780494] Sparsity at: 0.5116013948497854 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2299e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1858206 1.0957048 0.22776026] Sparsity at: 0.5116013948497854 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2180e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1858879 1.0957677 0.22771387] Sparsity at: 0.5116013948497854 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2061e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.185943 1.0958374 0.22766127] Sparsity at: 0.5116013948497854 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2121e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1860142 1.0958829 0.22758906] Sparsity at: 0.5116013948497854 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1842e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1860912 1.0959262 0.2275436 ] Sparsity at: 0.5116013948497854 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1445e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1861256 1.0960032 0.22748429] Sparsity at: 0.5116013948497854 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1286e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1861954 1.0960568 0.22743481] Sparsity at: 0.5116013948497854 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1584e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1862712 1.096132 0.22738832] Sparsity at: 0.5116013948497854 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1544e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1863238 1.0961661 0.2273484 ] Sparsity at: 0.5116013948497854 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0670e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1863686 1.0962348 0.2272747 ] Sparsity at: 0.5116013948497854 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1167e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1864234 1.0962626 0.22721869] Sparsity at: 0.5116013948497854 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0531e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1864911 1.0963213 0.22715443] Sparsity at: 0.5116013948497854 Epoch 442/500 235/235 [==============================] - 2s 10ms/step - loss: 4.0571e-09 - accuracy: 1.0000 - val_loss: 0.3608 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1865233 1.0963441 0.22711495] Sparsity at: 0.5116013948497854 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0213e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1865418 1.0963957 0.22703804] Sparsity at: 0.5116013948497854 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 4.0154e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1865777 1.0964448 0.22699466] Sparsity at: 0.5116013948497854 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9776e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1866338 1.0965048 0.22691755] Sparsity at: 0.5116013948497854 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 3.9915e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1866858 1.0965532 0.22686228] Sparsity at: 0.5116013948497854 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9796e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1867331 1.0966113 0.22678454] Sparsity at: 0.5116013948497854 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9518e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1867758 1.0966474 0.22671086] Sparsity at: 0.5116013948497854 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9081e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1868267 1.096685 0.22666039] Sparsity at: 0.5116013948497854 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9359e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1868615 1.0967233 0.22658953] Sparsity at: 0.5116013948497854 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8942e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1869019 1.0967691 0.22651814] Sparsity at: 0.5116013948497854 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9220e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1869365 1.0967869 0.22645849] Sparsity at: 0.5116013948497854 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8882e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1869774 1.0967975 0.22639075] Sparsity at: 0.5116013948497854 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8763e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1870223 1.0968598 0.22631685] Sparsity at: 0.5116013948497854 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8683e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.18705 1.0969089 0.22625887] Sparsity at: 0.5116013948497854 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8683e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1870877 1.0969284 0.22620864] Sparsity at: 0.5116013948497854 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7968e-09 - accuracy: 1.0000 - val_loss: 0.3607 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.187123 1.0969594 0.22611208] Sparsity at: 0.5116013948497854 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8306e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1871573 1.0970007 0.22604588] Sparsity at: 0.5116013948497854 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7988e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1871676 1.0970674 0.22596222] Sparsity at: 0.5116013948497854 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8147e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1872003 1.0970823 0.225897 ] Sparsity at: 0.5116013948497854 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7928e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1872361 1.0971226 0.22583555] Sparsity at: 0.5116013948497854 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7909e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1872604 1.0971763 0.22576582] Sparsity at: 0.5116013948497854 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7670e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.187282 1.0972104 0.22567806] Sparsity at: 0.5116013948497854 Epoch 464/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7352e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1873018 1.0972477 0.22561674] Sparsity at: 0.5116013948497854 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7690e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1873215 1.0972822 0.22554098] Sparsity at: 0.5116013948497854 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7352e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.187341 1.0973063 0.2254758 ] Sparsity at: 0.5116013948497854 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7352e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1873634 1.0973277 0.2254055 ] Sparsity at: 0.5116013948497854 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6875e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1873848 1.0973638 0.22533146] Sparsity at: 0.5116013948497854 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6915e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1874093 1.0973834 0.22526506] Sparsity at: 0.5116013948497854 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6935e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1874173 1.0974022 0.2251915 ] Sparsity at: 0.5116013948497854 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6975e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1874381 1.0974154 0.22511114] Sparsity at: 0.5116013948497854 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6875e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1874311 1.0974928 0.2250421 ] Sparsity at: 0.5116013948497854 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5961e-09 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1874449 1.097501 0.2249779 ] Sparsity at: 0.5116013948497854 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6458e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9711 [ 0.08002246 0. -0.07110295 ... 1.1874728 1.0975285 0.22490689] Sparsity at: 0.5116013948497854 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6279e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1874907 1.0975664 0.22485054] Sparsity at: 0.5116013948497854 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6577e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1875062 1.0975842 0.22477673] Sparsity at: 0.5116013948497854 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5961e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9710 [ 0.08002246 0. -0.07110295 ... 1.1875216 1.0976083 0.22471678] Sparsity at: 0.5116013948497854 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6041e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1875678 1.097632 0.22464328] Sparsity at: 0.5116013948497854 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5822e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1875643 1.0976611 0.22458616] Sparsity at: 0.5116013948497854 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5644e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1875792 1.0977179 0.22451063] Sparsity at: 0.5116013948497854 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5445e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1875803 1.0977219 0.22443463] Sparsity at: 0.5116013948497854 Epoch 482/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5842e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1875671 1.0977522 0.22435221] Sparsity at: 0.5116013948497854 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5842e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1875701 1.0977567 0.22427812] Sparsity at: 0.5116013948497854 Epoch 484/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5226e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1875992 1.0977837 0.22418645] Sparsity at: 0.5116013948497854 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5604e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1876097 1.0978056 0.22412522] Sparsity at: 0.5116013948497854 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5306e-09 - accuracy: 1.0000 - val_loss: 0.3603 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1876284 1.097843 0.22404796] Sparsity at: 0.5116013948497854 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5087e-09 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9709 [ 0.08002246 0. -0.07110295 ... 1.1876315 1.0978713 0.2239542 ] Sparsity at: 0.5116013948497854 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4630e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1876422 1.0978981 0.22387853] Sparsity at: 0.5116013948497854 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5246e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9706 [ 0.08002246 0. -0.07110295 ... 1.1876338 1.0979394 0.2238097 ] Sparsity at: 0.5116013948497854 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5048e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1876572 1.0979918 0.22371836] Sparsity at: 0.5116013948497854 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 3.5187e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1876634 1.0979987 0.22366638] Sparsity at: 0.5116013948497854 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4432e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1876688 1.0980256 0.22361137] Sparsity at: 0.5116013948497854 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4392e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1876969 1.0980332 0.22353478] Sparsity at: 0.5116013948497854 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4432e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9708 [ 0.08002246 0. -0.07110295 ... 1.1876934 1.0980425 0.22345057] Sparsity at: 0.5116013948497854 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4412e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.187698 1.0980881 0.22334658] Sparsity at: 0.5116013948497854 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4829e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.187698 1.0981208 0.223247 ] Sparsity at: 0.5116013948497854 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4332e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1876937 1.098148 0.2231615 ] Sparsity at: 0.5116013948497854 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4372e-09 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1876825 1.098143 0.22311288] Sparsity at: 0.5116013948497854 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4153e-09 - accuracy: 1.0000 - val_loss: 0.3600 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1877174 1.0981876 0.22300586] Sparsity at: 0.5116013948497854 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4153e-09 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.9707 [ 0.08002246 0. -0.07110295 ... 1.1877176 1.098194 0.22296345] Sparsity at: 0.5116013948497854 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 0.1405 - accuracy: 0.9774 - val_loss: 0.1938 - val_accuracy: 0.9614 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.5819893e-02 2.5809383e-02] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9781 - val_loss: 0.1865 - val_accuracy: 0.9656 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.7460165e-02 2.6834970e-02] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.1857 - val_accuracy: 0.9668 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.4778254e-02 2.9583558e-02] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9788 - val_loss: 0.1815 - val_accuracy: 0.9680 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.3779586e-02 1.9106163e-02] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1358 - accuracy: 0.9793 - val_loss: 0.1864 - val_accuracy: 0.9642 [ 0.000000e+00 -3.575435e-34 0.000000e+00 ... 0.000000e+00 -4.446961e-02 2.707614e-02] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9791 - val_loss: 0.1901 - val_accuracy: 0.9681 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.9915849e-02 3.1123517e-02] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9791 - val_loss: 0.1888 - val_accuracy: 0.9642 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.9868724e-02 2.9073132e-02] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9793 - val_loss: 0.1920 - val_accuracy: 0.9657 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.7668787e-02 2.6284816e-02] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9794 - val_loss: 0.1946 - val_accuracy: 0.9637 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.4260429e-02 2.8926058e-02] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9783 - val_loss: 0.2052 - val_accuracy: 0.9601 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.6977250e-02 2.2327606e-02] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1392 - accuracy: 0.9788 - val_loss: 0.1967 - val_accuracy: 0.9619 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.4309519e-02 2.2324832e-02] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1386 - accuracy: 0.9792 - val_loss: 0.2114 - val_accuracy: 0.9578 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.8307283e-02 1.1967044e-02] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9800 - val_loss: 0.2316 - val_accuracy: 0.9536 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.2278005e-02 1.6850524e-02] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9799 - val_loss: 0.2132 - val_accuracy: 0.9584 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... -0.0000000e+00 -4.9926020e-02 1.4892332e-02] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9787 - val_loss: 0.1958 - val_accuracy: 0.9618 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.3949567e-02 1.6402822e-02] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9798 - val_loss: 0.1872 - val_accuracy: 0.9629 [ 0.000000e+00 -3.575435e-34 0.000000e+00 ... 0.000000e+00 -5.902245e-02 9.148761e-03] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9785 - val_loss: 0.1979 - val_accuracy: 0.9636 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.8546914e-02 7.1059274e-03] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9791 - val_loss: 0.2188 - val_accuracy: 0.9561 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.2030463e-02 1.6246798e-02] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9796 - val_loss: 0.1875 - val_accuracy: 0.9659 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.8732214e-02 1.3158396e-02] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9791 - val_loss: 0.1929 - val_accuracy: 0.9647 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.3758623e-02 1.7782262e-02] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1378 - accuracy: 0.9796 - val_loss: 0.1810 - val_accuracy: 0.9660 [ 0.000000e+00 -3.575435e-34 0.000000e+00 ... 0.000000e+00 -4.331368e-02 1.785288e-02] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9801 - val_loss: 0.1847 - val_accuracy: 0.9669 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.3441677e-02 1.7161423e-02] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1420 - accuracy: 0.9781 - val_loss: 0.1999 - val_accuracy: 0.9634 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.9332183e-02 2.0365538e-02] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9797 - val_loss: 0.2042 - val_accuracy: 0.9610 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.3669270e-02 1.9014565e-02] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1407 - accuracy: 0.9785 - val_loss: 0.1851 - val_accuracy: 0.9646 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.7365466e-02 2.2156268e-02] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9797 - val_loss: 0.2105 - val_accuracy: 0.9597 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.5485448e-02 2.2151986e-02] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9781 - val_loss: 0.2149 - val_accuracy: 0.9583 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.8103373e-02 2.5436182e-02] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9784 - val_loss: 0.2059 - val_accuracy: 0.9623 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.1290769e-02 2.4999343e-02] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9795 - val_loss: 0.2014 - val_accuracy: 0.9587 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.5503071e-02 1.6207779e-02] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9796 - val_loss: 0.1819 - val_accuracy: 0.9647 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.0592499e-02 1.9432642e-02] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9784 - val_loss: 0.1789 - val_accuracy: 0.9671 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.8934489e-02 2.2640448e-02] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9791 - val_loss: 0.1934 - val_accuracy: 0.9630 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -5.0750870e-02 2.5195653e-02] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2209 - val_accuracy: 0.9562 [ 0.000000e+00 -3.575435e-34 0.000000e+00 ... 0.000000e+00 -3.676104e-02 1.438975e-02] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1390 - accuracy: 0.9781 - val_loss: 0.2010 - val_accuracy: 0.9615 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.0574357e-02 2.9619228e-02] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9790 - val_loss: 0.2156 - val_accuracy: 0.9590 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.5800462e-02 2.0529052e-02] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9800 - val_loss: 0.2169 - val_accuracy: 0.9560 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.3138053e-02 1.8827004e-02] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9790 - val_loss: 0.2006 - val_accuracy: 0.9603 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.4723384e-02 1.7102208e-02] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9792 - val_loss: 0.1888 - val_accuracy: 0.9635 [ 0.000000e+00 -3.575435e-34 0.000000e+00 ... 0.000000e+00 -5.657761e-02 1.612301e-02] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.2193 - val_accuracy: 0.9607 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... -0.0000000e+00 -7.1078889e-02 1.5133626e-02] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.2171 - val_accuracy: 0.9583 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.3893430e-02 2.6169395e-02] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1389 - accuracy: 0.9790 - val_loss: 0.1999 - val_accuracy: 0.9623 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.1034408e-02 3.1785730e-02] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9793 - val_loss: 0.2269 - val_accuracy: 0.9573 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.2622484e-02 2.5180925e-02] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9785 - val_loss: 0.2040 - val_accuracy: 0.9605 [ 0.000000e+00 -3.575435e-34 0.000000e+00 ... 0.000000e+00 -3.352122e-02 2.172237e-02] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9790 - val_loss: 0.2299 - val_accuracy: 0.9552 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.7076078e-02 1.9145414e-02] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9793 - val_loss: 0.2083 - val_accuracy: 0.9601 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.0090825e-02 1.7505517e-02] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9800 - val_loss: 0.2244 - val_accuracy: 0.9572 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -4.1549686e-02 1.7978881e-02] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9787 - val_loss: 0.2073 - val_accuracy: 0.9623 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.6542475e-02 2.3841660e-02] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.2406 - val_accuracy: 0.9491 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... -0.0000000e+00 -4.5478377e-02 1.3162736e-02] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9796 - val_loss: 0.2118 - val_accuracy: 0.9566 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -3.6596384e-02 2.0668095e-02] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9786 - val_loss: 0.1957 - val_accuracy: 0.9619 [ 0.0000000e+00 -3.5754350e-34 0.0000000e+00 ... 0.0000000e+00 -2.6196167e-02 2.0447779e-02] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9787 - val_loss: 0.1797 - val_accuracy: 0.9661 [ 0. 0. 0. ... 0. -0.03608026 -0. ] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9795 - val_loss: 0.2000 - val_accuracy: 0.9604 [ 0. 0. 0. ... -0. -0.02991254 -0. ] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9789 - val_loss: 0.1848 - val_accuracy: 0.9659 [ 0. 0. 0. ... 0. -0.03709638 -0. ] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1336 - accuracy: 0.9801 - val_loss: 0.2359 - val_accuracy: 0.9513 [ 0. 0. 0. ... 0. -0.04022135 -0. ] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1372 - accuracy: 0.9786 - val_loss: 0.1925 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. -0.04887099 -0. ] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.2083 - val_accuracy: 0.9584 [ 0. 0. 0. ... 0. -0.04775592 -0. ] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9797 - val_loss: 0.2096 - val_accuracy: 0.9582 [ 0. 0. 0. ... 0. -0.03270503 -0. ] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9797 - val_loss: 0.1884 - val_accuracy: 0.9633 [ 0. 0. 0. ... 0. -0.04266148 -0. ] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9787 - val_loss: 0.1872 - val_accuracy: 0.9649 [ 0. 0. 0. ... 0. -0.03778715 -0. ] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9802 - val_loss: 0.1939 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0.04031143 -0. ] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.1835 - val_accuracy: 0.9675 [ 0. 0. 0. ... 0. -0.03868223 -0. ] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9790 - val_loss: 0.1856 - val_accuracy: 0.9668 [ 0. 0. 0. ... 0. -0.05343734 -0. ] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9798 - val_loss: 0.2142 - val_accuracy: 0.9558 [ 0. 0. 0. ... 0. -0.03885864 -0. ] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9791 - val_loss: 0.1838 - val_accuracy: 0.9679 [ 0. 0. 0. ... 0. -0.04641924 -0. ] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1361 - accuracy: 0.9793 - val_loss: 0.2095 - val_accuracy: 0.9574 [ 0. 0. 0. ... -0. -0.04290385 -0. ] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9793 - val_loss: 0.1787 - val_accuracy: 0.9683 [ 0. 0. 0. ... 0. -0.04435262 -0. ] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9798 - val_loss: 0.2015 - val_accuracy: 0.9607 [ 0. 0. 0. ... 0. -0.04105549 -0. ] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9790 - val_loss: 0.1848 - val_accuracy: 0.9652 [ 0. 0. 0. ... 0. -0.05309426 -0. ] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9787 - val_loss: 0.1961 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. -0.04534542 -0. ] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9801 - val_loss: 0.1926 - val_accuracy: 0.9626 [ 0. 0. 0. ... 0. -0.04777248 -0. ] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9794 - val_loss: 0.1952 - val_accuracy: 0.9630 [ 0. 0. 0. ... 0. -0.05099372 -0. ] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9781 - val_loss: 0.2035 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. -0.05138997 -0. ] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9793 - val_loss: 0.1910 - val_accuracy: 0.9633 [ 0. 0. 0. ... -0. -0.04545542 0. ] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9793 - val_loss: 0.1896 - val_accuracy: 0.9652 [ 0. 0. 0. ... 0. -0.03235805 -0. ] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9790 - val_loss: 0.1780 - val_accuracy: 0.9657 [ 0. 0. 0. ... 0. -0.0392405 -0. ] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9785 - val_loss: 0.1835 - val_accuracy: 0.9677 [ 0. 0. 0. ... 0. -0.0377299 -0. ] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9796 - val_loss: 0.2122 - val_accuracy: 0.9574 [ 0. 0. 0. ... -0. -0.05849489 -0. ] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9797 - val_loss: 0.2065 - val_accuracy: 0.9612 [ 0. 0. 0. ... 0. -0.04537514 -0. ] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9792 - val_loss: 0.1861 - val_accuracy: 0.9661 [ 0. 0. 0. ... 0. -0.0388717 -0. ] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.1965 - val_accuracy: 0.9615 [ 0. 0. 0. ... 0. -0.04702219 -0. ] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9792 - val_loss: 0.2077 - val_accuracy: 0.9578 [ 0. 0. 0. ... 0. -0.06113814 0. ] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9783 - val_loss: 0.1932 - val_accuracy: 0.9652 [ 0. 0. 0. ... 0. -0.05584861 -0. ] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9791 - val_loss: 0.2110 - val_accuracy: 0.9587 [ 0. 0. 0. ... 0. -0.04865564 -0. ] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9789 - val_loss: 0.1959 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0.03944255 -0. ] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9793 - val_loss: 0.1935 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. -0.04088381 -0. ] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9785 - val_loss: 0.2057 - val_accuracy: 0.9583 [ 0. 0. 0. ... 0. -0.0348785 -0. ] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9796 - val_loss: 0.1990 - val_accuracy: 0.9601 [ 0. 0. 0. ... 0. -0.0341835 -0. ] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9805 - val_loss: 0.1976 - val_accuracy: 0.9607 [ 0. 0. 0. ... 0. -0.02939931 -0. ] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9780 - val_loss: 0.2028 - val_accuracy: 0.9613 [ 0. 0. 0. ... 0. -0.03214215 -0. ] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9791 - val_loss: 0.1754 - val_accuracy: 0.9688 [ 0. 0. 0. ... 0. -0.03378633 -0. ] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1905 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. -0.03156013 -0. ] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.1734 - val_accuracy: 0.9687 [ 0. 0. 0. ... 0. -0.03930213 -0. ] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9783 - val_loss: 0.2202 - val_accuracy: 0.9567 [ 0. 0. 0. ... 0. -0.03493787 -0. ] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.1716 - val_accuracy: 0.9683 [ 0. 0. 0. ... 0. -0.03642095 0. ] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9794 - val_loss: 0.1912 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0.04021035 -0. ] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.1959 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0.03334786 -0. ] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9789 - val_loss: 0.1876 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. -0.03569957 -0. ] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9797 - val_loss: 0.1848 - val_accuracy: 0.9661 [ 0. 0. 0. ... -0. -0.03729901 -0. ] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9798 - val_loss: 0.2041 - val_accuracy: 0.9608 [ 0. 0. 0. ... 0. -0.03978111 -0. ] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9788 - val_loss: 0.1757 - val_accuracy: 0.9668 [ 0. 0. 0. ... 0. -0.04269115 -0. ] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1372 - accuracy: 0.9789 - val_loss: 0.2182 - val_accuracy: 0.9569 [ 0. 0. 0. ... 0. -0.03211332 -0. ] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.1917 - val_accuracy: 0.9627 [ 0. 0. 0. ... 0. -0.03584477 -0. ] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9779 - val_loss: 0.2213 - val_accuracy: 0.9544 [ 0. 0. 0. ... -0. -0.02591021 -0. ] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9796 - val_loss: 0.2415 - val_accuracy: 0.9498 [ 0. 0. 0. ... 0. -0.03363626 -0. ] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9794 - val_loss: 0.1966 - val_accuracy: 0.9627 [ 0. 0. 0. ... 0. -0.0387113 -0. ] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9804 - val_loss: 0.2501 - val_accuracy: 0.9478 [ 0. 0. 0. ... -0. -0.03638672 -0. ] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9792 - val_loss: 0.1966 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. -0.04177481 -0. ] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9795 - val_loss: 0.2203 - val_accuracy: 0.9543 [ 0. 0. 0. ... 0. -0.04567624 -0. ] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9798 - val_loss: 0.1739 - val_accuracy: 0.9687 [ 0. 0. 0. ... 0. -0.04710067 -0. ] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9778 - val_loss: 0.2175 - val_accuracy: 0.9576 [ 0. 0. 0. ... -0. -0.04480966 -0. ] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9794 - val_loss: 0.2074 - val_accuracy: 0.9607 [ 0. 0. 0. ... -0. -0.03574139 -0. ] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9785 - val_loss: 0.1848 - val_accuracy: 0.9657 [ 0. 0. 0. ... 0. -0.03368543 -0. ] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9794 - val_loss: 0.2126 - val_accuracy: 0.9561 [ 0. 0. 0. ... 0. -0.03206794 -0. ] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9790 - val_loss: 0.2100 - val_accuracy: 0.9585 [ 0. 0. 0. ... 0. -0.02995818 -0. ] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9797 - val_loss: 0.2009 - val_accuracy: 0.9599 [ 0. 0. 0. ... -0. -0.03515729 -0. ] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.2321 - val_accuracy: 0.9486 [ 0. 0. 0. ... 0. -0.03244941 -0. ] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9783 - val_loss: 0.1979 - val_accuracy: 0.9612 [ 0. 0. 0. ... 0. -0.03633303 -0. ] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9789 - val_loss: 0.2059 - val_accuracy: 0.9591 [ 0. 0. 0. ... 0. -0.02373817 -0. ] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9794 - val_loss: 0.1959 - val_accuracy: 0.9618 [ 0. 0. 0. ... 0. -0.03560742 0. ] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9785 - val_loss: 0.1910 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0.02764485 -0. ] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9786 - val_loss: 0.2353 - val_accuracy: 0.9512 [ 0. 0. 0. ... 0. -0.0250035 -0. ] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9783 - val_loss: 0.2209 - val_accuracy: 0.9521 [ 0. 0. 0. ... 0. -0.02616629 -0. ] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9789 - val_loss: 0.2300 - val_accuracy: 0.9536 [ 0. 0. 0. ... 0. -0.0341068 -0. ] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9777 - val_loss: 0.1774 - val_accuracy: 0.9673 [ 0. 0. 0. ... 0. -0.04064483 -0. ] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1298 - accuracy: 0.9805 - val_loss: 0.1971 - val_accuracy: 0.9617 [ 0. 0. 0. ... 0. -0.03400218 -0. ] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.2211 - val_accuracy: 0.9533 [ 0. 0. 0. ... 0. -0.03219787 -0. ] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9798 - val_loss: 0.2371 - val_accuracy: 0.9542 [ 0. 0. 0. ... -0. -0.02586298 -0. ] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9795 - val_loss: 0.2240 - val_accuracy: 0.9563 [ 0. 0. 0. ... 0. -0.02731429 -0. ] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9796 - val_loss: 0.2160 - val_accuracy: 0.9579 [ 0. 0. 0. ... -0. -0.03156611 -0. ] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9783 - val_loss: 0.2460 - val_accuracy: 0.9499 [ 0. 0. 0. ... -0. -0.03899993 -0. ] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9803 - val_loss: 0.1911 - val_accuracy: 0.9615 [ 0. 0. 0. ... 0. -0.03075077 -0. ] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9793 - val_loss: 0.2021 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0.0322665 -0. ] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9800 - val_loss: 0.2052 - val_accuracy: 0.9574 [ 0. 0. 0. ... 0. -0.04178954 -0. ] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9787 - val_loss: 0.2113 - val_accuracy: 0.9585 [ 0. 0. 0. ... 0. -0.03717574 -0. ] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9793 - val_loss: 0.1982 - val_accuracy: 0.9617 [ 0. 0. 0. ... 0. -0.03805895 -0. ] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9790 - val_loss: 0.2162 - val_accuracy: 0.9583 [ 0. 0. 0. ... -0. -0.0292711 -0. ] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9798 - val_loss: 0.2060 - val_accuracy: 0.9614 [ 0. 0. 0. ... 0. -0.03683012 -0. ] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9793 - val_loss: 0.1868 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0.03066598 0. ] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9788 - val_loss: 0.1814 - val_accuracy: 0.9651 [ 0. 0. 0. ... 0. -0.03452725 -0. ] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9791 - val_loss: 0.2084 - val_accuracy: 0.9584 [ 0. 0. 0. ... 0. -0.03986276 -0. ] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9792 - val_loss: 0.1961 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. -0.03473964 -0. ] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9798 - val_loss: 0.2055 - val_accuracy: 0.9593 [ 0. 0. 0. ... 0. -0.03745791 -0. ] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9790 - val_loss: 0.2808 - val_accuracy: 0.9366 [ 0. 0. 0. ... 0. -0.03350218 -0. ] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9786 - val_loss: 0.1952 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0.03314424 -0. ] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1310 - accuracy: 0.9802 - val_loss: 0.2308 - val_accuracy: 0.9516 [ 0. 0. 0. ... 0. -0.03100204 -0. ] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9778 - val_loss: 0.1926 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. -0.03731347 0. ] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9797 - val_loss: 0.2133 - val_accuracy: 0.9567 [ 0. 0. 0. ... 0. -0.03904126 -0. ] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9788 - val_loss: 0.1906 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0.02536202 -0. ] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9791 - val_loss: 0.2044 - val_accuracy: 0.9596 [ 0. 0. 0. ... 0. -0.02155666 -0. ] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1371 - accuracy: 0.9778 - val_loss: 0.1979 - val_accuracy: 0.9621 [ 0. 0. 0. ... 0. -0.03493988 -0. ] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1363 - accuracy: 0.9783 - val_loss: 0.1871 - val_accuracy: 0.9650 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9774 - val_loss: 0.2038 - val_accuracy: 0.9574 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1397 - accuracy: 0.9773 - val_loss: 0.1965 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1345 - accuracy: 0.9785 - val_loss: 0.2090 - val_accuracy: 0.9583 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1342 - accuracy: 0.9785 - val_loss: 0.2674 - val_accuracy: 0.9458 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1367 - accuracy: 0.9781 - val_loss: 0.1965 - val_accuracy: 0.9622 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1338 - accuracy: 0.9794 - val_loss: 0.2147 - val_accuracy: 0.9575 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9790 - val_loss: 0.1858 - val_accuracy: 0.9643 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9786 - val_loss: 0.2037 - val_accuracy: 0.9587 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9786 - val_loss: 0.1873 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1314 - accuracy: 0.9793 - val_loss: 0.2040 - val_accuracy: 0.9605 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9798 - val_loss: 0.2254 - val_accuracy: 0.9542 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9774 - val_loss: 0.2194 - val_accuracy: 0.9547 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9793 - val_loss: 0.2154 - val_accuracy: 0.9544 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9783 - val_loss: 0.1916 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9780 - val_loss: 0.2049 - val_accuracy: 0.9597 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9786 - val_loss: 0.1868 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9785 - val_loss: 0.2415 - val_accuracy: 0.9475 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9778 - val_loss: 0.1888 - val_accuracy: 0.9640 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.1804 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9791 - val_loss: 0.2061 - val_accuracy: 0.9568 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9786 - val_loss: 0.1819 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9791 - val_loss: 0.1818 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9784 - val_loss: 0.1927 - val_accuracy: 0.9630 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9783 - val_loss: 0.2193 - val_accuracy: 0.9571 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9793 - val_loss: 0.1863 - val_accuracy: 0.9633 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9795 - val_loss: 0.1920 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9780 - val_loss: 0.2024 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9792 - val_loss: 0.2141 - val_accuracy: 0.9596 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9799 - val_loss: 0.2036 - val_accuracy: 0.9578 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9794 - val_loss: 0.2201 - val_accuracy: 0.9560 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9792 - val_loss: 0.2141 - val_accuracy: 0.9556 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9797 - val_loss: 0.1840 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1394 - accuracy: 0.9776 - val_loss: 0.1955 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9785 - val_loss: 0.2150 - val_accuracy: 0.9571 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9783 - val_loss: 0.2048 - val_accuracy: 0.9615 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9791 - val_loss: 0.1938 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9770 - val_loss: 0.1926 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9799 - val_loss: 0.2148 - val_accuracy: 0.9550 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9792 - val_loss: 0.2084 - val_accuracy: 0.9592 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9793 - val_loss: 0.1996 - val_accuracy: 0.9594 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9795 - val_loss: 0.1972 - val_accuracy: 0.9605 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9791 - val_loss: 0.1891 - val_accuracy: 0.9622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9784 - val_loss: 0.2094 - val_accuracy: 0.9616 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9793 - val_loss: 0.2118 - val_accuracy: 0.9584 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9783 - val_loss: 0.2006 - val_accuracy: 0.9602 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9779 - val_loss: 0.2149 - val_accuracy: 0.9591 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9789 - val_loss: 0.2114 - val_accuracy: 0.9582 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9790 - val_loss: 0.2149 - val_accuracy: 0.9564 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9780 - val_loss: 0.2099 - val_accuracy: 0.9563 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9783 - val_loss: 0.2080 - val_accuracy: 0.9577 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9773 - val_loss: 0.1891 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9771 - val_loss: 0.1957 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9778 - val_loss: 0.1961 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9778 - val_loss: 0.2207 - val_accuracy: 0.9558 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9787 - val_loss: 0.2044 - val_accuracy: 0.9605 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9780 - val_loss: 0.1942 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9789 - val_loss: 0.1985 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9783 - val_loss: 0.1946 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9780 - val_loss: 0.1821 - val_accuracy: 0.9654 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9785 - val_loss: 0.1865 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9786 - val_loss: 0.1856 - val_accuracy: 0.9663 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9789 - val_loss: 0.1978 - val_accuracy: 0.9619 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9789 - val_loss: 0.2084 - val_accuracy: 0.9567 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9782 - val_loss: 0.2067 - val_accuracy: 0.9608 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9786 - val_loss: 0.1974 - val_accuracy: 0.9588 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9785 - val_loss: 0.2064 - val_accuracy: 0.9606 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1329 - accuracy: 0.9786 - val_loss: 0.2592 - val_accuracy: 0.9454 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9795 - val_loss: 0.1821 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9779 - val_loss: 0.1882 - val_accuracy: 0.9624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9782 - val_loss: 0.2283 - val_accuracy: 0.9557 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9775 - val_loss: 0.1998 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9779 - val_loss: 0.2065 - val_accuracy: 0.9604 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9778 - val_loss: 0.1870 - val_accuracy: 0.9658 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9798 - val_loss: 0.1884 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9776 - val_loss: 0.1873 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9784 - val_loss: 0.2102 - val_accuracy: 0.9582 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9775 - val_loss: 0.1967 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9782 - val_loss: 0.1920 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9778 - val_loss: 0.1909 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9787 - val_loss: 0.1797 - val_accuracy: 0.9666 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1416 - accuracy: 0.9765 - val_loss: 0.1969 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9791 - val_loss: 0.1931 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9785 - val_loss: 0.1980 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9787 - val_loss: 0.2148 - val_accuracy: 0.9557 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9782 - val_loss: 0.1915 - val_accuracy: 0.9622 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9777 - val_loss: 0.2043 - val_accuracy: 0.9593 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9776 - val_loss: 0.1871 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9786 - val_loss: 0.1988 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9786 - val_loss: 0.1987 - val_accuracy: 0.9603 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9798 - val_loss: 0.1841 - val_accuracy: 0.9639 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9764 - val_loss: 0.1801 - val_accuracy: 0.9676 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9786 - val_loss: 0.2024 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9783 - val_loss: 0.1753 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9780 - val_loss: 0.2052 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9794 - val_loss: 0.2224 - val_accuracy: 0.9553 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9788 - val_loss: 0.1893 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1316 - accuracy: 0.9788 - val_loss: 0.2246 - val_accuracy: 0.9543 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9786 - val_loss: 0.2129 - val_accuracy: 0.9593 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9780 - val_loss: 0.1942 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9780 - val_loss: 0.1902 - val_accuracy: 0.9600 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1262 - accuracy: 0.9795 - val_loss: 0.1920 - val_accuracy: 0.9604 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9793 - val_loss: 0.1764 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1254 - accuracy: 0.9795 - val_loss: 0.1969 - val_accuracy: 0.9587 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9802 - val_loss: 0.1912 - val_accuracy: 0.9601 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9792 - val_loss: 0.1790 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9796 - val_loss: 0.1920 - val_accuracy: 0.9593 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9795 - val_loss: 0.2045 - val_accuracy: 0.9570 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9801 - val_loss: 0.1943 - val_accuracy: 0.9596 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9794 - val_loss: 0.1782 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9801 - val_loss: 0.2084 - val_accuracy: 0.9579 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9800 - val_loss: 0.2368 - val_accuracy: 0.9460 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9803 - val_loss: 0.1733 - val_accuracy: 0.9651 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9807 - val_loss: 0.2054 - val_accuracy: 0.9576 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9793 - val_loss: 0.2175 - val_accuracy: 0.9561 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9804 - val_loss: 0.1861 - val_accuracy: 0.9615 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1210 - accuracy: 0.9802 - val_loss: 0.1946 - val_accuracy: 0.9612 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9797 - val_loss: 0.1793 - val_accuracy: 0.9655 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9809 - val_loss: 0.1917 - val_accuracy: 0.9596 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9795 - val_loss: 0.1915 - val_accuracy: 0.9603 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9804 - val_loss: 0.2073 - val_accuracy: 0.9603 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1169 - accuracy: 0.9816 - val_loss: 0.2529 - val_accuracy: 0.9428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1197 - accuracy: 0.9807 - val_loss: 0.2158 - val_accuracy: 0.9547 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9805 - val_loss: 0.1940 - val_accuracy: 0.9583 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9810 - val_loss: 0.1911 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9792 - val_loss: 0.2041 - val_accuracy: 0.9588 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1189 - accuracy: 0.9806 - val_loss: 0.1746 - val_accuracy: 0.9680 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9802 - val_loss: 0.1841 - val_accuracy: 0.9616 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1184 - accuracy: 0.9801 - val_loss: 0.1844 - val_accuracy: 0.9623 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9805 - val_loss: 0.1836 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9797 - val_loss: 0.1930 - val_accuracy: 0.9616 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1161 - accuracy: 0.9817 - val_loss: 0.1745 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9801 - val_loss: 0.1795 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9807 - val_loss: 0.2256 - val_accuracy: 0.9507 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1159 - accuracy: 0.9811 - val_loss: 0.1929 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9797 - val_loss: 0.1795 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9799 - val_loss: 0.2031 - val_accuracy: 0.9616 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9797 - val_loss: 0.1770 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1173 - accuracy: 0.9809 - val_loss: 0.2066 - val_accuracy: 0.9562 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9803 - val_loss: 0.1821 - val_accuracy: 0.9625 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9804 - val_loss: 0.1829 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9805 - val_loss: 0.1684 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1162 - accuracy: 0.9815 - val_loss: 0.1753 - val_accuracy: 0.9655 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9803 - val_loss: 0.1803 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9810 - val_loss: 0.1952 - val_accuracy: 0.9587 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9801 - val_loss: 0.2023 - val_accuracy: 0.9551 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9803 - val_loss: 0.1832 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9814 - val_loss: 0.1921 - val_accuracy: 0.9641 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9801 - val_loss: 0.1888 - val_accuracy: 0.9616 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9797 - val_loss: 0.1832 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9767 - val_loss: 0.1649 - val_accuracy: 0.9662 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9787 - val_loss: 0.1694 - val_accuracy: 0.9643 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1141 - accuracy: 0.9795 - val_loss: 0.1862 - val_accuracy: 0.9606 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1120 - accuracy: 0.9801 - val_loss: 0.2026 - val_accuracy: 0.9553 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9808 - val_loss: 0.1745 - val_accuracy: 0.9622 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1105 - accuracy: 0.9798 - val_loss: 0.1898 - val_accuracy: 0.9599 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1068 - accuracy: 0.9812 - val_loss: 0.1956 - val_accuracy: 0.9562 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1076 - accuracy: 0.9805 - val_loss: 0.1894 - val_accuracy: 0.9574 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1062 - accuracy: 0.9813 - val_loss: 0.1926 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1083 - accuracy: 0.9806 - val_loss: 0.1790 - val_accuracy: 0.9613 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9808 - val_loss: 0.1857 - val_accuracy: 0.9608 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1068 - accuracy: 0.9805 - val_loss: 0.1712 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1055 - accuracy: 0.9811 - val_loss: 0.1772 - val_accuracy: 0.9601 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9815 - val_loss: 0.1831 - val_accuracy: 0.9578 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1057 - accuracy: 0.9814 - val_loss: 0.1695 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9811 - val_loss: 0.1794 - val_accuracy: 0.9615 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1034 - accuracy: 0.9821 - val_loss: 0.1680 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9812 - val_loss: 0.1622 - val_accuracy: 0.9664 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1040 - accuracy: 0.9813 - val_loss: 0.1810 - val_accuracy: 0.9597 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9816 - val_loss: 0.1793 - val_accuracy: 0.9611 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1051 - accuracy: 0.9812 - val_loss: 0.1654 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1027 - accuracy: 0.9817 - val_loss: 0.1631 - val_accuracy: 0.9657 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1077 - accuracy: 0.9804 - val_loss: 0.1672 - val_accuracy: 0.9665 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1035 - accuracy: 0.9816 - val_loss: 0.1668 - val_accuracy: 0.9645 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9819 - val_loss: 0.1834 - val_accuracy: 0.9598 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9816 - val_loss: 0.1826 - val_accuracy: 0.9626 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9813 - val_loss: 0.1718 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1018 - accuracy: 0.9821 - val_loss: 0.2174 - val_accuracy: 0.9485 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1046 - accuracy: 0.9812 - val_loss: 0.1887 - val_accuracy: 0.9607 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9815 - val_loss: 0.1935 - val_accuracy: 0.9564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1057 - accuracy: 0.9810 - val_loss: 0.1993 - val_accuracy: 0.9567 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1048 - accuracy: 0.9813 - val_loss: 0.1798 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9818 - val_loss: 0.1805 - val_accuracy: 0.9610 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1022 - accuracy: 0.9819 - val_loss: 0.1673 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9813 - val_loss: 0.1819 - val_accuracy: 0.9609 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9811 - val_loss: 0.1714 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9815 - val_loss: 0.1701 - val_accuracy: 0.9678 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9817 - val_loss: 0.1815 - val_accuracy: 0.9603 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1038 - accuracy: 0.9816 - val_loss: 0.1734 - val_accuracy: 0.9654 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9808 - val_loss: 0.1744 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1033 - accuracy: 0.9814 - val_loss: 0.1876 - val_accuracy: 0.9588 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1025 - accuracy: 0.9818 - val_loss: 0.1923 - val_accuracy: 0.9583 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1035 - accuracy: 0.9815 - val_loss: 0.1850 - val_accuracy: 0.9590 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1044 - accuracy: 0.9804 - val_loss: 0.1936 - val_accuracy: 0.9584 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9811 - val_loss: 0.1654 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1037 - accuracy: 0.9813 - val_loss: 0.1855 - val_accuracy: 0.9595 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1049 - accuracy: 0.9809 - val_loss: 0.1699 - val_accuracy: 0.9663 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1031 - accuracy: 0.9814 - val_loss: 0.1648 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1019 - accuracy: 0.9819 - val_loss: 0.2028 - val_accuracy: 0.9569 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9816 - val_loss: 0.1639 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9745 - val_loss: 0.1556 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1083 - accuracy: 0.9785 - val_loss: 0.1509 - val_accuracy: 0.9698 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1066 - accuracy: 0.9789 - val_loss: 0.1618 - val_accuracy: 0.9659 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1038 - accuracy: 0.9798 - val_loss: 0.1746 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1026 - accuracy: 0.9794 - val_loss: 0.1760 - val_accuracy: 0.9604 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9792 - val_loss: 0.1584 - val_accuracy: 0.9682 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1025 - accuracy: 0.9799 - val_loss: 0.1638 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1017 - accuracy: 0.9802 - val_loss: 0.1670 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1026 - accuracy: 0.9796 - val_loss: 0.1667 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9808 - val_loss: 0.1604 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0999 - accuracy: 0.9801 - val_loss: 0.1686 - val_accuracy: 0.9666 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1000 - accuracy: 0.9798 - val_loss: 0.1655 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0985 - accuracy: 0.9804 - val_loss: 0.1744 - val_accuracy: 0.9651 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1015 - accuracy: 0.9797 - val_loss: 0.1689 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1004 - accuracy: 0.9798 - val_loss: 0.1690 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9809 - val_loss: 0.1569 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9808 - val_loss: 0.1675 - val_accuracy: 0.9670 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0992 - accuracy: 0.9797 - val_loss: 0.1520 - val_accuracy: 0.9691 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9806 - val_loss: 0.1608 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9801 - val_loss: 0.1517 - val_accuracy: 0.9693 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0995 - accuracy: 0.9803 - val_loss: 0.1685 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9803 - val_loss: 0.1524 - val_accuracy: 0.9692 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0967 - accuracy: 0.9814 - val_loss: 0.1587 - val_accuracy: 0.9662 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0962 - accuracy: 0.9815 - val_loss: 0.1569 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0998 - accuracy: 0.9800 - val_loss: 0.1679 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9804 - val_loss: 0.1644 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9807 - val_loss: 0.1603 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9803 - val_loss: 0.1652 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9800 - val_loss: 0.1699 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0963 - accuracy: 0.9814 - val_loss: 0.1523 - val_accuracy: 0.9700 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9801 - val_loss: 0.1709 - val_accuracy: 0.9650 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0972 - accuracy: 0.9803 - val_loss: 0.1582 - val_accuracy: 0.9667 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9802 - val_loss: 0.1697 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9810 - val_loss: 0.1620 - val_accuracy: 0.9645 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0960 - accuracy: 0.9816 - val_loss: 0.1679 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0971 - accuracy: 0.9807 - val_loss: 0.1586 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0974 - accuracy: 0.9805 - val_loss: 0.1644 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0998 - accuracy: 0.9800 - val_loss: 0.1711 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9804 - val_loss: 0.1658 - val_accuracy: 0.9655 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9806 - val_loss: 0.1615 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0969 - accuracy: 0.9809 - val_loss: 0.1647 - val_accuracy: 0.9659 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0970 - accuracy: 0.9806 - val_loss: 0.1716 - val_accuracy: 0.9634 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9806 - val_loss: 0.1684 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9812 - val_loss: 0.1639 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9808 - val_loss: 0.1524 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9801 - val_loss: 0.1584 - val_accuracy: 0.9697 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9807 - val_loss: 0.1618 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9804 - val_loss: 0.1596 - val_accuracy: 0.9688 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0968 - accuracy: 0.9810 - val_loss: 0.1815 - val_accuracy: 0.9627 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9807 - val_loss: 0.1632 - val_accuracy: 0.9678 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9743 - val_loss: 0.1456 - val_accuracy: 0.9691 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1063 - accuracy: 0.9773 - val_loss: 0.1486 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1039 - accuracy: 0.9785 - val_loss: 0.1426 - val_accuracy: 0.9696 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9785 - val_loss: 0.1492 - val_accuracy: 0.9667 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1028 - accuracy: 0.9782 - val_loss: 0.1513 - val_accuracy: 0.9671 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1024 - accuracy: 0.9789 - val_loss: 0.1431 - val_accuracy: 0.9688 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1018 - accuracy: 0.9786 - val_loss: 0.1404 - val_accuracy: 0.9689 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1017 - accuracy: 0.9785 - val_loss: 0.1416 - val_accuracy: 0.9688 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1009 - accuracy: 0.9786 - val_loss: 0.1400 - val_accuracy: 0.9696 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1014 - accuracy: 0.9785 - val_loss: 0.1506 - val_accuracy: 0.9658 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1008 - accuracy: 0.9791 - val_loss: 0.1384 - val_accuracy: 0.9686 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0995 - accuracy: 0.9792 - val_loss: 0.1426 - val_accuracy: 0.9693 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9794 - val_loss: 0.1381 - val_accuracy: 0.9688 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0998 - accuracy: 0.9786 - val_loss: 0.1393 - val_accuracy: 0.9695 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1003 - accuracy: 0.9785 - val_loss: 0.1441 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1001 - accuracy: 0.9789 - val_loss: 0.1378 - val_accuracy: 0.9699 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1000 - accuracy: 0.9791 - val_loss: 0.1459 - val_accuracy: 0.9655 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9792 - val_loss: 0.1454 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9785 - val_loss: 0.1458 - val_accuracy: 0.9681 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1001 - accuracy: 0.9787 - val_loss: 0.1368 - val_accuracy: 0.9702 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9784 - val_loss: 0.1410 - val_accuracy: 0.9686 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9793 - val_loss: 0.1335 - val_accuracy: 0.9700 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9785 - val_loss: 0.1404 - val_accuracy: 0.9688 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0988 - accuracy: 0.9791 - val_loss: 0.1407 - val_accuracy: 0.9681 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0986 - accuracy: 0.9789 - val_loss: 0.1409 - val_accuracy: 0.9685 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1428 - val_accuracy: 0.9689 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9791 - val_loss: 0.1527 - val_accuracy: 0.9665 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9791 - val_loss: 0.1457 - val_accuracy: 0.9666 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9787 - val_loss: 0.1442 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9792 - val_loss: 0.1430 - val_accuracy: 0.9686 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0996 - accuracy: 0.9790 - val_loss: 0.1416 - val_accuracy: 0.9679 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1414 - val_accuracy: 0.9669 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9791 - val_loss: 0.1456 - val_accuracy: 0.9669 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9800 - val_loss: 0.1405 - val_accuracy: 0.9699 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0988 - accuracy: 0.9791 - val_loss: 0.1419 - val_accuracy: 0.9684 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9794 - val_loss: 0.1459 - val_accuracy: 0.9664 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0996 - accuracy: 0.9791 - val_loss: 0.1470 - val_accuracy: 0.9680 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9796 - val_loss: 0.1421 - val_accuracy: 0.9682 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9795 - val_loss: 0.1620 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1002 - accuracy: 0.9789 - val_loss: 0.1482 - val_accuracy: 0.9674 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9791 - val_loss: 0.1375 - val_accuracy: 0.9701 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9795 - val_loss: 0.1467 - val_accuracy: 0.9675 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9796 - val_loss: 0.1480 - val_accuracy: 0.9664 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0994 - accuracy: 0.9787 - val_loss: 0.1486 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9793 - val_loss: 0.1404 - val_accuracy: 0.9675 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0975 - accuracy: 0.9792 - val_loss: 0.1394 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0975 - accuracy: 0.9794 - val_loss: 0.1418 - val_accuracy: 0.9683 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9790 - val_loss: 0.1475 - val_accuracy: 0.9677 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9791 - val_loss: 0.1525 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9789 - val_loss: 0.1441 - val_accuracy: 0.9667 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0999 - accuracy: 0.9784 - val_loss: 0.1466 - val_accuracy: 0.9670 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9794 - val_loss: 0.1393 - val_accuracy: 0.9686 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0994 - accuracy: 0.9787 - val_loss: 0.1467 - val_accuracy: 0.9665 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0975 - accuracy: 0.9794 - val_loss: 0.1443 - val_accuracy: 0.9673 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1001 - accuracy: 0.9785 - val_loss: 0.1395 - val_accuracy: 0.9683 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0990 - accuracy: 0.9788 - val_loss: 0.1422 - val_accuracy: 0.9670 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9790 - val_loss: 0.1468 - val_accuracy: 0.9669 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0986 - accuracy: 0.9795 - val_loss: 0.1394 - val_accuracy: 0.9702 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0999 - accuracy: 0.9780 - val_loss: 0.1424 - val_accuracy: 0.9683 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9788 - val_loss: 0.1355 - val_accuracy: 0.9705 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0981 - accuracy: 0.9791 - val_loss: 0.1431 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0985 - accuracy: 0.9790 - val_loss: 0.1418 - val_accuracy: 0.9682 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0986 - accuracy: 0.9788 - val_loss: 0.1493 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9793 - val_loss: 0.1442 - val_accuracy: 0.9670 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0993 - accuracy: 0.9789 - val_loss: 0.1427 - val_accuracy: 0.9690 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0993 - accuracy: 0.9791 - val_loss: 0.1432 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0978 - accuracy: 0.9793 - val_loss: 0.1468 - val_accuracy: 0.9668 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0983 - accuracy: 0.9789 - val_loss: 0.1378 - val_accuracy: 0.9707 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9789 - val_loss: 0.1466 - val_accuracy: 0.9668 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0989 - accuracy: 0.9792 - val_loss: 0.1449 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0992 - accuracy: 0.9787 - val_loss: 0.1461 - val_accuracy: 0.9672 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9793 - val_loss: 0.1384 - val_accuracy: 0.9696 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0981 - accuracy: 0.9790 - val_loss: 0.1402 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9790 - val_loss: 0.1427 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0985 - accuracy: 0.9785 - val_loss: 0.1394 - val_accuracy: 0.9685 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0996 - accuracy: 0.9787 - val_loss: 0.1414 - val_accuracy: 0.9696 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9787 - val_loss: 0.1402 - val_accuracy: 0.9686 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9789 - val_loss: 0.1455 - val_accuracy: 0.9672 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9786 - val_loss: 0.1416 - val_accuracy: 0.9681 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0966 - accuracy: 0.9796 - val_loss: 0.1450 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0991 - accuracy: 0.9789 - val_loss: 0.1443 - val_accuracy: 0.9672 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0984 - accuracy: 0.9787 - val_loss: 0.1405 - val_accuracy: 0.9687 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0978 - accuracy: 0.9790 - val_loss: 0.1460 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9792 - val_loss: 0.1427 - val_accuracy: 0.9666 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0997 - accuracy: 0.9787 - val_loss: 0.1397 - val_accuracy: 0.9687 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0972 - accuracy: 0.9793 - val_loss: 0.1497 - val_accuracy: 0.9666 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1004 - accuracy: 0.9783 - val_loss: 0.1422 - val_accuracy: 0.9676 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0977 - accuracy: 0.9799 - val_loss: 0.1361 - val_accuracy: 0.9690 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9793 - val_loss: 0.1425 - val_accuracy: 0.9676 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0979 - accuracy: 0.9797 - val_loss: 0.1476 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0971 - accuracy: 0.9795 - val_loss: 0.1390 - val_accuracy: 0.9692 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9791 - val_loss: 0.1519 - val_accuracy: 0.9662 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0976 - accuracy: 0.9792 - val_loss: 0.1450 - val_accuracy: 0.9675 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9792 - val_loss: 0.1423 - val_accuracy: 0.9682 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0982 - accuracy: 0.9793 - val_loss: 0.1486 - val_accuracy: 0.9672 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0987 - accuracy: 0.9790 - val_loss: 0.1427 - val_accuracy: 0.9686 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9793 - val_loss: 0.1469 - val_accuracy: 0.9679 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0974 - accuracy: 0.9791 - val_loss: 0.1440 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0980 - accuracy: 0.9793 - val_loss: 0.1438 - val_accuracy: 0.9668 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0973 - accuracy: 0.9797 - val_loss: 0.1516 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 8.1139e-04 - accuracy: 0.9998 - val_loss: 0.0883 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. -0. -0.4671491] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8021e-04 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9836 [ 0. 0. -0. ... 0. 0. -0.46529546] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1083e-04 - accuracy: 1.0000 - val_loss: 0.0891 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. -0.46903822] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4239e-05 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. -0. -0.4711647] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8524e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. 0. -0.4723889] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4405e-05 - accuracy: 1.0000 - val_loss: 0.0906 - val_accuracy: 0.9826 [ 0. 0. -0. ... 0. 0. -0.47327575] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2017e-04 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9827 [ 0. 0. -0. ... 0. 0. -0.47514236] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5489e-05 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9822 [ 0. 0. -0. ... 0. 0. -0.4760198] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 6.1156e-04 - accuracy: 0.9999 - val_loss: 0.1024 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. -0. -0.50223625] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 15ms/step - loss: 6.0307e-04 - accuracy: 0.9998 - val_loss: 0.1024 - val_accuracy: 0.9817 [ 0. 0. -0. ... -0. 0. -0.53111416] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9754e-04 - accuracy: 0.9998 - val_loss: 0.1012 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. 0. -0.5248197] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5332e-04 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. 0. -0.5346722] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6018e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9834 [ 0. 0. -0. ... -0. -0. -0.534304] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9690e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9833 [ 0. 0. -0. ... -0. 0. -0.53748435] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6706e-05 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9837 [ 0. 0. -0. ... -0. -0. -0.53555095] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7840e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9840 [ 0. 0. -0. ... 0. -0. -0.536586] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6978e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9837 [ 0. 0. -0. ... 0. 0. -0.528645] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8348e-05 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9827 [ 0. 0. -0. ... 0. 0. -0.5363368] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3327e-05 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9832 [ 0. 0. -0. ... -0. 0. -0.53580153] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9591e-05 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. 0. -0.53923535] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0101e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.53984076] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0219e-05 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9831 [ 0. 0. -0. ... -0. 0. -0.540348] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6337e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. 0. -0.5417688] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 6.9942e-06 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. 0. -0.5425944] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8343e-06 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9832 [ 0. 0. -0. ... 0. 0. -0.5438243] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2881e-06 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9837 [ 0. 0. -0. ... 0. 0. -0.54764444] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1882e-06 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9835 [ 0. 0. -0. ... -0. 0. -0.5485463] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1989e-06 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9832 [ 0. 0. -0. ... 0. 0. -0.5491649] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2284e-06 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9833 [ 0. 0. -0. ... 0. 0. -0.55360985] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3660e-06 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. 0. -0.5534608] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6567e-06 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9834 [ 0. 0. -0. ... 0. 0. -0.5550426] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3635e-06 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9832 [ 0. 0. -0. ... 0. 0. -0.55540156] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8715e-06 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. 0. -0.55539864] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9133e-06 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9834 [ 0. 0. -0. ... -0. 0. -0.5557232] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2862e-06 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9832 [ 0. 0. -0. ... 0. 0. -0.55700326] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 15ms/step - loss: 2.4172e-06 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9832 [ 0. 0. -0. ... -0. 0. -0.5576949] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2425e-04 - accuracy: 0.9999 - val_loss: 0.1549 - val_accuracy: 0.9767 [ 0. 0. -0. ... 0. 0. -0.53346455] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.1323 - val_accuracy: 0.9811 [ 0. 0. -0. ... -0. 0. -0.562493] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2956e-04 - accuracy: 0.9998 - val_loss: 0.1259 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.5601767] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3955e-04 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. -0.56557643] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6135e-04 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9824 [ 0. 0. -0. ... 0. 0. -0.5675069] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8136e-05 - accuracy: 1.0000 - val_loss: 0.1197 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. 0. -0.5653893] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6441e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. 0. -0.5657731] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3002e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. 0. -0.56671613] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3028e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9832 [ 0. 0. -0. ... -0. 0. -0.5710753] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4862e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. 0. -0.57677317] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3468e-06 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. 0. -0.5773296] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1781e-05 - accuracy: 1.0000 - val_loss: 0.1202 - val_accuracy: 0.9836 [ 0. 0. -0. ... 0. -0. -0.576891] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 8.3234e-06 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9835 [ 0. 0. -0. ... 0. 0. -0.5779928] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 15ms/step - loss: 9.8848e-06 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. -0. -0.5803385] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0038 - accuracy: 0.9987 - val_loss: 0.1104 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. -0. -0.61099213] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 6.9359e-04 - accuracy: 0.9998 - val_loss: 0.1066 - val_accuracy: 0.9836 [ 0. 0. -0. ... -0. 0. -0.5973706] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5210e-04 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9835 [ 0. 0. -0. ... -0. 0. -0.59842896] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8846e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.600792] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1912e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. 0. -0.59894073] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1351e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. 0. -0.6014865] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2841e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. -0.6021348] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7245e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.60381955] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6288e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. -0. -0.6035501] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2969e-05 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9826 [ 0. 0. -0. ... 0. 0. -0.6065442] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2369e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9826 [ 0. 0. -0. ... 0. 0. -0.60562664] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7321e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9833 [ 0. 0. -0. ... -0. 0. -0.6088135] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8203e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9822 [ 0. 0. -0. ... 0. 0. -0.607443] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7933e-05 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9835 [ 0. 0. -0. ... 0. 0. -0.61090803] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4568e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. 0. -0.61216223] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1277e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. -0. -0.6139568] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7235e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9834 [ 0. 0. -0. ... -0. -0. -0.61513203] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5118e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9834 [ 0. 0. -0. ... -0. -0. -0.61469525] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2736e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9834 [ 0. 0. -0. ... -0. -0. -0.6140655] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2254e-05 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. 0. -0.6163622] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1742e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. 0. -0.6176249] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0528e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9835 [ 0. 0. -0. ... 0. -0. -0.61973166] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5280e-06 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9834 [ 0. 0. -0. ... -0. 0. -0.61982465] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0174e-04 - accuracy: 0.9999 - val_loss: 0.1111 - val_accuracy: 0.9826 [ 0. 0. -0. ... 0. 0. -0.63283396] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6202e-05 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. -0. -0.63129914] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4495e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9836 [ 0. 0. -0. ... -0. -0. -0.63378036] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0891e-05 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. 0. -0.63251144] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1781e-04 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9831 [ 0. 0. -0. ... -0. 0. -0.6369516] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1018e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9827 [ 0. 0. -0. ... 0. 0. -0.6396962] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1593e-05 - accuracy: 1.0000 - val_loss: 0.1155 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. 0. -0.6388876] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0472e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9826 [ 0. 0. -0. ... 0. 0. -0.6370882] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7347e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9828 [ 0. 0. -0. ... -0. 0. -0.6405147] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6881e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. 0. -0.64197314] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3105e-06 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9827 [ 0. 0. -0. ... 0. -0. -0.6447998] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2670e-06 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.6506941] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8014e-04 - accuracy: 0.9999 - val_loss: 0.1231 - val_accuracy: 0.9821 [ 0. 0. -0. ... 0. 0. -0.65720934] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2471e-04 - accuracy: 0.9999 - val_loss: 0.1281 - val_accuracy: 0.9822 [ 0. 0. -0. ... -0. -0. -0.65045637] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5254e-04 - accuracy: 0.9999 - val_loss: 0.1236 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. -0.64981794] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1732e-04 - accuracy: 1.0000 - val_loss: 0.1243 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. -0. -0.65554583] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0435e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. 0. -0.64563733] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1209e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9827 [ 0. 0. -0. ... -0. -0. -0.64557904] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9674e-05 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.6469672] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7864e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. -0. -0.6440701] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2459e-05 - accuracy: 1.0000 - val_loss: 0.1231 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. 0. -0.6452636] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4388e-06 - accuracy: 1.0000 - val_loss: 0.1224 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. 0. -0.6460221] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1928e-06 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9831 [ 0. 0. -0. ... -0. 0. -0.6454796] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 4s 15ms/step - loss: 5.3541e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9832 [ 0. 0. -0. ... -0. 0. -0.64631057] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5180e-06 - accuracy: 1.0000 - val_loss: 0.1230 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. 0. -0.6477382] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8481e-06 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. 0. -0.64765185] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2249e-06 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9831 [ 0. 0. -0. ... -0. 0. -0.6476807] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0078 - accuracy: 0.9974 - val_loss: 0.1163 - val_accuracy: 0.9799 [ 0. 0. -0. ... 0. 0. -0.5842136] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8807e-04 - accuracy: 0.9998 - val_loss: 0.1092 - val_accuracy: 0.9813 [ 0. 0. -0. ... 0. 0. -0.5974014] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1643e-04 - accuracy: 0.9999 - val_loss: 0.1085 - val_accuracy: 0.9817 [ 0. 0. -0. ... 0. 0. -0.5953442] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5785e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9818 [ 0. 0. -0. ... -0. 0. -0.5923075] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6611e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9815 [ 0. 0. -0. ... -0. -0. -0.5914813] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3024e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9820 [ 0. 0. -0. ... -0. 0. -0.59226054] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4367e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9814 [ 0. 0. -0. ... -0. 0. -0.5966814] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1934e-04 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9820 [ 0. 0. -0. ... -0. 0. -0.5975729] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1665e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9817 [ 0. 0. -0. ... 0. -0. -0.59761626] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2262e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. -0. -0.597967] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 6.9770e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9821 [ 0. 0. -0. ... 0. 0. -0.5988403] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9540e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9818 [ 0. 0. -0. ... 0. 0. -0.59926516] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0765e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9825 [ 0. 0. -0. ... -0. 0. -0.5995413] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2578e-05 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. 0. -0.60125154] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1549e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. -0. -0.60275507] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4555e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. 0. -0.6044884] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2628e-05 - accuracy: 1.0000 - val_loss: 0.1094 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. -0.6038372] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2986e-05 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9828 [ 0. 0. -0. ... -0. -0. -0.6053426] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4561e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9826 [ 0. 0. -0. ... 0. 0. -0.6063129] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2560e-05 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. -0. -0.607389] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0694e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9832 [ 0. 0. -0. ... -0. 0. -0.608513] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9994e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. -0. -0.61073303] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8792e-05 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9828 [ 0. 0. -0. ... -0. -0. -0.61220765] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6124e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. 0. -0.6150207] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4744e-05 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. -0. -0.6076736] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5048e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9829 [ 0. 0. -0. ... 0. 0. -0.6079288] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3930e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. -0. -0.6082331] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1395e-05 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9827 [ 0. 0. -0. ... -0. 0. -0.61064637] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6879e-06 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9830 [ 0. 0. -0. ... -0. -0. -0.61191964] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7471e-06 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9829 [ 0. 0. -0. ... -0. -0. -0.6168884] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4289e-06 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. -0. -0.61866254] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8104e-06 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9831 [ 0. 0. -0. ... -0. 0. -0.6144433] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8746e-06 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9831 [ 0. 0. -0. ... -0. -0. -0.6183386] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6879e-06 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9833 [ 0. 0. -0. ... -0. -0. -0.6206976] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9201e-06 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9830 [ 0. 0. -0. ... 0. 0. -0.6204374] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2600e-06 - accuracy: 1.0000 - val_loss: 0.1198 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. 0. -0.6214009] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1230e-06 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9828 [ 0. 0. -0. ... 0. -0. -0.6234882] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3326e-06 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9827 [ 0. 0. -0. ... -0. 0. -0.62424976] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5562e-06 - accuracy: 1.0000 - val_loss: 0.1222 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. 0. -0.62826645] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3924e-06 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9826 [ 0. 0. -0. ... -0. 0. -0.6309619] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5809e-06 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9828 [ 0. 0. -0. ... -0. 0. -0.63668805] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2865e-06 - accuracy: 1.0000 - val_loss: 0.1244 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. 0. -0.64158785] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0963e-06 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9831 [ 0. 0. -0. ... 0. -0. -0.6464676] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3335e-04 - accuracy: 0.9999 - val_loss: 0.1356 - val_accuracy: 0.9817 [ 0. 0. -0. ... -0. 0. -0.6423403] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2827e-04 - accuracy: 0.9999 - val_loss: 0.1322 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. -0.6174077] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 15ms/step - loss: 3.1257e-05 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9825 [ 0. 0. -0. ... -0. 0. -0.62500393] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0572e-05 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. 0. -0.62708527] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5441e-05 - accuracy: 1.0000 - val_loss: 0.1308 - val_accuracy: 0.9828 [ 0. 0. -0. ... -0. -0. -0.61869895] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0918e-05 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. 0. -0.62153697] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6082e-05 - accuracy: 1.0000 - val_loss: 0.1328 - val_accuracy: 0.9825 [ 0. 0. -0. ... -0. 0. -0.6228914] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0301 - accuracy: 0.9914 - val_loss: 0.1117 - val_accuracy: 0.9793 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.1071 - val_accuracy: 0.9801 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1065 - val_accuracy: 0.9809 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1063 - val_accuracy: 0.9806 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1053 - val_accuracy: 0.9804 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0075e-04 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9807 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7817e-04 - accuracy: 0.9999 - val_loss: 0.1051 - val_accuracy: 0.9811 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8842e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9811 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1369e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9812 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7678e-04 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9814 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1642e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9815 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5146e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9812 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9013e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9816 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5453e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9813 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3158e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9813 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1217e-04 - accuracy: 1.0000 - val_loss: 0.1087 - val_accuracy: 0.9811 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8526e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9815 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8377e-04 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9820 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2598e-04 - accuracy: 1.0000 - val_loss: 0.1083 - val_accuracy: 0.9814 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2123e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9813 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0645e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9818 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5415e-05 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9818 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8355e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 8.7932e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9820 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 8.0768e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9815 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3563e-05 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9815 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1337e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9812 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9862e-05 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3391e-05 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1900e-05 - accuracy: 1.0000 - val_loss: 0.1155 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7239e-05 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2089e-04 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3092e-05 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9820 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9689e-05 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9822 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7875e-05 - accuracy: 1.0000 - val_loss: 0.1205 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0202e-04 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9817 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7977e-05 - accuracy: 1.0000 - val_loss: 0.1246 - val_accuracy: 0.9824 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 4s 16ms/step - loss: 2.2410e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 4s 16ms/step - loss: 2.6043e-05 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9825 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 4s 16ms/step - loss: 3.7757e-05 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9601e-05 - accuracy: 1.0000 - val_loss: 0.1262 - val_accuracy: 0.9819 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 15ms/step - loss: 1.9345e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9820 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5515e-05 - accuracy: 1.0000 - val_loss: 0.1252 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7347e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9821 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1876e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9818 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8458e-05 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9816 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9270e-05 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9823 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4144e-04 - accuracy: 1.0000 - val_loss: 0.1278 - val_accuracy: 0.9822 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5209e-05 - accuracy: 1.0000 - val_loss: 0.1295 - val_accuracy: 0.9824 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0913e-05 - accuracy: 1.0000 - val_loss: 0.1291 - val_accuracy: 0.9823 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0641 - accuracy: 0.9828 - val_loss: 0.1471 - val_accuracy: 0.9754 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0217 - accuracy: 0.9933 - val_loss: 0.1356 - val_accuracy: 0.9764 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0130 - accuracy: 0.9959 - val_loss: 0.1303 - val_accuracy: 0.9772 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0093 - accuracy: 0.9973 - val_loss: 0.1287 - val_accuracy: 0.9773 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 0.1278 - val_accuracy: 0.9780 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0053 - accuracy: 0.9989 - val_loss: 0.1269 - val_accuracy: 0.9785 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 0.1271 - val_accuracy: 0.9790 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1264 - val_accuracy: 0.9789 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.1264 - val_accuracy: 0.9789 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0025 - accuracy: 0.9997 - val_loss: 0.1272 - val_accuracy: 0.9792 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.1268 - val_accuracy: 0.9787 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 0.1291 - val_accuracy: 0.9792 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0020 - accuracy: 0.9998 - val_loss: 0.1289 - val_accuracy: 0.9791 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1290 - val_accuracy: 0.9793 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9998 - val_loss: 0.1294 - val_accuracy: 0.9797 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9799 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1319 - val_accuracy: 0.9797 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 9.6419e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9791 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.1347 - val_accuracy: 0.9792 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6382e-04 - accuracy: 0.9999 - val_loss: 0.1347 - val_accuracy: 0.9791 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1523e-04 - accuracy: 1.0000 - val_loss: 0.1363 - val_accuracy: 0.9795 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7127e-04 - accuracy: 0.9999 - val_loss: 0.1382 - val_accuracy: 0.9792 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2885e-04 - accuracy: 0.9999 - val_loss: 0.1375 - val_accuracy: 0.9797 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5147e-04 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9793 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0875e-04 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9791 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3029e-04 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9790 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0350e-04 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9797 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2924e-04 - accuracy: 1.0000 - val_loss: 0.1403 - val_accuracy: 0.9795 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6186e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9797 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7471e-04 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9803 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9325e-04 - accuracy: 1.0000 - val_loss: 0.1455 - val_accuracy: 0.9802 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4099e-04 - accuracy: 1.0000 - val_loss: 0.1471 - val_accuracy: 0.9798 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6475e-04 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9800 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5566e-04 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9800 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5523e-04 - accuracy: 0.9999 - val_loss: 0.1505 - val_accuracy: 0.9800 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0042e-04 - accuracy: 1.0000 - val_loss: 0.1502 - val_accuracy: 0.9799 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7887e-04 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9804 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0170e-04 - accuracy: 1.0000 - val_loss: 0.1519 - val_accuracy: 0.9806 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0058e-04 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9799 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0833e-04 - accuracy: 0.9999 - val_loss: 0.1566 - val_accuracy: 0.9795 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2957e-04 - accuracy: 0.9999 - val_loss: 0.1562 - val_accuracy: 0.9795 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4819e-04 - accuracy: 1.0000 - val_loss: 0.1559 - val_accuracy: 0.9791 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5834e-04 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9792 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2612e-04 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9799 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7013e-04 - accuracy: 1.0000 - val_loss: 0.1618 - val_accuracy: 0.9797 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5782e-05 - accuracy: 1.0000 - val_loss: 0.1619 - val_accuracy: 0.9798 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1604e-04 - accuracy: 1.0000 - val_loss: 0.1624 - val_accuracy: 0.9800 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2603e-04 - accuracy: 1.0000 - val_loss: 0.1658 - val_accuracy: 0.9801 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0294e-04 - accuracy: 1.0000 - val_loss: 0.1671 - val_accuracy: 0.9801 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5512e-04 - accuracy: 1.0000 - val_loss: 0.1693 - val_accuracy: 0.9800 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2561 - accuracy: 0.9444 - val_loss: 0.2392 - val_accuracy: 0.9494 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1075 - accuracy: 0.9700 - val_loss: 0.2073 - val_accuracy: 0.9547 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0852 - accuracy: 0.9750 - val_loss: 0.1916 - val_accuracy: 0.9575 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0729 - accuracy: 0.9778 - val_loss: 0.1809 - val_accuracy: 0.9585 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0646 - accuracy: 0.9798 - val_loss: 0.1726 - val_accuracy: 0.9597 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0585 - accuracy: 0.9812 - val_loss: 0.1672 - val_accuracy: 0.9605 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0542 - accuracy: 0.9829 - val_loss: 0.1638 - val_accuracy: 0.9613 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0497 - accuracy: 0.9840 - val_loss: 0.1601 - val_accuracy: 0.9621 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0474 - accuracy: 0.9846 - val_loss: 0.1584 - val_accuracy: 0.9624 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0437 - accuracy: 0.9862 - val_loss: 0.1574 - val_accuracy: 0.9632 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0411 - accuracy: 0.9873 - val_loss: 0.1570 - val_accuracy: 0.9629 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0388 - accuracy: 0.9876 - val_loss: 0.1551 - val_accuracy: 0.9634 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0368 - accuracy: 0.9883 - val_loss: 0.1540 - val_accuracy: 0.9639 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0346 - accuracy: 0.9894 - val_loss: 0.1543 - val_accuracy: 0.9638 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0336 - accuracy: 0.9895 - val_loss: 0.1542 - val_accuracy: 0.9639 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0317 - accuracy: 0.9900 - val_loss: 0.1540 - val_accuracy: 0.9638 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0300 - accuracy: 0.9907 - val_loss: 0.1546 - val_accuracy: 0.9640 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0285 - accuracy: 0.9912 - val_loss: 0.1547 - val_accuracy: 0.9644 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0269 - accuracy: 0.9918 - val_loss: 0.1549 - val_accuracy: 0.9645 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0259 - accuracy: 0.9920 - val_loss: 0.1562 - val_accuracy: 0.9648 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0249 - accuracy: 0.9924 - val_loss: 0.1555 - val_accuracy: 0.9646 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0234 - accuracy: 0.9927 - val_loss: 0.1580 - val_accuracy: 0.9649 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0227 - accuracy: 0.9932 - val_loss: 0.1579 - val_accuracy: 0.9654 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.1588 - val_accuracy: 0.9648 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0211 - accuracy: 0.9939 - val_loss: 0.1602 - val_accuracy: 0.9649 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9940 - val_loss: 0.1609 - val_accuracy: 0.9655 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0191 - accuracy: 0.9946 - val_loss: 0.1612 - val_accuracy: 0.9660 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0175 - accuracy: 0.9952 - val_loss: 0.1623 - val_accuracy: 0.9657 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0175 - accuracy: 0.9951 - val_loss: 0.1641 - val_accuracy: 0.9660 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0170 - accuracy: 0.9950 - val_loss: 0.1644 - val_accuracy: 0.9670 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0156 - accuracy: 0.9960 - val_loss: 0.1654 - val_accuracy: 0.9670 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0149 - accuracy: 0.9961 - val_loss: 0.1670 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0142 - accuracy: 0.9966 - val_loss: 0.1693 - val_accuracy: 0.9669 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0136 - accuracy: 0.9965 - val_loss: 0.1714 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.1723 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0129 - accuracy: 0.9966 - val_loss: 0.1727 - val_accuracy: 0.9669 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0120 - accuracy: 0.9969 - val_loss: 0.1761 - val_accuracy: 0.9672 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0114 - accuracy: 0.9975 - val_loss: 0.1764 - val_accuracy: 0.9664 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0113 - accuracy: 0.9973 - val_loss: 0.1792 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 0.1812 - val_accuracy: 0.9670 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0105 - accuracy: 0.9976 - val_loss: 0.1810 - val_accuracy: 0.9673 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0099 - accuracy: 0.9977 - val_loss: 0.1832 - val_accuracy: 0.9675 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0093 - accuracy: 0.9980 - val_loss: 0.1871 - val_accuracy: 0.9673 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9980 - val_loss: 0.1871 - val_accuracy: 0.9676 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9983 - val_loss: 0.1895 - val_accuracy: 0.9674 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 0.1921 - val_accuracy: 0.9671 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9981 - val_loss: 0.1931 - val_accuracy: 0.9678 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0079 - accuracy: 0.9984 - val_loss: 0.1946 - val_accuracy: 0.9676 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0074 - accuracy: 0.9984 - val_loss: 0.1972 - val_accuracy: 0.9677 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9987 - val_loss: 0.2003 - val_accuracy: 0.9680 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4957 - accuracy: 0.8734 - val_loss: 0.3896 - val_accuracy: 0.9014 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2971 - accuracy: 0.9146 - val_loss: 0.3224 - val_accuracy: 0.9191 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2581 - accuracy: 0.9239 - val_loss: 0.2935 - val_accuracy: 0.9228 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2367 - accuracy: 0.9287 - val_loss: 0.2767 - val_accuracy: 0.9262 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2218 - accuracy: 0.9333 - val_loss: 0.2641 - val_accuracy: 0.9289 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2121 - accuracy: 0.9357 - val_loss: 0.2542 - val_accuracy: 0.9302 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2026 - accuracy: 0.9383 - val_loss: 0.2465 - val_accuracy: 0.9324 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1971 - accuracy: 0.9403 - val_loss: 0.2405 - val_accuracy: 0.9329 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1915 - accuracy: 0.9414 - val_loss: 0.2358 - val_accuracy: 0.9348 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1871 - accuracy: 0.9426 - val_loss: 0.2312 - val_accuracy: 0.9363 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1836 - accuracy: 0.9442 - val_loss: 0.2276 - val_accuracy: 0.9373 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1793 - accuracy: 0.9450 - val_loss: 0.2235 - val_accuracy: 0.9378 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1770 - accuracy: 0.9452 - val_loss: 0.2208 - val_accuracy: 0.9382 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1735 - accuracy: 0.9470 - val_loss: 0.2178 - val_accuracy: 0.9381 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1706 - accuracy: 0.9479 - val_loss: 0.2152 - val_accuracy: 0.9381 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1671 - accuracy: 0.9488 - val_loss: 0.2126 - val_accuracy: 0.9384 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1656 - accuracy: 0.9488 - val_loss: 0.2107 - val_accuracy: 0.9397 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1629 - accuracy: 0.9495 - val_loss: 0.2080 - val_accuracy: 0.9396 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1606 - accuracy: 0.9508 - val_loss: 0.2065 - val_accuracy: 0.9405 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1582 - accuracy: 0.9516 - val_loss: 0.2048 - val_accuracy: 0.9413 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1562 - accuracy: 0.9516 - val_loss: 0.2035 - val_accuracy: 0.9417 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1549 - accuracy: 0.9520 - val_loss: 0.2020 - val_accuracy: 0.9419 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1524 - accuracy: 0.9526 - val_loss: 0.2007 - val_accuracy: 0.9418 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1510 - accuracy: 0.9524 - val_loss: 0.1995 - val_accuracy: 0.9427 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1487 - accuracy: 0.9538 - val_loss: 0.1985 - val_accuracy: 0.9431 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1476 - accuracy: 0.9542 - val_loss: 0.1976 - val_accuracy: 0.9430 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1458 - accuracy: 0.9537 - val_loss: 0.1962 - val_accuracy: 0.9440 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9550 - val_loss: 0.1950 - val_accuracy: 0.9447 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9555 - val_loss: 0.1945 - val_accuracy: 0.9453 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1418 - accuracy: 0.9561 - val_loss: 0.1940 - val_accuracy: 0.9455 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9563 - val_loss: 0.1933 - val_accuracy: 0.9453 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9565 - val_loss: 0.1930 - val_accuracy: 0.9456 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9572 - val_loss: 0.1925 - val_accuracy: 0.9456 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9574 - val_loss: 0.1923 - val_accuracy: 0.9459 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9578 - val_loss: 0.1919 - val_accuracy: 0.9465 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9578 - val_loss: 0.1918 - val_accuracy: 0.9466 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9582 - val_loss: 0.1914 - val_accuracy: 0.9472 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1333 - accuracy: 0.9583 - val_loss: 0.1912 - val_accuracy: 0.9473 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9590 - val_loss: 0.1908 - val_accuracy: 0.9475 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9591 - val_loss: 0.1904 - val_accuracy: 0.9472 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9596 - val_loss: 0.1901 - val_accuracy: 0.9474 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9592 - val_loss: 0.1894 - val_accuracy: 0.9478 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9596 - val_loss: 0.1893 - val_accuracy: 0.9474 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9599 - val_loss: 0.1889 - val_accuracy: 0.9481 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9605 - val_loss: 0.1889 - val_accuracy: 0.9477 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9599 - val_loss: 0.1890 - val_accuracy: 0.9474 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9606 - val_loss: 0.1887 - val_accuracy: 0.9477 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9611 - val_loss: 0.1884 - val_accuracy: 0.9474 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9604 - val_loss: 0.1883 - val_accuracy: 0.9476 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9613 - val_loss: 0.1883 - val_accuracy: 0.9482 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 14ms/step - loss: 0.6897 - accuracy: 0.7849 - val_loss: 0.6106 - val_accuracy: 0.8092 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5769 - accuracy: 0.8160 - val_loss: 0.5671 - val_accuracy: 0.8214 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5502 - accuracy: 0.8233 - val_loss: 0.5467 - val_accuracy: 0.8275 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5368 - accuracy: 0.8265 - val_loss: 0.5344 - val_accuracy: 0.8313 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5269 - accuracy: 0.8295 - val_loss: 0.5243 - val_accuracy: 0.8364 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5187 - accuracy: 0.8318 - val_loss: 0.5165 - val_accuracy: 0.8371 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5129 - accuracy: 0.8329 - val_loss: 0.5104 - val_accuracy: 0.8386 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5071 - accuracy: 0.8341 - val_loss: 0.5053 - val_accuracy: 0.8415 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5018 - accuracy: 0.8361 - val_loss: 0.5004 - val_accuracy: 0.8428 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4975 - accuracy: 0.8370 - val_loss: 0.4958 - val_accuracy: 0.8438 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4928 - accuracy: 0.8391 - val_loss: 0.4915 - val_accuracy: 0.8460 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4887 - accuracy: 0.8397 - val_loss: 0.4859 - val_accuracy: 0.8475 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4838 - accuracy: 0.8425 - val_loss: 0.4825 - val_accuracy: 0.8481 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4811 - accuracy: 0.8425 - val_loss: 0.4794 - val_accuracy: 0.8483 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4783 - accuracy: 0.8442 - val_loss: 0.4772 - val_accuracy: 0.8495 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4766 - accuracy: 0.8440 - val_loss: 0.4751 - val_accuracy: 0.8501 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4744 - accuracy: 0.8452 - val_loss: 0.4732 - val_accuracy: 0.8504 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4729 - accuracy: 0.8457 - val_loss: 0.4712 - val_accuracy: 0.8520 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4700 - accuracy: 0.8468 - val_loss: 0.4694 - val_accuracy: 0.8523 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4690 - accuracy: 0.8466 - val_loss: 0.4678 - val_accuracy: 0.8531 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4663 - accuracy: 0.8475 - val_loss: 0.4661 - val_accuracy: 0.8549 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4649 - accuracy: 0.8482 - val_loss: 0.4647 - val_accuracy: 0.8551 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4637 - accuracy: 0.8486 - val_loss: 0.4637 - val_accuracy: 0.8554 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4625 - accuracy: 0.8492 - val_loss: 0.4627 - val_accuracy: 0.8566 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4624 - accuracy: 0.8494 - val_loss: 0.4619 - val_accuracy: 0.8567 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4604 - accuracy: 0.8495 - val_loss: 0.4613 - val_accuracy: 0.8569 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4604 - accuracy: 0.8503 - val_loss: 0.4606 - val_accuracy: 0.8572 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4594 - accuracy: 0.8513 - val_loss: 0.4598 - val_accuracy: 0.8573 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4584 - accuracy: 0.8507 - val_loss: 0.4600 - val_accuracy: 0.8575 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4580 - accuracy: 0.8505 - val_loss: 0.4596 - val_accuracy: 0.8571 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4580 - accuracy: 0.8507 - val_loss: 0.4595 - val_accuracy: 0.8563 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4574 - accuracy: 0.8504 - val_loss: 0.4589 - val_accuracy: 0.8568 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4566 - accuracy: 0.8508 - val_loss: 0.4581 - val_accuracy: 0.8568 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4554 - accuracy: 0.8523 - val_loss: 0.4575 - val_accuracy: 0.8574 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4544 - accuracy: 0.8520 - val_loss: 0.4571 - val_accuracy: 0.8576 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4547 - accuracy: 0.8513 - val_loss: 0.4569 - val_accuracy: 0.8570 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4537 - accuracy: 0.8522 - val_loss: 0.4559 - val_accuracy: 0.8567 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4537 - accuracy: 0.8518 - val_loss: 0.4560 - val_accuracy: 0.8568 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4525 - accuracy: 0.8520 - val_loss: 0.4556 - val_accuracy: 0.8566 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4510 - accuracy: 0.8528 - val_loss: 0.4550 - val_accuracy: 0.8578 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4514 - accuracy: 0.8522 - val_loss: 0.4542 - val_accuracy: 0.8570 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4511 - accuracy: 0.8525 - val_loss: 0.4538 - val_accuracy: 0.8566 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4499 - accuracy: 0.8531 - val_loss: 0.4534 - val_accuracy: 0.8577 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4499 - accuracy: 0.8533 - val_loss: 0.4532 - val_accuracy: 0.8573 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4497 - accuracy: 0.8531 - val_loss: 0.4528 - val_accuracy: 0.8568 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4479 - accuracy: 0.8538 - val_loss: 0.4518 - val_accuracy: 0.8577 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4473 - accuracy: 0.8550 - val_loss: 0.4508 - val_accuracy: 0.8577 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4471 - accuracy: 0.8541 - val_loss: 0.4498 - val_accuracy: 0.8572 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4463 - accuracy: 0.8557 - val_loss: 0.4489 - val_accuracy: 0.8582 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 4s 16ms/step - loss: 0.4460 - accuracy: 0.8546 - val_loss: 0.4485 - val_accuracy: 0.8581 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 4s 16ms/step - loss: 1.0185 - accuracy: 0.6533 - val_loss: 0.9604 - val_accuracy: 0.6694 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9434 - accuracy: 0.6708 - val_loss: 0.9275 - val_accuracy: 0.6843 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9315 - accuracy: 0.6748 - val_loss: 0.9188 - val_accuracy: 0.6857 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9238 - accuracy: 0.6759 - val_loss: 0.9133 - val_accuracy: 0.6854 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9189 - accuracy: 0.6783 - val_loss: 0.9097 - val_accuracy: 0.6852 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9160 - accuracy: 0.6791 - val_loss: 0.9067 - val_accuracy: 0.6873 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9128 - accuracy: 0.6795 - val_loss: 0.9044 - val_accuracy: 0.6852 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9100 - accuracy: 0.6819 - val_loss: 0.9023 - val_accuracy: 0.6884 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9074 - accuracy: 0.6818 - val_loss: 0.8995 - val_accuracy: 0.6872 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9043 - accuracy: 0.6830 - val_loss: 0.8969 - val_accuracy: 0.6874 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9029 - accuracy: 0.6841 - val_loss: 0.8955 - val_accuracy: 0.6893 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9015 - accuracy: 0.6837 - val_loss: 0.8938 - val_accuracy: 0.6904 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8996 - accuracy: 0.6845 - val_loss: 0.8934 - val_accuracy: 0.6908 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8989 - accuracy: 0.6849 - val_loss: 0.8921 - val_accuracy: 0.6907 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8974 - accuracy: 0.6858 - val_loss: 0.8910 - val_accuracy: 0.6942 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8951 - accuracy: 0.6868 - val_loss: 0.8871 - val_accuracy: 0.6929 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8912 - accuracy: 0.6876 - val_loss: 0.8847 - val_accuracy: 0.6944 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8903 - accuracy: 0.6876 - val_loss: 0.8830 - val_accuracy: 0.6954 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8901 - accuracy: 0.6888 - val_loss: 0.8824 - val_accuracy: 0.6966 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8872 - accuracy: 0.6885 - val_loss: 0.8791 - val_accuracy: 0.6979 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8852 - accuracy: 0.6885 - val_loss: 0.8777 - val_accuracy: 0.6977 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8840 - accuracy: 0.6885 - val_loss: 0.8762 - val_accuracy: 0.6978 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8836 - accuracy: 0.6894 - val_loss: 0.8753 - val_accuracy: 0.6977 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8830 - accuracy: 0.6893 - val_loss: 0.8753 - val_accuracy: 0.6974 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8830 - accuracy: 0.6900 - val_loss: 0.8744 - val_accuracy: 0.6969 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8819 - accuracy: 0.6892 - val_loss: 0.8739 - val_accuracy: 0.6988 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8816 - accuracy: 0.6899 - val_loss: 0.8735 - val_accuracy: 0.6983 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8803 - accuracy: 0.6908 - val_loss: 0.8735 - val_accuracy: 0.6995 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 4s 15ms/step - loss: 0.8806 - accuracy: 0.6901 - val_loss: 0.8731 - val_accuracy: 0.7000 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8801 - accuracy: 0.6906 - val_loss: 0.8731 - val_accuracy: 0.6989 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8798 - accuracy: 0.6909 - val_loss: 0.8725 - val_accuracy: 0.7004 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8784 - accuracy: 0.6919 - val_loss: 0.8719 - val_accuracy: 0.6988 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8790 - accuracy: 0.6910 - val_loss: 0.8721 - val_accuracy: 0.6996 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8792 - accuracy: 0.6911 - val_loss: 0.8720 - val_accuracy: 0.6996 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8774 - accuracy: 0.6930 - val_loss: 0.8711 - val_accuracy: 0.6994 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8775 - accuracy: 0.6922 - val_loss: 0.8711 - val_accuracy: 0.7001 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8770 - accuracy: 0.6921 - val_loss: 0.8714 - val_accuracy: 0.7009 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8766 - accuracy: 0.6928 - val_loss: 0.8705 - val_accuracy: 0.7000 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8763 - accuracy: 0.6922 - val_loss: 0.8704 - val_accuracy: 0.7013 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8765 - accuracy: 0.6928 - val_loss: 0.8701 - val_accuracy: 0.7011 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8755 - accuracy: 0.6925 - val_loss: 0.8693 - val_accuracy: 0.7003 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8759 - accuracy: 0.6930 - val_loss: 0.8685 - val_accuracy: 0.7004 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8749 - accuracy: 0.6934 - val_loss: 0.8681 - val_accuracy: 0.7010 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8741 - accuracy: 0.6931 - val_loss: 0.8681 - val_accuracy: 0.7016 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8738 - accuracy: 0.6935 - val_loss: 0.8678 - val_accuracy: 0.7012 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8736 - accuracy: 0.6937 - val_loss: 0.8684 - val_accuracy: 0.7020 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8730 - accuracy: 0.6939 - val_loss: 0.8675 - val_accuracy: 0.7024 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8731 - accuracy: 0.6939 - val_loss: 0.8689 - val_accuracy: 0.7022 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8726 - accuracy: 0.6941 - val_loss: 0.8686 - val_accuracy: 0.7004 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8722 - accuracy: 0.6940 - val_loss: 0.8686 - val_accuracy: 0.7027 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8726 - accuracy: 0.6940 - val_loss: 0.8673 - val_accuracy: 0.7009 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8718 - accuracy: 0.6941 - val_loss: 0.8676 - val_accuracy: 0.7017 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8735 - accuracy: 0.6940 - val_loss: 0.8676 - val_accuracy: 0.7021 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8720 - accuracy: 0.6952 - val_loss: 0.8669 - val_accuracy: 0.7014 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8711 - accuracy: 0.6951 - val_loss: 0.8674 - val_accuracy: 0.7015 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8700 - accuracy: 0.6953 - val_loss: 0.8673 - val_accuracy: 0.7028 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8708 - accuracy: 0.6946 - val_loss: 0.8664 - val_accuracy: 0.7030 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 12ms/step - loss: 0.8707 - accuracy: 0.6949 - val_loss: 0.8667 - val_accuracy: 0.7034 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8706 - accuracy: 0.6950 - val_loss: 0.8660 - val_accuracy: 0.7023 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8708 - accuracy: 0.6949 - val_loss: 0.8660 - val_accuracy: 0.7019 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8716 - accuracy: 0.6959 - val_loss: 0.8656 - val_accuracy: 0.7035 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8707 - accuracy: 0.6949 - val_loss: 0.8657 - val_accuracy: 0.7020 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8704 - accuracy: 0.6955 - val_loss: 0.8655 - val_accuracy: 0.7024 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8704 - accuracy: 0.6952 - val_loss: 0.8648 - val_accuracy: 0.7018 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8701 - accuracy: 0.6947 - val_loss: 0.8653 - val_accuracy: 0.7040 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8697 - accuracy: 0.6959 - val_loss: 0.8646 - val_accuracy: 0.7024 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8697 - accuracy: 0.6949 - val_loss: 0.8641 - val_accuracy: 0.7031 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8696 - accuracy: 0.6951 - val_loss: 0.8645 - val_accuracy: 0.7036 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8696 - accuracy: 0.6956 - val_loss: 0.8645 - val_accuracy: 0.7046 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8695 - accuracy: 0.6968 - val_loss: 0.8636 - val_accuracy: 0.7028 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8692 - accuracy: 0.6946 - val_loss: 0.8642 - val_accuracy: 0.7029 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8693 - accuracy: 0.6961 - val_loss: 0.8636 - val_accuracy: 0.7024 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8687 - accuracy: 0.6962 - val_loss: 0.8634 - val_accuracy: 0.7024 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8695 - accuracy: 0.6959 - val_loss: 0.8630 - val_accuracy: 0.7035 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8688 - accuracy: 0.6957 - val_loss: 0.8623 - val_accuracy: 0.7041 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6959 - val_loss: 0.8619 - val_accuracy: 0.7027 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8674 - accuracy: 0.6963 - val_loss: 0.8621 - val_accuracy: 0.7036 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8681 - accuracy: 0.6960 - val_loss: 0.8626 - val_accuracy: 0.7027 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8681 - accuracy: 0.6966 - val_loss: 0.8619 - val_accuracy: 0.7039 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6963 - val_loss: 0.8611 - val_accuracy: 0.7028 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8682 - accuracy: 0.6960 - val_loss: 0.8617 - val_accuracy: 0.7038 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6953 - val_loss: 0.8611 - val_accuracy: 0.7036 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8678 - accuracy: 0.6958 - val_loss: 0.8613 - val_accuracy: 0.7039 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8674 - accuracy: 0.6970 - val_loss: 0.8615 - val_accuracy: 0.7045 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8686 - accuracy: 0.6962 - val_loss: 0.8616 - val_accuracy: 0.7042 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8678 - accuracy: 0.6970 - val_loss: 0.8616 - val_accuracy: 0.7034 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8680 - accuracy: 0.6964 - val_loss: 0.8613 - val_accuracy: 0.7040 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8676 - accuracy: 0.6968 - val_loss: 0.8615 - val_accuracy: 0.7048 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8688 - accuracy: 0.6967 - val_loss: 0.8611 - val_accuracy: 0.7039 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8678 - accuracy: 0.6961 - val_loss: 0.8614 - val_accuracy: 0.7032 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8676 - accuracy: 0.6973 - val_loss: 0.8614 - val_accuracy: 0.7044 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8672 - accuracy: 0.6971 - val_loss: 0.8606 - val_accuracy: 0.7044 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8673 - accuracy: 0.6966 - val_loss: 0.8611 - val_accuracy: 0.7040 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8660 - accuracy: 0.6969 - val_loss: 0.8603 - val_accuracy: 0.7040 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8665 - accuracy: 0.6965 - val_loss: 0.8605 - val_accuracy: 0.7035 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8674 - accuracy: 0.6971 - val_loss: 0.8604 - val_accuracy: 0.7032 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8668 - accuracy: 0.6978 - val_loss: 0.8605 - val_accuracy: 0.7039 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8667 - accuracy: 0.6968 - val_loss: 0.8597 - val_accuracy: 0.7038 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 15ms/step - loss: 0.8674 - accuracy: 0.6956 - val_loss: 0.8599 - val_accuracy: 0.7033 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.8673 - accuracy: 0.6964 - val_loss: 0.8601 - val_accuracy: 0.7048 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 3s 9ms/step - loss: 0.8526 - accuracy: 0.8982 - val_loss: 0.8265 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. 0.13614766 0. ] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8437 - accuracy: 0.9003 - val_loss: 0.8249 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. 0.14399227 0. ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8427 - accuracy: 0.9002 - val_loss: 0.8243 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. 0.14975326 0. ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8422 - accuracy: 0.8999 - val_loss: 0.8238 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. 0.15280853 0. ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8418 - accuracy: 0.8999 - val_loss: 0.8236 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.15454446 0. ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8419 - accuracy: 0.8999 - val_loss: 0.8236 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. 0.1554479 0. ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8417 - accuracy: 0.9000 - val_loss: 0.8235 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. 0.15593015 0. ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8416 - accuracy: 0.9000 - val_loss: 0.8234 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. 0.15611143 0. ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8414 - accuracy: 0.9000 - val_loss: 0.8232 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. 0.1561268 0. ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8413 - accuracy: 0.8999 - val_loss: 0.8231 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. 0.15617429 0. ] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8413 - accuracy: 0.8997 - val_loss: 0.8229 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. 0.15605137 0. ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8411 - accuracy: 0.9004 - val_loss: 0.8229 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. 0.15591909 0. ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8411 - accuracy: 0.9001 - val_loss: 0.8228 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.15589362 0. ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8410 - accuracy: 0.9002 - val_loss: 0.8231 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. 0.15556595 0. ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8409 - accuracy: 0.9003 - val_loss: 0.8226 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. 0.15540807 0. ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.9000 - val_loss: 0.8230 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. 0.15545778 0. ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8408 - accuracy: 0.9000 - val_loss: 0.8228 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.15539718 0. ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8410 - accuracy: 0.9003 - val_loss: 0.8227 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. 0.15533721 0. ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8410 - accuracy: 0.8999 - val_loss: 0.8226 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. 0.15529539 0. ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.9002 - val_loss: 0.8227 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.1553931 0. ] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9002 - val_loss: 0.8229 - val_accuracy: 0.9030 [ 0. 0. 0. ... -0. 0.15537092 0. ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.9002 - val_loss: 0.8224 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. 0.15536803 0. ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.8999 - val_loss: 0.8226 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. 0.15553221 0. ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8407 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. 0.155561 0. ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8408 - accuracy: 0.9001 - val_loss: 0.8229 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. 0.15556252 0. ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8407 - accuracy: 0.9002 - val_loss: 0.8226 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. 0.15558323 0. ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.9002 - val_loss: 0.8222 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. 0.15563019 0. ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.15549228 0. ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9001 - val_loss: 0.8219 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. 0.15556586 0. ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9002 - val_loss: 0.8222 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. 0.15543585 0. ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9003 - val_loss: 0.8224 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. 0.15558897 0. ] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9001 - val_loss: 0.8226 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. 0.15551874 0. ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9004 - val_loss: 0.8222 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.155691 0. ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8999 - val_loss: 0.8223 - val_accuracy: 0.9030 [ 0. 0. 0. ... -0. 0.15572841 0. ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9000 - val_loss: 0.8221 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. 0.15581815 0. ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9003 - val_loss: 0.8221 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. 0.1560769 0. ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.8998 - val_loss: 0.8223 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.1557283 0. ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.15587306 0. ] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8408 - accuracy: 0.9000 - val_loss: 0.8223 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. 0.1558708 0. ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9001 - val_loss: 0.8224 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. 0.15607613 0. ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8998 - val_loss: 0.8222 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. 0.15618749 0. ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8997 - val_loss: 0.8225 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. 0.15593015 0. ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8405 - accuracy: 0.8999 - val_loss: 0.8221 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. 0.1560732 0. ] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.8999 - val_loss: 0.8219 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. 0.15593822 0. ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8405 - accuracy: 0.9001 - val_loss: 0.8222 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.15585046 0. ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8407 - accuracy: 0.8999 - val_loss: 0.8224 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. 0.15607128 0. ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8406 - accuracy: 0.9000 - val_loss: 0.8225 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. 0.15611024 0. ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8406 - accuracy: 0.9001 - val_loss: 0.8224 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. 0.15624961 0. ] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8404 - accuracy: 0.8999 - val_loss: 0.8227 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. 0.15619323 0. ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8404 - accuracy: 0.9002 - val_loss: 0.8220 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. 0.1561847 0. ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8644 - accuracy: 0.8993 - val_loss: 0.8411 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. 0.16709255 0. ] Sparsity at: 0.6457718615879828 Epoch 52/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.9002 - val_loss: 0.8401 - val_accuracy: 0.9053 [ 0. 0. 0. ... -0. 0.1681191 0. ] Sparsity at: 0.6457718615879828 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8588 - accuracy: 0.9001 - val_loss: 0.8398 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16831204 0. ] Sparsity at: 0.6457718615879828 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8584 - accuracy: 0.9000 - val_loss: 0.8395 - val_accuracy: 0.9052 [ 0. 0. 0. ... -0. 0.16851896 0. ] Sparsity at: 0.6457718615879828 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8585 - accuracy: 0.9002 - val_loss: 0.8397 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16847257 0. ] Sparsity at: 0.6457718615879828 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9003 - val_loss: 0.8393 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. 0.1684288 0. ] Sparsity at: 0.6457718615879828 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8581 - accuracy: 0.9001 - val_loss: 0.8393 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. 0.16856475 0. ] Sparsity at: 0.6457718615879828 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8582 - accuracy: 0.9001 - val_loss: 0.8395 - val_accuracy: 0.9050 [ 0. 0. 0. ... -0. 0.1685264 0. ] Sparsity at: 0.6457718615879828 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8580 - accuracy: 0.9003 - val_loss: 0.8393 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16869482 0. ] Sparsity at: 0.6457718615879828 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9003 - val_loss: 0.8390 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16872844 0. ] Sparsity at: 0.6457718615879828 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8580 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16866003 0. ] Sparsity at: 0.6457718615879828 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8580 - accuracy: 0.9003 - val_loss: 0.8388 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16862403 0. ] Sparsity at: 0.6457718615879828 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9003 - val_loss: 0.8393 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16875425 0. ] Sparsity at: 0.6457718615879828 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8389 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.16872597 0. ] Sparsity at: 0.6457718615879828 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8391 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16869523 0. ] Sparsity at: 0.6457718615879828 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9002 - val_loss: 0.8392 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16852418 0. ] Sparsity at: 0.6457718615879828 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16840167 0. ] Sparsity at: 0.6457718615879828 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9002 - val_loss: 0.8388 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.16841999 0. ] Sparsity at: 0.6457718615879828 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9001 - val_loss: 0.8391 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.16855407 0. ] Sparsity at: 0.6457718615879828 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8580 - accuracy: 0.9002 - val_loss: 0.8393 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.16848816 0. ] Sparsity at: 0.6457718615879828 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.1685914 0. ] Sparsity at: 0.6457718615879828 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9004 - val_loss: 0.8389 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16871257 0. ] Sparsity at: 0.6457718615879828 Epoch 73/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9004 - val_loss: 0.8391 - val_accuracy: 0.9051 [ 0. 0. 0. ... -0. 0.16862175 0. ] Sparsity at: 0.6457718615879828 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8576 - accuracy: 0.9004 - val_loss: 0.8389 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.16859733 0. ] Sparsity at: 0.6457718615879828 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8389 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16842467 0. ] Sparsity at: 0.6457718615879828 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9004 - val_loss: 0.8389 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.16854127 0. ] Sparsity at: 0.6457718615879828 Epoch 77/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8578 - accuracy: 0.9005 - val_loss: 0.8390 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16846651 0. ] Sparsity at: 0.6457718615879828 Epoch 78/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8577 - accuracy: 0.9002 - val_loss: 0.8389 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16853034 0. ] Sparsity at: 0.6457718615879828 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8389 - val_accuracy: 0.9049 [ 0. 0. 0. ... -0. 0.16842672 0. ] Sparsity at: 0.6457718615879828 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.9002 - val_loss: 0.8387 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.16840588 0. ] Sparsity at: 0.6457718615879828 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.9001 - val_loss: 0.8389 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16844028 0. ] Sparsity at: 0.6457718615879828 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8390 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16845325 0. ] Sparsity at: 0.6457718615879828 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9000 - val_loss: 0.8388 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16829588 0. ] Sparsity at: 0.6457718615879828 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8576 - accuracy: 0.9003 - val_loss: 0.8390 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16849287 0. ] Sparsity at: 0.6457718615879828 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9051 [ 0. 0. 0. ... -0. 0.16851202 0. ] Sparsity at: 0.6457718615879828 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9001 - val_loss: 0.8388 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. 0.16839112 0. ] Sparsity at: 0.6457718615879828 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8389 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16838484 0. ] Sparsity at: 0.6457718615879828 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.9000 - val_loss: 0.8386 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16855457 0. ] Sparsity at: 0.6457718615879828 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16866429 0. ] Sparsity at: 0.6457718615879828 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8388 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.1684724 0. ] Sparsity at: 0.6457718615879828 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8392 - val_accuracy: 0.9053 [ 0. 0. 0. ... -0. 0.1685284 0. ] Sparsity at: 0.6457718615879828 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9001 - val_loss: 0.8392 - val_accuracy: 0.9051 [ 0. 0. 0. ... -0. 0.16854161 0. ] Sparsity at: 0.6457718615879828 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8387 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16842692 0. ] Sparsity at: 0.6457718615879828 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9000 - val_loss: 0.8390 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. 0.16829602 0. ] Sparsity at: 0.6457718615879828 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8577 - accuracy: 0.9001 - val_loss: 0.8387 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. 0.16845188 0. ] Sparsity at: 0.6457718615879828 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9002 - val_loss: 0.8390 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.16847849 0. ] Sparsity at: 0.6457718615879828 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8576 - accuracy: 0.9002 - val_loss: 0.8387 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. 0.16848397 0. ] Sparsity at: 0.6457718615879828 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8579 - accuracy: 0.9001 - val_loss: 0.8388 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. 0.16843626 0. ] Sparsity at: 0.6457718615879828 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8577 - accuracy: 0.8998 - val_loss: 0.8388 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16848972 0. ] Sparsity at: 0.6457718615879828 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8578 - accuracy: 0.9001 - val_loss: 0.8387 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. 0.16826412 0. ] Sparsity at: 0.6457718615879828 Epoch 101/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9062 - accuracy: 0.8971 - val_loss: 0.8813 - val_accuracy: 0.9029 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8980 - accuracy: 0.8986 - val_loss: 0.8804 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8973 - accuracy: 0.8984 - val_loss: 0.8797 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8968 - accuracy: 0.8986 - val_loss: 0.8793 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8966 - accuracy: 0.8986 - val_loss: 0.8797 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8964 - accuracy: 0.8984 - val_loss: 0.8793 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8962 - accuracy: 0.8986 - val_loss: 0.8789 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8962 - accuracy: 0.8985 - val_loss: 0.8791 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8961 - accuracy: 0.8984 - val_loss: 0.8789 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8960 - accuracy: 0.8985 - val_loss: 0.8788 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8959 - accuracy: 0.8985 - val_loss: 0.8787 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8958 - accuracy: 0.8988 - val_loss: 0.8790 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8959 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8959 - accuracy: 0.8987 - val_loss: 0.8787 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8959 - accuracy: 0.8984 - val_loss: 0.8787 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8785 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9021 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8787 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8958 - accuracy: 0.8986 - val_loss: 0.8787 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8786 - val_accuracy: 0.9025 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8785 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8987 - val_loss: 0.8788 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8784 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8988 - val_loss: 0.8782 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8985 - val_loss: 0.8786 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8988 - val_loss: 0.8785 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8784 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8785 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8988 - val_loss: 0.8785 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8984 - val_loss: 0.8785 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8782 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8788 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8983 - val_loss: 0.8785 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8955 - accuracy: 0.8988 - val_loss: 0.8787 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8957 - accuracy: 0.8987 - val_loss: 0.8784 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8988 - val_loss: 0.8786 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8781 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8783 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8955 - accuracy: 0.8987 - val_loss: 0.8786 - val_accuracy: 0.9030 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8785 - val_accuracy: 0.9020 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8782 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8987 - val_loss: 0.8782 - val_accuracy: 0.9027 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8955 - accuracy: 0.8988 - val_loss: 0.8783 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8955 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9024 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8785 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8957 - accuracy: 0.8986 - val_loss: 0.8784 - val_accuracy: 0.9025 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8986 - val_loss: 0.8786 - val_accuracy: 0.9022 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.8985 - val_loss: 0.8784 - val_accuracy: 0.9023 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9513 - accuracy: 0.8920 - val_loss: 0.9243 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9418 - accuracy: 0.8941 - val_loss: 0.9232 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9410 - accuracy: 0.8944 - val_loss: 0.9228 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9406 - accuracy: 0.8946 - val_loss: 0.9226 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9405 - accuracy: 0.8946 - val_loss: 0.9225 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9403 - accuracy: 0.8944 - val_loss: 0.9224 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9402 - accuracy: 0.8946 - val_loss: 0.9222 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9403 - accuracy: 0.8943 - val_loss: 0.9222 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9402 - accuracy: 0.8945 - val_loss: 0.9223 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9401 - accuracy: 0.8946 - val_loss: 0.9222 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9221 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 162/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9220 - val_accuracy: 0.8982 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9220 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9219 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9220 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9219 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8946 - val_loss: 0.9221 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 169/500 235/235 [==============================] - 2s 10ms/step - loss: 0.9400 - accuracy: 0.8945 - val_loss: 0.9220 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 170/500 235/235 [==============================] - 2s 10ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9220 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8948 - val_loss: 0.9220 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9219 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9398 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9398 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9218 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8943 - val_loss: 0.9219 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9400 - accuracy: 0.8946 - val_loss: 0.9219 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8943 - val_loss: 0.9217 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8943 - val_loss: 0.9219 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8948 - val_loss: 0.9220 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 186/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8946 - val_loss: 0.9218 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9398 - accuracy: 0.8946 - val_loss: 0.9220 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9217 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8944 - val_loss: 0.9220 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8947 - val_loss: 0.9218 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9219 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9218 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9218 - val_accuracy: 0.8981 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8948 - val_loss: 0.9219 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9219 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8944 - val_loss: 0.9219 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9218 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9399 - accuracy: 0.8945 - val_loss: 0.9218 - val_accuracy: 0.8980 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9400 - accuracy: 0.8947 - val_loss: 0.9219 - val_accuracy: 0.8979 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8447223712446352 Epoch 201/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0900 - accuracy: 0.8714 - val_loss: 1.0471 - val_accuracy: 0.8846 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059482296137339 Epoch 202/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0653 - accuracy: 0.8819 - val_loss: 1.0437 - val_accuracy: 0.8858 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0631 - accuracy: 0.8829 - val_loss: 1.0421 - val_accuracy: 0.8868 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0620 - accuracy: 0.8830 - val_loss: 1.0414 - val_accuracy: 0.8874 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0614 - accuracy: 0.8832 - val_loss: 1.0410 - val_accuracy: 0.8875 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0609 - accuracy: 0.8832 - val_loss: 1.0404 - val_accuracy: 0.8878 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0606 - accuracy: 0.8835 - val_loss: 1.0400 - val_accuracy: 0.8876 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0603 - accuracy: 0.8836 - val_loss: 1.0397 - val_accuracy: 0.8876 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0601 - accuracy: 0.8833 - val_loss: 1.0393 - val_accuracy: 0.8881 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0598 - accuracy: 0.8833 - val_loss: 1.0391 - val_accuracy: 0.8885 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0597 - accuracy: 0.8834 - val_loss: 1.0386 - val_accuracy: 0.8884 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0595 - accuracy: 0.8835 - val_loss: 1.0385 - val_accuracy: 0.8887 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0594 - accuracy: 0.8834 - val_loss: 1.0385 - val_accuracy: 0.8888 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0594 - accuracy: 0.8835 - val_loss: 1.0383 - val_accuracy: 0.8890 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0592 - accuracy: 0.8835 - val_loss: 1.0383 - val_accuracy: 0.8889 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0592 - accuracy: 0.8834 - val_loss: 1.0382 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0591 - accuracy: 0.8837 - val_loss: 1.0382 - val_accuracy: 0.8890 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0591 - accuracy: 0.8837 - val_loss: 1.0382 - val_accuracy: 0.8890 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0591 - accuracy: 0.8837 - val_loss: 1.0381 - val_accuracy: 0.8890 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0382 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8834 - val_loss: 1.0381 - val_accuracy: 0.8888 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8837 - val_loss: 1.0380 - val_accuracy: 0.8889 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8835 - val_loss: 1.0381 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8892 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8834 - val_loss: 1.0380 - val_accuracy: 0.8892 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0590 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0378 - val_accuracy: 0.8896 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8836 - val_loss: 1.0381 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8834 - val_loss: 1.0380 - val_accuracy: 0.8892 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 231/500 235/235 [==============================] - 3s 11ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0590 - accuracy: 0.8838 - val_loss: 1.0378 - val_accuracy: 0.8890 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0378 - val_accuracy: 0.8895 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0378 - val_accuracy: 0.8892 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8888 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8837 - val_loss: 1.0378 - val_accuracy: 0.8893 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8836 - val_loss: 1.0378 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8892 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 241/500 235/235 [==============================] - 2s 10ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8895 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8894 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0380 - val_accuracy: 0.8891 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8837 - val_loss: 1.0379 - val_accuracy: 0.8892 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8837 - val_loss: 1.0380 - val_accuracy: 0.8893 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8893 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8835 - val_loss: 1.0380 - val_accuracy: 0.8894 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0377 - val_accuracy: 0.8893 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0589 - accuracy: 0.8835 - val_loss: 1.0379 - val_accuracy: 0.8894 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0588 - accuracy: 0.8836 - val_loss: 1.0379 - val_accuracy: 0.8889 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059482296137339 Epoch 251/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2557 - accuracy: 0.8450 - val_loss: 1.1934 - val_accuracy: 0.8707 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2080 - accuracy: 0.8680 - val_loss: 1.1836 - val_accuracy: 0.8750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2019 - accuracy: 0.8697 - val_loss: 1.1802 - val_accuracy: 0.8756 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1994 - accuracy: 0.8698 - val_loss: 1.1785 - val_accuracy: 0.8768 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1981 - accuracy: 0.8701 - val_loss: 1.1777 - val_accuracy: 0.8766 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1973 - accuracy: 0.8702 - val_loss: 1.1772 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1969 - accuracy: 0.8705 - val_loss: 1.1769 - val_accuracy: 0.8769 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1965 - accuracy: 0.8702 - val_loss: 1.1767 - val_accuracy: 0.8766 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1963 - accuracy: 0.8703 - val_loss: 1.1765 - val_accuracy: 0.8765 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1962 - accuracy: 0.8703 - val_loss: 1.1763 - val_accuracy: 0.8763 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1960 - accuracy: 0.8704 - val_loss: 1.1763 - val_accuracy: 0.8765 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1960 - accuracy: 0.8705 - val_loss: 1.1762 - val_accuracy: 0.8765 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1959 - accuracy: 0.8703 - val_loss: 1.1762 - val_accuracy: 0.8764 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1958 - accuracy: 0.8704 - val_loss: 1.1761 - val_accuracy: 0.8767 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1958 - accuracy: 0.8703 - val_loss: 1.1761 - val_accuracy: 0.8773 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1957 - accuracy: 0.8705 - val_loss: 1.1761 - val_accuracy: 0.8772 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 267/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1957 - accuracy: 0.8705 - val_loss: 1.1761 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1957 - accuracy: 0.8705 - val_loss: 1.1760 - val_accuracy: 0.8773 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8772 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1760 - val_accuracy: 0.8772 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1957 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8773 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8704 - val_loss: 1.1759 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1760 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1760 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8774 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1758 - val_accuracy: 0.8774 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1955 - accuracy: 0.8708 - val_loss: 1.1759 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1760 - val_accuracy: 0.8773 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8708 - val_loss: 1.1758 - val_accuracy: 0.8773 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1955 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8768 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1758 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 290/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1759 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8771 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1758 - val_accuracy: 0.8772 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1955 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8769 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1956 - accuracy: 0.8705 - val_loss: 1.1758 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1955 - accuracy: 0.8707 - val_loss: 1.1759 - val_accuracy: 0.8768 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1955 - accuracy: 0.8708 - val_loss: 1.1758 - val_accuracy: 0.8769 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1955 - accuracy: 0.8708 - val_loss: 1.1759 - val_accuracy: 0.8772 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8706 - val_loss: 1.1758 - val_accuracy: 0.8768 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1956 - accuracy: 0.8707 - val_loss: 1.1758 - val_accuracy: 0.8770 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.946938707081545 Epoch 301/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4323 - accuracy: 0.7466 - val_loss: 1.3752 - val_accuracy: 0.7682 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 302/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3829 - accuracy: 0.7654 - val_loss: 1.3633 - val_accuracy: 0.7734 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3769 - accuracy: 0.7677 - val_loss: 1.3599 - val_accuracy: 0.7735 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3751 - accuracy: 0.7688 - val_loss: 1.3585 - val_accuracy: 0.7738 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 305/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3742 - accuracy: 0.7693 - val_loss: 1.3578 - val_accuracy: 0.7744 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3738 - accuracy: 0.7694 - val_loss: 1.3573 - val_accuracy: 0.7747 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 307/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3735 - accuracy: 0.7697 - val_loss: 1.3570 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3733 - accuracy: 0.7699 - val_loss: 1.3568 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3731 - accuracy: 0.7698 - val_loss: 1.3566 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3730 - accuracy: 0.7699 - val_loss: 1.3564 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3730 - accuracy: 0.7698 - val_loss: 1.3564 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3729 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3728 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3728 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7747 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 315/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3728 - accuracy: 0.7699 - val_loss: 1.3563 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3562 - val_accuracy: 0.7749 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3562 - val_accuracy: 0.7747 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3562 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3562 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 320/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3562 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 321/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 323/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7749 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3561 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7699 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7703 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3726 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7702 - val_loss: 1.3561 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3560 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3560 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3560 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7749 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7748 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3726 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3727 - accuracy: 0.7700 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3561 - val_accuracy: 0.7750 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3726 - accuracy: 0.7701 - val_loss: 1.3560 - val_accuracy: 0.7751 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3727 - accuracy: 0.7701 - val_loss: 1.3560 - val_accuracy: 0.7752 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9717341738197425 Epoch 351/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7640 - accuracy: 0.5156 - val_loss: 1.7262 - val_accuracy: 0.5298 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7226 - accuracy: 0.5956 - val_loss: 1.7166 - val_accuracy: 0.6094 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7173 - accuracy: 0.6056 - val_loss: 1.7138 - val_accuracy: 0.6100 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.6063 - val_loss: 1.7124 - val_accuracy: 0.6102 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7142 - accuracy: 0.6067 - val_loss: 1.7115 - val_accuracy: 0.6102 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.6067 - val_loss: 1.7110 - val_accuracy: 0.6106 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.6066 - val_loss: 1.7106 - val_accuracy: 0.6108 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7128 - accuracy: 0.6067 - val_loss: 1.7102 - val_accuracy: 0.6105 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7125 - accuracy: 0.6068 - val_loss: 1.7100 - val_accuracy: 0.6107 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7123 - accuracy: 0.6068 - val_loss: 1.7098 - val_accuracy: 0.6105 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7122 - accuracy: 0.6069 - val_loss: 1.7096 - val_accuracy: 0.6106 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7121 - accuracy: 0.6069 - val_loss: 1.7096 - val_accuracy: 0.6105 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7120 - accuracy: 0.6068 - val_loss: 1.7095 - val_accuracy: 0.6106 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7120 - accuracy: 0.6067 - val_loss: 1.7094 - val_accuracy: 0.6105 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7119 - accuracy: 0.6067 - val_loss: 1.7093 - val_accuracy: 0.6109 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7118 - accuracy: 0.6066 - val_loss: 1.7093 - val_accuracy: 0.6109 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.6066 - val_loss: 1.7093 - val_accuracy: 0.6111 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.6066 - val_loss: 1.7092 - val_accuracy: 0.6109 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6066 - val_loss: 1.7092 - val_accuracy: 0.6110 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6068 - val_loss: 1.7092 - val_accuracy: 0.6110 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6068 - val_loss: 1.7092 - val_accuracy: 0.6109 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7117 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6109 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7117 - accuracy: 0.6069 - val_loss: 1.7092 - val_accuracy: 0.6108 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7116 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6110 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6109 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.6067 - val_loss: 1.7091 - val_accuracy: 0.6110 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7116 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6111 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 378/500 235/235 [==============================] - 2s 10ms/step - loss: 1.7116 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6111 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7116 - accuracy: 0.6068 - val_loss: 1.7091 - val_accuracy: 0.6110 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7090 - val_accuracy: 0.6111 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7090 - val_accuracy: 0.6111 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6110 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7091 - val_accuracy: 0.6112 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6069 - val_loss: 1.7090 - val_accuracy: 0.6111 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 385/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6070 - val_loss: 1.7090 - val_accuracy: 0.6112 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6112 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7115 - accuracy: 0.6070 - val_loss: 1.7090 - val_accuracy: 0.6112 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6112 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6115 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6115 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7115 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6113 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 396/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7089 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7089 - val_accuracy: 0.6114 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6071 - val_loss: 1.7090 - val_accuracy: 0.6117 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7114 - accuracy: 0.6072 - val_loss: 1.7090 - val_accuracy: 0.6115 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9843414699570815 Epoch 401/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7768 - accuracy: 0.5136 - val_loss: 1.7602 - val_accuracy: 0.5430 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 402/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7557 - accuracy: 0.5387 - val_loss: 1.7520 - val_accuracy: 0.5431 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7518 - accuracy: 0.5390 - val_loss: 1.7501 - val_accuracy: 0.5425 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7509 - accuracy: 0.5391 - val_loss: 1.7496 - val_accuracy: 0.5424 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7506 - accuracy: 0.5392 - val_loss: 1.7494 - val_accuracy: 0.5426 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5393 - val_loss: 1.7493 - val_accuracy: 0.5426 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5426 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 410/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 418/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5426 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5429 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5426 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5425 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9892871512875536 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 462/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9892871512875536 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 476/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 480/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 483/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 484/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5390 - val_loss: 1.7493 - val_accuracy: 0.5427 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5392 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5389 - val_loss: 1.7492 - val_accuracy: 0.5429 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5390 - val_loss: 1.7492 - val_accuracy: 0.5428 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7493 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 495/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7504 - accuracy: 0.5392 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9892871512875536 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7505 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7504 - accuracy: 0.5391 - val_loss: 1.7492 - val_accuracy: 0.5428 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9892871512875536 Epoch 1/500 235/235 [==============================] - 4s 9ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.2541 - val_accuracy: 0.9718 [ 0. 0. -0. ... -0. 0.5798692 0. ] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3818e-04 - accuracy: 0.9997 - val_loss: 0.2485 - val_accuracy: 0.9722 [ 0. 0. -0. ... -0. 0.5817863 -0. ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8454e-04 - accuracy: 0.9999 - val_loss: 0.2441 - val_accuracy: 0.9742 [ 0. 0. -0. ... -0. 0.58657855 0. ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6396e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9737 [ 0. 0. -0. ... -0. 0.58737755 0. ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8413e-05 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.58724725 0. ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4000e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9743 [ 0. 0. -0. ... -0. 0.58759534 0. ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1905e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0.5878739 0. ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0487e-05 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9747 [ 0. 0. -0. ... -0. 0.5881475 0. ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3982e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9748 [ 0. 0. -0. ... -0. 0.5884196 0. ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5122e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9747 [ 0. 0. -0. ... -0. 0.5886924 0. ] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7713e-06 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9746 [ 0. 0. -0. ... -0. 0.58897007 0. ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 7.1351e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9746 [ 0. 0. -0. ... -0. 0.58925337 0. ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5787e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9746 [ 0. 0. -0. ... -0. 0.58954704 0. ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0861e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9747 [ 0. 0. -0. ... 0. 0.58984965 0. ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6436e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9746 [ 0. 0. -0. ... -0. 0.5901675 0. ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 5.2441e-06 - accuracy: 1.0000 - val_loss: 0.2412 - val_accuracy: 0.9744 [ 0. 0. -0. ... -0. 0.5904983 0. ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 10ms/step - loss: 4.8797e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0.59084624 0. ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 4.5464e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9745 [ 0. 0. -0. ... 0. 0.59121466 0. ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2397e-06 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9746 [ 0. 0. -0. ... -0. 0.59160346 0. ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9567e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9744 [ 0. 0. -0. ... -0. 0.5920179 0. ] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6934e-06 - accuracy: 1.0000 - val_loss: 0.2414 - val_accuracy: 0.9744 [ 0. 0. -0. ... -0. 0.59245455 0. ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4487e-06 - accuracy: 1.0000 - val_loss: 0.2415 - val_accuracy: 0.9745 [ 0. 0. -0. ... 0. 0.5929268 0. ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2210e-06 - accuracy: 1.0000 - val_loss: 0.2416 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0.5934342 0. ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0079e-06 - accuracy: 1.0000 - val_loss: 0.2417 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0.5939757 0. ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8102e-06 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9743 [ 0. 0. -0. ... -0. 0.59456307 0. ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6242e-06 - accuracy: 1.0000 - val_loss: 0.2419 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0.5951844 0. ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4502e-06 - accuracy: 1.0000 - val_loss: 0.2421 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0.5958549 0. ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2876e-06 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9742 [ 0. 0. -0. ... -0. 0.59657866 0. ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1337e-06 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9742 [ 0. 0. -0. ... -0. 0.59734833 0. ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9909e-06 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9740 [ 0. 0. -0. ... -0. 0.59817684 0. ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8564e-06 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.9740 [ 0. 0. -0. ... 0. 0.59905386 0. ] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7293e-06 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.59998876 0. ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6108e-06 - accuracy: 1.0000 - val_loss: 0.2435 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.6009811 0. ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4997e-06 - accuracy: 1.0000 - val_loss: 0.2438 - val_accuracy: 0.9738 [ 0. 0. -0. ... 0. 0.60203713 0. ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3952e-06 - accuracy: 1.0000 - val_loss: 0.2442 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.6031557 0. ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2971e-06 - accuracy: 1.0000 - val_loss: 0.2446 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.60432386 0. ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2055e-06 - accuracy: 1.0000 - val_loss: 0.2450 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.6055551 0. ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1196e-06 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9739 [ 0. 0. -0. ... 0. 0.6068444 0. ] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0387e-06 - accuracy: 1.0000 - val_loss: 0.2459 - val_accuracy: 0.9738 [ 0. 0. -0. ... 0. 0.608189 0. ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 9.6378e-07 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9737 [ 0. 0. -0. ... 0. 0.6095982 0. ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 8.9336e-07 - accuracy: 1.0000 - val_loss: 0.2469 - val_accuracy: 0.9740 [ 0. 0. -0. ... 0. 0.61105037 0. ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2757e-07 - accuracy: 1.0000 - val_loss: 0.2475 - val_accuracy: 0.9740 [ 0. 0. -0. ... 0. 0.61256397 0. ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6626e-07 - accuracy: 1.0000 - val_loss: 0.2481 - val_accuracy: 0.9740 [ 0. 0. -0. ... 0. 0.6141519 0. ] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0855e-07 - accuracy: 1.0000 - val_loss: 0.2487 - val_accuracy: 0.9740 [ 0. 0. -0. ... 0. 0.61576706 0. ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5538e-07 - accuracy: 1.0000 - val_loss: 0.2494 - val_accuracy: 0.9740 [ 0. 0. -0. ... 0. 0.6174415 0. ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0573e-07 - accuracy: 1.0000 - val_loss: 0.2500 - val_accuracy: 0.9741 [ 0. 0. -0. ... 0. 0.61917865 0. ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5909e-07 - accuracy: 1.0000 - val_loss: 0.2507 - val_accuracy: 0.9742 [ 0. 0. -0. ... 0. 0.6209467 0. ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1612e-07 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9742 [ 0. 0. -0. ... 0. 0.62279207 0. ] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7616e-07 - accuracy: 1.0000 - val_loss: 0.2522 - val_accuracy: 0.9743 [ 0. 0. -0. ... 0. 0.62463796 0. ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3905e-07 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9743 [ 0. 0. -0. ... 0. 0.6265392 0. ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0249 - accuracy: 0.9924 - val_loss: 0.1987 - val_accuracy: 0.9741 [ 0. 0. -0. ... -0. -0. -0.] Sparsity at: 0.6458724517167382 Epoch 52/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.1947 - val_accuracy: 0.9745 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.6458724517167382 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0493e-04 - accuracy: 0.9999 - val_loss: 0.1921 - val_accuracy: 0.9753 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 3.6043e-04 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9752 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5860e-04 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9750 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1958e-04 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9750 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9434e-04 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9750 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7441e-04 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9748 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5811e-04 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9749 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4400e-04 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9749 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3169e-04 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9749 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2083e-04 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9749 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1108e-04 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9751 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0218e-04 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9753 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 9.4111e-05 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9755 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 8.6732e-05 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9755 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9925e-05 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9754 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3677e-05 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9754 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7878e-05 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9752 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2510e-05 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9752 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7529e-05 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9752 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2922e-05 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9753 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8663e-05 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9755 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4734e-05 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9756 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1061e-05 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9755 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7690e-05 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9756 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4597e-05 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9756 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1696e-05 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9006e-05 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6545e-05 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9759 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4278e-05 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2174e-05 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9759 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0260e-05 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8437e-05 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6790e-05 - accuracy: 1.0000 - val_loss: 0.2104 - val_accuracy: 0.9762 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5270e-05 - accuracy: 1.0000 - val_loss: 0.2113 - val_accuracy: 0.9761 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3900e-05 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9761 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2608e-05 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9761 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1448e-05 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9762 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0371e-05 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9763 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 9.3953e-06 - accuracy: 1.0000 - val_loss: 0.2163 - val_accuracy: 0.9763 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 8.5032e-06 - accuracy: 1.0000 - val_loss: 0.2174 - val_accuracy: 0.9763 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 7.6918e-06 - accuracy: 1.0000 - val_loss: 0.2185 - val_accuracy: 0.9764 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9502e-06 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9763 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2768e-06 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9763 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6628e-06 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9762 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1007e-06 - accuracy: 1.0000 - val_loss: 0.2231 - val_accuracy: 0.9761 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6065e-06 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9761 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1407e-06 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7283e-06 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9760 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.6458724517167382 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0397 - accuracy: 0.9885 - val_loss: 0.1696 - val_accuracy: 0.9715 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.1657 - val_accuracy: 0.9714 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0052 - accuracy: 0.9987 - val_loss: 0.1649 - val_accuracy: 0.9719 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.1644 - val_accuracy: 0.9722 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9999 - val_loss: 0.1647 - val_accuracy: 0.9727 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1651 - val_accuracy: 0.9721 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9718 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1662 - val_accuracy: 0.9720 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.1668 - val_accuracy: 0.9719 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9723 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3116e-04 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9727 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3286e-04 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9728 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5030e-04 - accuracy: 1.0000 - val_loss: 0.1697 - val_accuracy: 0.9730 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 6.7847e-04 - accuracy: 1.0000 - val_loss: 0.1704 - val_accuracy: 0.9729 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1487e-04 - accuracy: 1.0000 - val_loss: 0.1713 - val_accuracy: 0.9728 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6012e-04 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9730 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0944e-04 - accuracy: 1.0000 - val_loss: 0.1730 - val_accuracy: 0.9730 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6454e-04 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9729 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2337e-04 - accuracy: 1.0000 - val_loss: 0.1748 - val_accuracy: 0.9728 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8571e-04 - accuracy: 1.0000 - val_loss: 0.1758 - val_accuracy: 0.9729 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5163e-04 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9727 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2110e-04 - accuracy: 1.0000 - val_loss: 0.1780 - val_accuracy: 0.9729 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9283e-04 - accuracy: 1.0000 - val_loss: 0.1791 - val_accuracy: 0.9730 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6661e-04 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9729 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4297e-04 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9730 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2086e-04 - accuracy: 1.0000 - val_loss: 0.1826 - val_accuracy: 0.9730 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0093e-04 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9730 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8265e-04 - accuracy: 1.0000 - val_loss: 0.1852 - val_accuracy: 0.9731 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6589e-04 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9733 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5057e-04 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9731 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3675e-04 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9729 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2381e-04 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9728 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1222e-04 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9727 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0163e-04 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 9.1841e-05 - accuracy: 1.0000 - val_loss: 0.1956 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 8.3015e-05 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9727 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4879e-05 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9727 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7743e-05 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9723 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1115e-05 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9724 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5203e-05 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9723 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9760e-05 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4851e-05 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0384e-05 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.9724 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6411e-05 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2790e-05 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9464e-05 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9725 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6493e-05 - accuracy: 1.0000 - val_loss: 0.2176 - val_accuracy: 0.9726 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3852e-05 - accuracy: 1.0000 - val_loss: 0.2197 - val_accuracy: 0.9726 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1453e-05 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9726 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9262e-05 - accuracy: 1.0000 - val_loss: 0.2238 - val_accuracy: 0.9729 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1001 - accuracy: 0.9732 - val_loss: 0.2054 - val_accuracy: 0.9622 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 152/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0400 - accuracy: 0.9870 - val_loss: 0.1927 - val_accuracy: 0.9642 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0300 - accuracy: 0.9902 - val_loss: 0.1868 - val_accuracy: 0.9646 [ 0. 0. -0. ... -0. 0. -0.] Sparsity at: 0.8448229613733905 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0246 - accuracy: 0.9921 - val_loss: 0.1837 - val_accuracy: 0.9654 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0210 - accuracy: 0.9935 - val_loss: 0.1813 - val_accuracy: 0.9656 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0184 - accuracy: 0.9944 - val_loss: 0.1795 - val_accuracy: 0.9657 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.1784 - val_accuracy: 0.9659 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0146 - accuracy: 0.9958 - val_loss: 0.1775 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0132 - accuracy: 0.9964 - val_loss: 0.1767 - val_accuracy: 0.9669 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0120 - accuracy: 0.9970 - val_loss: 0.1765 - val_accuracy: 0.9670 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0109 - accuracy: 0.9974 - val_loss: 0.1761 - val_accuracy: 0.9672 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0100 - accuracy: 0.9979 - val_loss: 0.1759 - val_accuracy: 0.9670 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.8448229613733905 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0092 - accuracy: 0.9982 - val_loss: 0.1761 - val_accuracy: 0.9664 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0085 - accuracy: 0.9985 - val_loss: 0.1764 - val_accuracy: 0.9663 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0079 - accuracy: 0.9987 - val_loss: 0.1767 - val_accuracy: 0.9663 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0074 - accuracy: 0.9989 - val_loss: 0.1771 - val_accuracy: 0.9661 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0069 - accuracy: 0.9991 - val_loss: 0.1779 - val_accuracy: 0.9659 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0064 - accuracy: 0.9993 - val_loss: 0.1783 - val_accuracy: 0.9656 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0060 - accuracy: 0.9995 - val_loss: 0.1789 - val_accuracy: 0.9655 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0056 - accuracy: 0.9996 - val_loss: 0.1799 - val_accuracy: 0.9658 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.8448229613733905 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0053 - accuracy: 0.9997 - val_loss: 0.1806 - val_accuracy: 0.9657 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.1813 - val_accuracy: 0.9657 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0046 - accuracy: 0.9998 - val_loss: 0.1823 - val_accuracy: 0.9657 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0044 - accuracy: 0.9998 - val_loss: 0.1832 - val_accuracy: 0.9659 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0041 - accuracy: 0.9999 - val_loss: 0.1843 - val_accuracy: 0.9663 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0039 - accuracy: 0.9999 - val_loss: 0.1852 - val_accuracy: 0.9661 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9661 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9661 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 179/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9660 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.1900 - val_accuracy: 0.9663 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9664 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9665 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9664 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9667 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9663 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9665 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9667 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9667 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9667 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2115 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9669 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.2170 - val_accuracy: 0.9668 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7319e-04 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 9.1524e-04 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9666 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.8448229613733905 Epoch 201/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1814 - accuracy: 0.9504 - val_loss: 0.2344 - val_accuracy: 0.9500 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 202/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1017 - accuracy: 0.9680 - val_loss: 0.2128 - val_accuracy: 0.9536 [ 0. 0. -0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0857 - accuracy: 0.9726 - val_loss: 0.2022 - val_accuracy: 0.9546 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0766 - accuracy: 0.9749 - val_loss: 0.1956 - val_accuracy: 0.9558 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0702 - accuracy: 0.9766 - val_loss: 0.1907 - val_accuracy: 0.9565 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0655 - accuracy: 0.9780 - val_loss: 0.1871 - val_accuracy: 0.9570 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0617 - accuracy: 0.9788 - val_loss: 0.1841 - val_accuracy: 0.9578 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0586 - accuracy: 0.9799 - val_loss: 0.1816 - val_accuracy: 0.9581 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0560 - accuracy: 0.9807 - val_loss: 0.1795 - val_accuracy: 0.9587 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0538 - accuracy: 0.9816 - val_loss: 0.1778 - val_accuracy: 0.9587 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0519 - accuracy: 0.9822 - val_loss: 0.1762 - val_accuracy: 0.9593 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0503 - accuracy: 0.9830 - val_loss: 0.1749 - val_accuracy: 0.9593 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0488 - accuracy: 0.9836 - val_loss: 0.1737 - val_accuracy: 0.9598 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0475 - accuracy: 0.9839 - val_loss: 0.1728 - val_accuracy: 0.9603 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0463 - accuracy: 0.9844 - val_loss: 0.1719 - val_accuracy: 0.9602 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0452 - accuracy: 0.9846 - val_loss: 0.1712 - val_accuracy: 0.9603 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0442 - accuracy: 0.9853 - val_loss: 0.1705 - val_accuracy: 0.9600 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0433 - accuracy: 0.9855 - val_loss: 0.1699 - val_accuracy: 0.9601 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 219/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0425 - accuracy: 0.9858 - val_loss: 0.1694 - val_accuracy: 0.9603 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0417 - accuracy: 0.9860 - val_loss: 0.1690 - val_accuracy: 0.9602 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0410 - accuracy: 0.9863 - val_loss: 0.1686 - val_accuracy: 0.9605 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0403 - accuracy: 0.9867 - val_loss: 0.1683 - val_accuracy: 0.9608 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0396 - accuracy: 0.9870 - val_loss: 0.1680 - val_accuracy: 0.9610 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0390 - accuracy: 0.9873 - val_loss: 0.1678 - val_accuracy: 0.9611 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0384 - accuracy: 0.9876 - val_loss: 0.1676 - val_accuracy: 0.9612 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0379 - accuracy: 0.9877 - val_loss: 0.1675 - val_accuracy: 0.9614 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0374 - accuracy: 0.9880 - val_loss: 0.1673 - val_accuracy: 0.9615 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 228/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0369 - accuracy: 0.9880 - val_loss: 0.1672 - val_accuracy: 0.9618 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0364 - accuracy: 0.9883 - val_loss: 0.1671 - val_accuracy: 0.9621 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0359 - accuracy: 0.9886 - val_loss: 0.1671 - val_accuracy: 0.9619 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0355 - accuracy: 0.9887 - val_loss: 0.1671 - val_accuracy: 0.9618 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0350 - accuracy: 0.9888 - val_loss: 0.1671 - val_accuracy: 0.9621 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0346 - accuracy: 0.9889 - val_loss: 0.1672 - val_accuracy: 0.9620 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0342 - accuracy: 0.9891 - val_loss: 0.1672 - val_accuracy: 0.9623 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0338 - accuracy: 0.9894 - val_loss: 0.1673 - val_accuracy: 0.9624 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0334 - accuracy: 0.9895 - val_loss: 0.1674 - val_accuracy: 0.9623 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0331 - accuracy: 0.9897 - val_loss: 0.1675 - val_accuracy: 0.9620 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0327 - accuracy: 0.9898 - val_loss: 0.1676 - val_accuracy: 0.9622 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0324 - accuracy: 0.9901 - val_loss: 0.1678 - val_accuracy: 0.9622 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0320 - accuracy: 0.9903 - val_loss: 0.1680 - val_accuracy: 0.9624 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0317 - accuracy: 0.9904 - val_loss: 0.1681 - val_accuracy: 0.9625 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0314 - accuracy: 0.9905 - val_loss: 0.1683 - val_accuracy: 0.9623 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0311 - accuracy: 0.9906 - val_loss: 0.1685 - val_accuracy: 0.9622 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0308 - accuracy: 0.9907 - val_loss: 0.1688 - val_accuracy: 0.9624 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0305 - accuracy: 0.9908 - val_loss: 0.1690 - val_accuracy: 0.9626 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0302 - accuracy: 0.9909 - val_loss: 0.1692 - val_accuracy: 0.9628 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0299 - accuracy: 0.9911 - val_loss: 0.1695 - val_accuracy: 0.9627 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0296 - accuracy: 0.9911 - val_loss: 0.1697 - val_accuracy: 0.9624 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0293 - accuracy: 0.9912 - val_loss: 0.1700 - val_accuracy: 0.9624 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0291 - accuracy: 0.9913 - val_loss: 0.1703 - val_accuracy: 0.9624 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9059985246781116 Epoch 251/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4915 - accuracy: 0.8496 - val_loss: 0.3458 - val_accuracy: 0.8989 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2890 - accuracy: 0.9065 - val_loss: 0.2948 - val_accuracy: 0.9139 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2541 - accuracy: 0.9188 - val_loss: 0.2722 - val_accuracy: 0.9234 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2357 - accuracy: 0.9247 - val_loss: 0.2588 - val_accuracy: 0.9267 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2235 - accuracy: 0.9287 - val_loss: 0.2494 - val_accuracy: 0.9289 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2145 - accuracy: 0.9315 - val_loss: 0.2424 - val_accuracy: 0.9309 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2074 - accuracy: 0.9340 - val_loss: 0.2368 - val_accuracy: 0.9316 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2016 - accuracy: 0.9358 - val_loss: 0.2322 - val_accuracy: 0.9327 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 259/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1966 - accuracy: 0.9374 - val_loss: 0.2284 - val_accuracy: 0.9340 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1924 - accuracy: 0.9388 - val_loss: 0.2250 - val_accuracy: 0.9348 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1886 - accuracy: 0.9400 - val_loss: 0.2221 - val_accuracy: 0.9360 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1853 - accuracy: 0.9408 - val_loss: 0.2195 - val_accuracy: 0.9364 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1823 - accuracy: 0.9416 - val_loss: 0.2172 - val_accuracy: 0.9369 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1796 - accuracy: 0.9429 - val_loss: 0.2150 - val_accuracy: 0.9382 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1772 - accuracy: 0.9435 - val_loss: 0.2131 - val_accuracy: 0.9390 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1750 - accuracy: 0.9442 - val_loss: 0.2114 - val_accuracy: 0.9393 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1729 - accuracy: 0.9449 - val_loss: 0.2098 - val_accuracy: 0.9391 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 268/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1710 - accuracy: 0.9454 - val_loss: 0.2083 - val_accuracy: 0.9399 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1693 - accuracy: 0.9460 - val_loss: 0.2070 - val_accuracy: 0.9402 [ 0. 0. -0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1677 - accuracy: 0.9467 - val_loss: 0.2057 - val_accuracy: 0.9413 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1662 - accuracy: 0.9474 - val_loss: 0.2045 - val_accuracy: 0.9417 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1648 - accuracy: 0.9478 - val_loss: 0.2033 - val_accuracy: 0.9420 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1635 - accuracy: 0.9482 - val_loss: 0.2023 - val_accuracy: 0.9424 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1622 - accuracy: 0.9485 - val_loss: 0.2013 - val_accuracy: 0.9424 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1611 - accuracy: 0.9488 - val_loss: 0.2004 - val_accuracy: 0.9425 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1600 - accuracy: 0.9491 - val_loss: 0.1995 - val_accuracy: 0.9427 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1590 - accuracy: 0.9495 - val_loss: 0.1987 - val_accuracy: 0.9426 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 278/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1581 - accuracy: 0.9499 - val_loss: 0.1980 - val_accuracy: 0.9430 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1571 - accuracy: 0.9501 - val_loss: 0.1972 - val_accuracy: 0.9431 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1563 - accuracy: 0.9505 - val_loss: 0.1965 - val_accuracy: 0.9432 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1555 - accuracy: 0.9509 - val_loss: 0.1959 - val_accuracy: 0.9435 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1547 - accuracy: 0.9512 - val_loss: 0.1953 - val_accuracy: 0.9438 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1540 - accuracy: 0.9514 - val_loss: 0.1947 - val_accuracy: 0.9441 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1532 - accuracy: 0.9516 - val_loss: 0.1941 - val_accuracy: 0.9440 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1526 - accuracy: 0.9517 - val_loss: 0.1935 - val_accuracy: 0.9442 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1519 - accuracy: 0.9519 - val_loss: 0.1930 - val_accuracy: 0.9449 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1513 - accuracy: 0.9520 - val_loss: 0.1925 - val_accuracy: 0.9450 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1507 - accuracy: 0.9520 - val_loss: 0.1921 - val_accuracy: 0.9451 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1501 - accuracy: 0.9523 - val_loss: 0.1916 - val_accuracy: 0.9451 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1495 - accuracy: 0.9527 - val_loss: 0.1912 - val_accuracy: 0.9454 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1490 - accuracy: 0.9527 - val_loss: 0.1907 - val_accuracy: 0.9456 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1485 - accuracy: 0.9530 - val_loss: 0.1903 - val_accuracy: 0.9458 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1480 - accuracy: 0.9532 - val_loss: 0.1900 - val_accuracy: 0.9461 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1475 - accuracy: 0.9533 - val_loss: 0.1895 - val_accuracy: 0.9463 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1470 - accuracy: 0.9532 - val_loss: 0.1891 - val_accuracy: 0.9464 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1465 - accuracy: 0.9535 - val_loss: 0.1888 - val_accuracy: 0.9465 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 297/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1461 - accuracy: 0.9536 - val_loss: 0.1884 - val_accuracy: 0.9467 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1457 - accuracy: 0.9537 - val_loss: 0.1881 - val_accuracy: 0.9469 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1453 - accuracy: 0.9539 - val_loss: 0.1878 - val_accuracy: 0.9471 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1448 - accuracy: 0.9541 - val_loss: 0.1874 - val_accuracy: 0.9473 [ 0. 0. -0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 301/500 235/235 [==============================] - 2s 8ms/step - loss: 0.9672 - accuracy: 0.6791 - val_loss: 0.8213 - val_accuracy: 0.7273 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 302/500 235/235 [==============================] - 2s 8ms/step - loss: 0.7850 - accuracy: 0.7347 - val_loss: 0.7587 - val_accuracy: 0.7489 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 0.7402 - accuracy: 0.7516 - val_loss: 0.7238 - val_accuracy: 0.7639 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 0.7102 - accuracy: 0.7639 - val_loss: 0.6985 - val_accuracy: 0.7744 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6882 - accuracy: 0.7723 - val_loss: 0.6802 - val_accuracy: 0.7826 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6726 - accuracy: 0.7772 - val_loss: 0.6661 - val_accuracy: 0.7878 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6605 - accuracy: 0.7821 - val_loss: 0.6544 - val_accuracy: 0.7916 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6508 - accuracy: 0.7859 - val_loss: 0.6453 - val_accuracy: 0.7931 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6431 - accuracy: 0.7883 - val_loss: 0.6382 - val_accuracy: 0.7945 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6369 - accuracy: 0.7907 - val_loss: 0.6327 - val_accuracy: 0.7965 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6317 - accuracy: 0.7930 - val_loss: 0.6281 - val_accuracy: 0.7970 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6270 - accuracy: 0.7947 - val_loss: 0.6243 - val_accuracy: 0.7989 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 313/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6228 - accuracy: 0.7965 - val_loss: 0.6210 - val_accuracy: 0.8001 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6192 - accuracy: 0.7976 - val_loss: 0.6183 - val_accuracy: 0.8013 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6160 - accuracy: 0.7988 - val_loss: 0.6159 - val_accuracy: 0.8021 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6133 - accuracy: 0.7999 - val_loss: 0.6139 - val_accuracy: 0.8020 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6108 - accuracy: 0.8009 - val_loss: 0.6120 - val_accuracy: 0.8028 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6086 - accuracy: 0.8015 - val_loss: 0.6104 - val_accuracy: 0.8031 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6066 - accuracy: 0.8017 - val_loss: 0.6089 - val_accuracy: 0.8037 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6047 - accuracy: 0.8023 - val_loss: 0.6075 - val_accuracy: 0.8038 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6030 - accuracy: 0.8027 - val_loss: 0.6062 - val_accuracy: 0.8039 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6015 - accuracy: 0.8032 - val_loss: 0.6051 - val_accuracy: 0.8042 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.6000 - accuracy: 0.8038 - val_loss: 0.6039 - val_accuracy: 0.8054 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5986 - accuracy: 0.8042 - val_loss: 0.6029 - val_accuracy: 0.8057 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5973 - accuracy: 0.8048 - val_loss: 0.6019 - val_accuracy: 0.8064 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5961 - accuracy: 0.8053 - val_loss: 0.6009 - val_accuracy: 0.8063 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5949 - accuracy: 0.8057 - val_loss: 0.6000 - val_accuracy: 0.8071 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5939 - accuracy: 0.8061 - val_loss: 0.5992 - val_accuracy: 0.8074 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5928 - accuracy: 0.8066 - val_loss: 0.5984 - val_accuracy: 0.8082 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5918 - accuracy: 0.8069 - val_loss: 0.5976 - val_accuracy: 0.8088 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5908 - accuracy: 0.8073 - val_loss: 0.5967 - val_accuracy: 0.8093 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5898 - accuracy: 0.8076 - val_loss: 0.5960 - val_accuracy: 0.8094 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5888 - accuracy: 0.8078 - val_loss: 0.5952 - val_accuracy: 0.8101 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5879 - accuracy: 0.8084 - val_loss: 0.5944 - val_accuracy: 0.8098 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5869 - accuracy: 0.8089 - val_loss: 0.5936 - val_accuracy: 0.8105 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5859 - accuracy: 0.8091 - val_loss: 0.5929 - val_accuracy: 0.8111 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5850 - accuracy: 0.8096 - val_loss: 0.5921 - val_accuracy: 0.8121 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5842 - accuracy: 0.8097 - val_loss: 0.5914 - val_accuracy: 0.8123 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5833 - accuracy: 0.8101 - val_loss: 0.5907 - val_accuracy: 0.8130 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5825 - accuracy: 0.8103 - val_loss: 0.5899 - val_accuracy: 0.8131 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5818 - accuracy: 0.8104 - val_loss: 0.5893 - val_accuracy: 0.8132 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 342/500 235/235 [==============================] - 2s 10ms/step - loss: 0.5811 - accuracy: 0.8105 - val_loss: 0.5886 - val_accuracy: 0.8135 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5804 - accuracy: 0.8110 - val_loss: 0.5880 - val_accuracy: 0.8139 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5798 - accuracy: 0.8112 - val_loss: 0.5874 - val_accuracy: 0.8137 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5792 - accuracy: 0.8114 - val_loss: 0.5868 - val_accuracy: 0.8142 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5786 - accuracy: 0.8115 - val_loss: 0.5863 - val_accuracy: 0.8141 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5781 - accuracy: 0.8119 - val_loss: 0.5858 - val_accuracy: 0.8144 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 348/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5776 - accuracy: 0.8121 - val_loss: 0.5853 - val_accuracy: 0.8141 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5770 - accuracy: 0.8122 - val_loss: 0.5848 - val_accuracy: 0.8140 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5765 - accuracy: 0.8124 - val_loss: 0.5843 - val_accuracy: 0.8145 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9718515289699571 Epoch 351/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8291 - accuracy: 0.4167 - val_loss: 1.5087 - val_accuracy: 0.4377 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 352/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5259 - accuracy: 0.4839 - val_loss: 1.4726 - val_accuracy: 0.4964 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5000 - accuracy: 0.4892 - val_loss: 1.4585 - val_accuracy: 0.4989 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4876 - accuracy: 0.5044 - val_loss: 1.4501 - val_accuracy: 0.4996 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4798 - accuracy: 0.5284 - val_loss: 1.4440 - val_accuracy: 0.5456 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4735 - accuracy: 0.5385 - val_loss: 1.4378 - val_accuracy: 0.5486 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4652 - accuracy: 0.5419 - val_loss: 1.4260 - val_accuracy: 0.5546 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4508 - accuracy: 0.5497 - val_loss: 1.4143 - val_accuracy: 0.5588 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4414 - accuracy: 0.5541 - val_loss: 1.4076 - val_accuracy: 0.5607 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4350 - accuracy: 0.5570 - val_loss: 1.4023 - val_accuracy: 0.5625 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4297 - accuracy: 0.5595 - val_loss: 1.3977 - val_accuracy: 0.5641 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4249 - accuracy: 0.5612 - val_loss: 1.3934 - val_accuracy: 0.5656 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4203 - accuracy: 0.5628 - val_loss: 1.3892 - val_accuracy: 0.5682 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4156 - accuracy: 0.5646 - val_loss: 1.3849 - val_accuracy: 0.5698 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4109 - accuracy: 0.5663 - val_loss: 1.3810 - val_accuracy: 0.5718 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4072 - accuracy: 0.5681 - val_loss: 1.3783 - val_accuracy: 0.5730 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4044 - accuracy: 0.5689 - val_loss: 1.3763 - val_accuracy: 0.5737 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4023 - accuracy: 0.5690 - val_loss: 1.3747 - val_accuracy: 0.5738 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4006 - accuracy: 0.5695 - val_loss: 1.3733 - val_accuracy: 0.5741 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3990 - accuracy: 0.5699 - val_loss: 1.3722 - val_accuracy: 0.5753 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3976 - accuracy: 0.5705 - val_loss: 1.3711 - val_accuracy: 0.5761 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3963 - accuracy: 0.5709 - val_loss: 1.3700 - val_accuracy: 0.5769 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3951 - accuracy: 0.5714 - val_loss: 1.3691 - val_accuracy: 0.5772 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3941 - accuracy: 0.5717 - val_loss: 1.3681 - val_accuracy: 0.5773 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3930 - accuracy: 0.5718 - val_loss: 1.3673 - val_accuracy: 0.5776 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3921 - accuracy: 0.5725 - val_loss: 1.3665 - val_accuracy: 0.5780 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3911 - accuracy: 0.5727 - val_loss: 1.3657 - val_accuracy: 0.5774 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3902 - accuracy: 0.5730 - val_loss: 1.3649 - val_accuracy: 0.5775 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3894 - accuracy: 0.5732 - val_loss: 1.3642 - val_accuracy: 0.5777 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3886 - accuracy: 0.5732 - val_loss: 1.3634 - val_accuracy: 0.5779 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3878 - accuracy: 0.5737 - val_loss: 1.3628 - val_accuracy: 0.5778 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3871 - accuracy: 0.5740 - val_loss: 1.3621 - val_accuracy: 0.5785 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3863 - accuracy: 0.5742 - val_loss: 1.3615 - val_accuracy: 0.5782 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3856 - accuracy: 0.5739 - val_loss: 1.3609 - val_accuracy: 0.5785 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3850 - accuracy: 0.5742 - val_loss: 1.3604 - val_accuracy: 0.5791 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3843 - accuracy: 0.5746 - val_loss: 1.3598 - val_accuracy: 0.5793 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3837 - accuracy: 0.5744 - val_loss: 1.3593 - val_accuracy: 0.5800 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3832 - accuracy: 0.5742 - val_loss: 1.3587 - val_accuracy: 0.5800 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3826 - accuracy: 0.5741 - val_loss: 1.3582 - val_accuracy: 0.5790 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3821 - accuracy: 0.5736 - val_loss: 1.3577 - val_accuracy: 0.5764 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3815 - accuracy: 0.5733 - val_loss: 1.3573 - val_accuracy: 0.5762 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3811 - accuracy: 0.5735 - val_loss: 1.3568 - val_accuracy: 0.5765 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3806 - accuracy: 0.5733 - val_loss: 1.3564 - val_accuracy: 0.5768 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3802 - accuracy: 0.5731 - val_loss: 1.3560 - val_accuracy: 0.5768 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3797 - accuracy: 0.5730 - val_loss: 1.3556 - val_accuracy: 0.5772 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3793 - accuracy: 0.5727 - val_loss: 1.3552 - val_accuracy: 0.5778 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 397/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3788 - accuracy: 0.5727 - val_loss: 1.3547 - val_accuracy: 0.5780 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9846097103004292 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3784 - accuracy: 0.5727 - val_loss: 1.3542 - val_accuracy: 0.5782 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3780 - accuracy: 0.5729 - val_loss: 1.3538 - val_accuracy: 0.5788 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3775 - accuracy: 0.5730 - val_loss: 1.3533 - val_accuracy: 0.5795 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 401/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8041 - accuracy: 0.3775 - val_loss: 1.7264 - val_accuracy: 0.3880 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7310 - accuracy: 0.3974 - val_loss: 1.7223 - val_accuracy: 0.3890 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7278 - accuracy: 0.3990 - val_loss: 1.7205 - val_accuracy: 0.3885 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7258 - accuracy: 0.3988 - val_loss: 1.7192 - val_accuracy: 0.3881 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7244 - accuracy: 0.3995 - val_loss: 1.7182 - val_accuracy: 0.3880 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7231 - accuracy: 0.3987 - val_loss: 1.7174 - val_accuracy: 0.3886 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7222 - accuracy: 0.3996 - val_loss: 1.7167 - val_accuracy: 0.3885 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7214 - accuracy: 0.3993 - val_loss: 1.7162 - val_accuracy: 0.3887 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7209 - accuracy: 0.3994 - val_loss: 1.7158 - val_accuracy: 0.3888 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7204 - accuracy: 0.4005 - val_loss: 1.7155 - val_accuracy: 0.3887 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7201 - accuracy: 0.4003 - val_loss: 1.7152 - val_accuracy: 0.3892 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7198 - accuracy: 0.4006 - val_loss: 1.7149 - val_accuracy: 0.3892 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7195 - accuracy: 0.4006 - val_loss: 1.7147 - val_accuracy: 0.3890 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7193 - accuracy: 0.4002 - val_loss: 1.7145 - val_accuracy: 0.3890 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7191 - accuracy: 0.4007 - val_loss: 1.7142 - val_accuracy: 0.3893 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7189 - accuracy: 0.4014 - val_loss: 1.7141 - val_accuracy: 0.3896 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7187 - accuracy: 0.4007 - val_loss: 1.7139 - val_accuracy: 0.3896 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7185 - accuracy: 0.4004 - val_loss: 1.7138 - val_accuracy: 0.3899 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7184 - accuracy: 0.4012 - val_loss: 1.7136 - val_accuracy: 0.3898 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7182 - accuracy: 0.4010 - val_loss: 1.7135 - val_accuracy: 0.3897 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7181 - accuracy: 0.4018 - val_loss: 1.7133 - val_accuracy: 0.3896 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7180 - accuracy: 0.4011 - val_loss: 1.7132 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7178 - accuracy: 0.4015 - val_loss: 1.7131 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7177 - accuracy: 0.4005 - val_loss: 1.7130 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7176 - accuracy: 0.4011 - val_loss: 1.7129 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7175 - accuracy: 0.4018 - val_loss: 1.7128 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7173 - accuracy: 0.4016 - val_loss: 1.7127 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7172 - accuracy: 0.4018 - val_loss: 1.7126 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7171 - accuracy: 0.4016 - val_loss: 1.7125 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7170 - accuracy: 0.4022 - val_loss: 1.7124 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7169 - accuracy: 0.4019 - val_loss: 1.7124 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7168 - accuracy: 0.4017 - val_loss: 1.7123 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7167 - accuracy: 0.4017 - val_loss: 1.7122 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7166 - accuracy: 0.4023 - val_loss: 1.7121 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7165 - accuracy: 0.4026 - val_loss: 1.7121 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7164 - accuracy: 0.4025 - val_loss: 1.7120 - val_accuracy: 0.3899 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7162 - accuracy: 0.4023 - val_loss: 1.7119 - val_accuracy: 0.3899 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7162 - accuracy: 0.4027 - val_loss: 1.7119 - val_accuracy: 0.3899 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7161 - accuracy: 0.4028 - val_loss: 1.7118 - val_accuracy: 0.3898 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7160 - accuracy: 0.4026 - val_loss: 1.7117 - val_accuracy: 0.3898 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7159 - accuracy: 0.4023 - val_loss: 1.7117 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7158 - accuracy: 0.4031 - val_loss: 1.7116 - val_accuracy: 0.3899 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.4026 - val_loss: 1.7116 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7157 - accuracy: 0.4031 - val_loss: 1.7115 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7156 - accuracy: 0.4028 - val_loss: 1.7114 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7155 - accuracy: 0.4028 - val_loss: 1.7113 - val_accuracy: 0.3899 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7154 - accuracy: 0.4029 - val_loss: 1.7113 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.4033 - val_loss: 1.7113 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7153 - accuracy: 0.4030 - val_loss: 1.7112 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7152 - accuracy: 0.4036 - val_loss: 1.7111 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7151 - accuracy: 0.4036 - val_loss: 1.7111 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7150 - accuracy: 0.4030 - val_loss: 1.7110 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.4035 - val_loss: 1.7110 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7149 - accuracy: 0.4035 - val_loss: 1.7109 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7148 - accuracy: 0.4030 - val_loss: 1.7109 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7147 - accuracy: 0.4037 - val_loss: 1.7108 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7146 - accuracy: 0.4028 - val_loss: 1.7107 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7145 - accuracy: 0.4036 - val_loss: 1.7107 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7144 - accuracy: 0.4035 - val_loss: 1.7106 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7144 - accuracy: 0.4036 - val_loss: 1.7105 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7143 - accuracy: 0.4040 - val_loss: 1.7105 - val_accuracy: 0.3904 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7142 - accuracy: 0.4037 - val_loss: 1.7104 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7141 - accuracy: 0.4038 - val_loss: 1.7103 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7140 - accuracy: 0.4041 - val_loss: 1.7102 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7138 - accuracy: 0.4034 - val_loss: 1.7101 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7137 - accuracy: 0.4044 - val_loss: 1.7099 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7136 - accuracy: 0.4039 - val_loss: 1.7098 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7134 - accuracy: 0.4040 - val_loss: 1.7096 - val_accuracy: 0.3902 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7132 - accuracy: 0.4041 - val_loss: 1.7094 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7131 - accuracy: 0.4038 - val_loss: 1.7092 - val_accuracy: 0.3903 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7129 - accuracy: 0.4048 - val_loss: 1.7089 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7127 - accuracy: 0.4035 - val_loss: 1.7088 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7126 - accuracy: 0.4042 - val_loss: 1.7086 - val_accuracy: 0.3900 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7124 - accuracy: 0.4049 - val_loss: 1.7085 - val_accuracy: 0.3901 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7123 - accuracy: 0.4042 - val_loss: 1.7084 - val_accuracy: 0.3907 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7122 - accuracy: 0.4047 - val_loss: 1.7083 - val_accuracy: 0.3907 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7122 - accuracy: 0.4055 - val_loss: 1.7082 - val_accuracy: 0.3907 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7121 - accuracy: 0.4056 - val_loss: 1.7081 - val_accuracy: 0.3908 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7120 - accuracy: 0.4053 - val_loss: 1.7080 - val_accuracy: 0.3907 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7119 - accuracy: 0.4053 - val_loss: 1.7079 - val_accuracy: 0.3906 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.4055 - val_loss: 1.7078 - val_accuracy: 0.3909 [ 0. 0. -0. ... 0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7118 - accuracy: 0.4058 - val_loss: 1.7077 - val_accuracy: 0.3909 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7117 - accuracy: 0.4049 - val_loss: 1.7076 - val_accuracy: 0.3908 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.4052 - val_loss: 1.7075 - val_accuracy: 0.3908 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7116 - accuracy: 0.4055 - val_loss: 1.7074 - val_accuracy: 0.3909 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7115 - accuracy: 0.4054 - val_loss: 1.7073 - val_accuracy: 0.3910 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7115 - accuracy: 0.4057 - val_loss: 1.7072 - val_accuracy: 0.3910 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.4062 - val_loss: 1.7071 - val_accuracy: 0.3912 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7114 - accuracy: 0.4053 - val_loss: 1.7071 - val_accuracy: 0.3914 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7113 - accuracy: 0.4061 - val_loss: 1.7070 - val_accuracy: 0.3914 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7112 - accuracy: 0.4057 - val_loss: 1.7069 - val_accuracy: 0.3914 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7112 - accuracy: 0.4056 - val_loss: 1.7069 - val_accuracy: 0.3914 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7111 - accuracy: 0.4054 - val_loss: 1.7068 - val_accuracy: 0.3913 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7111 - accuracy: 0.4056 - val_loss: 1.7068 - val_accuracy: 0.3914 [ 0. 0. -0. ... 0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7110 - accuracy: 0.4056 - val_loss: 1.7067 - val_accuracy: 0.3913 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7110 - accuracy: 0.4065 - val_loss: 1.7067 - val_accuracy: 0.3913 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7109 - accuracy: 0.4061 - val_loss: 1.7066 - val_accuracy: 0.3913 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7109 - accuracy: 0.4054 - val_loss: 1.7066 - val_accuracy: 0.3915 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.4060 - val_loss: 1.7066 - val_accuracy: 0.3915 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7108 - accuracy: 0.4058 - val_loss: 1.7066 - val_accuracy: 0.3916 [ 0. 0. -0. ... 0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 1/200 235/235 [==============================] - 4s 14ms/step - loss: 2.2210 - accuracy: 0.9245 - val_loss: 1.5419 - val_accuracy: 0.8959 Epoch 2/200 235/235 [==============================] - 3s 13ms/step - loss: 0.4462 - accuracy: 0.9601 - val_loss: 0.4865 - val_accuracy: 0.9515 Epoch 3/200 235/235 [==============================] - 3s 13ms/step - loss: 0.3127 - accuracy: 0.9630 - val_loss: 0.3147 - val_accuracy: 0.9588 Epoch 4/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2773 - accuracy: 0.9666 - val_loss: 0.3279 - val_accuracy: 0.9445 Epoch 5/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2607 - accuracy: 0.9677 - val_loss: 0.3167 - val_accuracy: 0.9451 Epoch 6/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2469 - accuracy: 0.9691 - val_loss: 0.2823 - val_accuracy: 0.9528 Epoch 7/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2348 - accuracy: 0.9700 - val_loss: 0.2940 - val_accuracy: 0.9483 Epoch 8/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2305 - accuracy: 0.9706 - val_loss: 0.2694 - val_accuracy: 0.9551 Epoch 9/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2201 - accuracy: 0.9717 - val_loss: 0.2682 - val_accuracy: 0.9550 Epoch 10/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2160 - accuracy: 0.9708 - val_loss: 0.3005 - val_accuracy: 0.9433 Epoch 11/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2135 - accuracy: 0.9709 - val_loss: 0.2479 - val_accuracy: 0.9599 Epoch 12/200 235/235 [==============================] - 3s 13ms/step - loss: 0.2058 - accuracy: 0.9725 - val_loss: 0.2911 - val_accuracy: 0.9425 Epoch 13/200 235/235 [==============================] - 3s 14ms/step - loss: 0.2042 - accuracy: 0.9727 - val_loss: 0.2796 - val_accuracy: 0.9451 Epoch 14/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1971 - accuracy: 0.9724 - val_loss: 0.2358 - val_accuracy: 0.9592 Epoch 15/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1965 - accuracy: 0.9727 - val_loss: 0.2443 - val_accuracy: 0.9591 Epoch 16/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1983 - accuracy: 0.9719 - val_loss: 0.2603 - val_accuracy: 0.9516 Epoch 17/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1946 - accuracy: 0.9721 - val_loss: 0.2690 - val_accuracy: 0.9488 Epoch 18/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1879 - accuracy: 0.9737 - val_loss: 0.2379 - val_accuracy: 0.9562 Epoch 19/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1877 - accuracy: 0.9726 - val_loss: 0.2539 - val_accuracy: 0.9522 Epoch 20/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1844 - accuracy: 0.9738 - val_loss: 0.2654 - val_accuracy: 0.9481 Epoch 21/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1796 - accuracy: 0.9745 - val_loss: 0.2283 - val_accuracy: 0.9585 Epoch 22/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1822 - accuracy: 0.9731 - val_loss: 0.2313 - val_accuracy: 0.9578 Epoch 23/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1817 - accuracy: 0.9736 - val_loss: 0.2639 - val_accuracy: 0.9468 Epoch 24/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1767 - accuracy: 0.9744 - val_loss: 0.2216 - val_accuracy: 0.9627 Epoch 25/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1750 - accuracy: 0.9743 - val_loss: 0.2460 - val_accuracy: 0.9533 Epoch 26/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1787 - accuracy: 0.9740 - val_loss: 0.2144 - val_accuracy: 0.9627 Epoch 27/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1741 - accuracy: 0.9747 - val_loss: 0.2404 - val_accuracy: 0.9560 Epoch 28/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1725 - accuracy: 0.9746 - val_loss: 0.2374 - val_accuracy: 0.9547 Epoch 29/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1737 - accuracy: 0.9741 - val_loss: 0.2183 - val_accuracy: 0.9608 Epoch 30/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1701 - accuracy: 0.9751 - val_loss: 0.2454 - val_accuracy: 0.9509 Epoch 31/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1695 - accuracy: 0.9747 - val_loss: 0.2287 - val_accuracy: 0.9568 Epoch 32/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1691 - accuracy: 0.9743 - val_loss: 0.2136 - val_accuracy: 0.9627 Epoch 33/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1668 - accuracy: 0.9753 - val_loss: 0.2550 - val_accuracy: 0.9460 Epoch 34/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1707 - accuracy: 0.9745 - val_loss: 0.2225 - val_accuracy: 0.9581 Epoch 35/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1640 - accuracy: 0.9755 - val_loss: 0.2136 - val_accuracy: 0.9614 Epoch 36/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1656 - accuracy: 0.9751 - val_loss: 0.2163 - val_accuracy: 0.9605 Epoch 37/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1674 - accuracy: 0.9746 - val_loss: 0.1981 - val_accuracy: 0.9660 Epoch 38/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1676 - accuracy: 0.9745 - val_loss: 0.2280 - val_accuracy: 0.9579 Epoch 39/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1648 - accuracy: 0.9750 - val_loss: 0.2151 - val_accuracy: 0.9612 Epoch 40/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1603 - accuracy: 0.9761 - val_loss: 0.2118 - val_accuracy: 0.9609 Epoch 41/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1634 - accuracy: 0.9753 - val_loss: 0.2301 - val_accuracy: 0.9584 Epoch 42/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1640 - accuracy: 0.9760 - val_loss: 0.2093 - val_accuracy: 0.9612 Epoch 43/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1615 - accuracy: 0.9759 - val_loss: 0.2037 - val_accuracy: 0.9631 Epoch 44/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1604 - accuracy: 0.9756 - val_loss: 0.2205 - val_accuracy: 0.9599 Epoch 45/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1606 - accuracy: 0.9755 - val_loss: 0.2291 - val_accuracy: 0.9556 Epoch 46/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1607 - accuracy: 0.9762 - val_loss: 0.2714 - val_accuracy: 0.9395 Epoch 47/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1610 - accuracy: 0.9760 - val_loss: 0.2034 - val_accuracy: 0.9630 Epoch 48/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1557 - accuracy: 0.9762 - val_loss: 0.2247 - val_accuracy: 0.9576 Epoch 49/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1573 - accuracy: 0.9765 - val_loss: 0.2052 - val_accuracy: 0.9614 Epoch 50/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1570 - accuracy: 0.9764 - val_loss: 0.2251 - val_accuracy: 0.9572 Epoch 51/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1596 - accuracy: 0.9752 - val_loss: 0.2193 - val_accuracy: 0.9584 Epoch 52/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1589 - accuracy: 0.9761 - val_loss: 0.2396 - val_accuracy: 0.9515 Epoch 53/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1562 - accuracy: 0.9763 - val_loss: 0.2198 - val_accuracy: 0.9586 Epoch 54/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1577 - accuracy: 0.9754 - val_loss: 0.2284 - val_accuracy: 0.9548 Epoch 55/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1568 - accuracy: 0.9763 - val_loss: 0.2207 - val_accuracy: 0.9562 Epoch 56/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1590 - accuracy: 0.9758 - val_loss: 0.2193 - val_accuracy: 0.9577 Epoch 57/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1559 - accuracy: 0.9760 - val_loss: 0.2135 - val_accuracy: 0.9591 Epoch 58/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1524 - accuracy: 0.9775 - val_loss: 0.2061 - val_accuracy: 0.9613 Epoch 59/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1560 - accuracy: 0.9762 - val_loss: 0.2209 - val_accuracy: 0.9570 Epoch 60/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1521 - accuracy: 0.9767 - val_loss: 0.2308 - val_accuracy: 0.9544 Epoch 61/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1553 - accuracy: 0.9765 - val_loss: 0.2538 - val_accuracy: 0.9447 Epoch 62/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1528 - accuracy: 0.9769 - val_loss: 0.2233 - val_accuracy: 0.9548 Epoch 63/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1543 - accuracy: 0.9765 - val_loss: 0.1979 - val_accuracy: 0.9643 Epoch 64/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1511 - accuracy: 0.9780 - val_loss: 0.2314 - val_accuracy: 0.9539 Epoch 65/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1544 - accuracy: 0.9758 - val_loss: 0.2437 - val_accuracy: 0.9513 Epoch 66/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1494 - accuracy: 0.9782 - val_loss: 0.2271 - val_accuracy: 0.9548 Epoch 67/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1545 - accuracy: 0.9763 - val_loss: 0.2289 - val_accuracy: 0.9523 Epoch 68/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1537 - accuracy: 0.9758 - val_loss: 0.2406 - val_accuracy: 0.9509 Epoch 69/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1536 - accuracy: 0.9772 - val_loss: 0.2283 - val_accuracy: 0.9545 Epoch 70/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1510 - accuracy: 0.9775 - val_loss: 0.1912 - val_accuracy: 0.9638 Epoch 71/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1496 - accuracy: 0.9774 - val_loss: 0.2245 - val_accuracy: 0.9530 Epoch 72/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1484 - accuracy: 0.9774 - val_loss: 0.2160 - val_accuracy: 0.9599 Epoch 73/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1512 - accuracy: 0.9773 - val_loss: 0.2093 - val_accuracy: 0.9571 Epoch 74/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1514 - accuracy: 0.9772 - val_loss: 0.2104 - val_accuracy: 0.9587 Epoch 75/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1516 - accuracy: 0.9772 - val_loss: 0.2338 - val_accuracy: 0.9515 Epoch 76/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1527 - accuracy: 0.9771 - val_loss: 0.2060 - val_accuracy: 0.9603 Epoch 77/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9782 - val_loss: 0.2436 - val_accuracy: 0.9474 Epoch 78/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1539 - accuracy: 0.9764 - val_loss: 0.2044 - val_accuracy: 0.9634 Epoch 79/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1493 - accuracy: 0.9781 - val_loss: 0.2612 - val_accuracy: 0.9450 Epoch 80/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1534 - accuracy: 0.9773 - val_loss: 0.2063 - val_accuracy: 0.9601 Epoch 81/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1482 - accuracy: 0.9783 - val_loss: 0.2267 - val_accuracy: 0.9546 Epoch 82/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1472 - accuracy: 0.9777 - val_loss: 0.2476 - val_accuracy: 0.9477 Epoch 83/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1496 - accuracy: 0.9778 - val_loss: 0.2131 - val_accuracy: 0.9576 Epoch 84/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1485 - accuracy: 0.9777 - val_loss: 0.2229 - val_accuracy: 0.9563 Epoch 85/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1496 - accuracy: 0.9776 - val_loss: 0.2129 - val_accuracy: 0.9592 Epoch 86/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1491 - accuracy: 0.9774 - val_loss: 0.2350 - val_accuracy: 0.9534 Epoch 87/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1487 - accuracy: 0.9776 - val_loss: 0.2401 - val_accuracy: 0.9518 Epoch 88/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1475 - accuracy: 0.9776 - val_loss: 0.2088 - val_accuracy: 0.9611 Epoch 89/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1503 - accuracy: 0.9771 - val_loss: 0.2321 - val_accuracy: 0.9535 Epoch 90/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1515 - accuracy: 0.9764 - val_loss: 0.2070 - val_accuracy: 0.9625 Epoch 91/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1464 - accuracy: 0.9784 - val_loss: 0.2114 - val_accuracy: 0.9612 Epoch 92/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1449 - accuracy: 0.9782 - val_loss: 0.2460 - val_accuracy: 0.9481 Epoch 93/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1501 - accuracy: 0.9774 - val_loss: 0.2111 - val_accuracy: 0.9611 Epoch 94/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1465 - accuracy: 0.9780 - val_loss: 0.2113 - val_accuracy: 0.9594 Epoch 95/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1436 - accuracy: 0.9790 - val_loss: 0.2027 - val_accuracy: 0.9618 Epoch 96/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1475 - accuracy: 0.9777 - val_loss: 0.2012 - val_accuracy: 0.9619 Epoch 97/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9774 - val_loss: 0.2003 - val_accuracy: 0.9627 Epoch 98/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1470 - accuracy: 0.9781 - val_loss: 0.2171 - val_accuracy: 0.9578 Epoch 99/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9783 - val_loss: 0.2237 - val_accuracy: 0.9573 Epoch 100/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1432 - accuracy: 0.9797 - val_loss: 0.2283 - val_accuracy: 0.9535 Epoch 101/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1477 - accuracy: 0.9774 - val_loss: 0.2588 - val_accuracy: 0.9430 Epoch 102/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1467 - accuracy: 0.9775 - val_loss: 0.2297 - val_accuracy: 0.9544 Epoch 103/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1483 - accuracy: 0.9773 - val_loss: 0.2353 - val_accuracy: 0.9520 Epoch 104/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1488 - accuracy: 0.9771 - val_loss: 0.2555 - val_accuracy: 0.9479 Epoch 105/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1479 - accuracy: 0.9776 - val_loss: 0.2328 - val_accuracy: 0.9524 Epoch 106/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1466 - accuracy: 0.9778 - val_loss: 0.2349 - val_accuracy: 0.9520 Epoch 107/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1480 - accuracy: 0.9771 - val_loss: 0.2884 - val_accuracy: 0.9362 Epoch 108/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1492 - accuracy: 0.9772 - val_loss: 0.2068 - val_accuracy: 0.9602 Epoch 109/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1439 - accuracy: 0.9793 - val_loss: 0.2576 - val_accuracy: 0.9432 Epoch 110/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1464 - accuracy: 0.9784 - val_loss: 0.2048 - val_accuracy: 0.9605 Epoch 111/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1446 - accuracy: 0.9781 - val_loss: 0.2071 - val_accuracy: 0.9596 Epoch 112/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1409 - accuracy: 0.9786 - val_loss: 0.2263 - val_accuracy: 0.9543 Epoch 113/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1495 - accuracy: 0.9776 - val_loss: 0.2206 - val_accuracy: 0.9576 Epoch 114/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1433 - accuracy: 0.9786 - val_loss: 0.2057 - val_accuracy: 0.9619 Epoch 115/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1435 - accuracy: 0.9787 - val_loss: 0.2237 - val_accuracy: 0.9534 Epoch 116/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9792 - val_loss: 0.2151 - val_accuracy: 0.9589 Epoch 117/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1448 - accuracy: 0.9781 - val_loss: 0.2060 - val_accuracy: 0.9606 Epoch 118/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1465 - accuracy: 0.9784 - val_loss: 0.2463 - val_accuracy: 0.9502 Epoch 119/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1454 - accuracy: 0.9780 - val_loss: 0.2300 - val_accuracy: 0.9519 Epoch 120/200 235/235 [==============================] - 3s 12ms/step - loss: 0.1462 - accuracy: 0.9782 - val_loss: 0.2011 - val_accuracy: 0.9648 Epoch 121/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1434 - accuracy: 0.9791 - val_loss: 0.2214 - val_accuracy: 0.9579 Epoch 122/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9785 - val_loss: 0.1909 - val_accuracy: 0.9653 Epoch 123/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1469 - accuracy: 0.9778 - val_loss: 0.2274 - val_accuracy: 0.9535 Epoch 124/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1426 - accuracy: 0.9799 - val_loss: 0.2514 - val_accuracy: 0.9486 Epoch 125/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1444 - accuracy: 0.9781 - val_loss: 0.2224 - val_accuracy: 0.9575 Epoch 126/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9797 - val_loss: 0.2179 - val_accuracy: 0.9591 Epoch 127/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9790 - val_loss: 0.2824 - val_accuracy: 0.9378 Epoch 128/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1469 - accuracy: 0.9785 - val_loss: 0.2513 - val_accuracy: 0.9445 Epoch 129/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1443 - accuracy: 0.9784 - val_loss: 0.2546 - val_accuracy: 0.9450 Epoch 130/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2515 - val_accuracy: 0.9478 Epoch 131/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1437 - accuracy: 0.9792 - val_loss: 0.2057 - val_accuracy: 0.9614 Epoch 132/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1408 - accuracy: 0.9795 - val_loss: 0.1892 - val_accuracy: 0.9660 Epoch 133/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1438 - accuracy: 0.9783 - val_loss: 0.2172 - val_accuracy: 0.9589 Epoch 134/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1486 - accuracy: 0.9773 - val_loss: 0.1984 - val_accuracy: 0.9615 Epoch 135/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9789 - val_loss: 0.2004 - val_accuracy: 0.9638 Epoch 136/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1457 - accuracy: 0.9780 - val_loss: 0.2167 - val_accuracy: 0.9560 Epoch 137/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1420 - accuracy: 0.9787 - val_loss: 0.2740 - val_accuracy: 0.9400 Epoch 138/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9790 - val_loss: 0.2354 - val_accuracy: 0.9523 Epoch 139/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1423 - accuracy: 0.9786 - val_loss: 0.2705 - val_accuracy: 0.9420 Epoch 140/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1427 - accuracy: 0.9789 - val_loss: 0.2017 - val_accuracy: 0.9639 Epoch 141/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1399 - accuracy: 0.9794 - val_loss: 0.1998 - val_accuracy: 0.9616 Epoch 142/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1435 - accuracy: 0.9782 - val_loss: 0.2286 - val_accuracy: 0.9535 Epoch 143/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1445 - accuracy: 0.9778 - val_loss: 0.2072 - val_accuracy: 0.9616 Epoch 144/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1419 - accuracy: 0.9788 - val_loss: 0.1928 - val_accuracy: 0.9642 Epoch 145/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1420 - accuracy: 0.9789 - val_loss: 0.2126 - val_accuracy: 0.9592 Epoch 146/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1423 - accuracy: 0.9789 - val_loss: 0.2234 - val_accuracy: 0.9574 Epoch 147/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1418 - accuracy: 0.9790 - val_loss: 0.2193 - val_accuracy: 0.9566 Epoch 148/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1432 - accuracy: 0.9784 - val_loss: 0.2026 - val_accuracy: 0.9630 Epoch 149/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1412 - accuracy: 0.9792 - val_loss: 0.2450 - val_accuracy: 0.9504 Epoch 150/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2221 - val_accuracy: 0.9550 Epoch 151/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9790 - val_loss: 0.2343 - val_accuracy: 0.9529 Epoch 152/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9789 - val_loss: 0.2156 - val_accuracy: 0.9556 Epoch 153/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1409 - accuracy: 0.9790 - val_loss: 0.2119 - val_accuracy: 0.9576 Epoch 154/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1425 - accuracy: 0.9782 - val_loss: 0.2032 - val_accuracy: 0.9611 Epoch 155/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1403 - accuracy: 0.9791 - val_loss: 0.2076 - val_accuracy: 0.9598 Epoch 156/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1397 - accuracy: 0.9797 - val_loss: 0.2013 - val_accuracy: 0.9624 Epoch 157/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1407 - accuracy: 0.9790 - val_loss: 0.2254 - val_accuracy: 0.9545 Epoch 158/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9784 - val_loss: 0.2260 - val_accuracy: 0.9551 Epoch 159/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9790 - val_loss: 0.1849 - val_accuracy: 0.9671 Epoch 160/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1436 - accuracy: 0.9782 - val_loss: 0.2214 - val_accuracy: 0.9581 Epoch 161/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1474 - accuracy: 0.9781 - val_loss: 0.2178 - val_accuracy: 0.9583 Epoch 162/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1389 - accuracy: 0.9791 - val_loss: 0.2128 - val_accuracy: 0.9574 Epoch 163/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9786 - val_loss: 0.1974 - val_accuracy: 0.9616 Epoch 164/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1388 - accuracy: 0.9793 - val_loss: 0.2091 - val_accuracy: 0.9589 Epoch 165/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9780 - val_loss: 0.2026 - val_accuracy: 0.9633 Epoch 166/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1406 - accuracy: 0.9783 - val_loss: 0.1892 - val_accuracy: 0.9644 Epoch 167/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1387 - accuracy: 0.9790 - val_loss: 0.2008 - val_accuracy: 0.9601 Epoch 168/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1421 - accuracy: 0.9786 - val_loss: 0.2483 - val_accuracy: 0.9493 Epoch 169/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1424 - accuracy: 0.9790 - val_loss: 0.2105 - val_accuracy: 0.9591 Epoch 170/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9797 - val_loss: 0.2180 - val_accuracy: 0.9575 Epoch 171/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1401 - accuracy: 0.9790 - val_loss: 0.2305 - val_accuracy: 0.9543 Epoch 172/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1398 - accuracy: 0.9783 - val_loss: 0.2348 - val_accuracy: 0.9571 Epoch 173/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9791 - val_loss: 0.2252 - val_accuracy: 0.9518 Epoch 174/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9787 - val_loss: 0.2253 - val_accuracy: 0.9548 Epoch 175/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1415 - accuracy: 0.9790 - val_loss: 0.2382 - val_accuracy: 0.9509 Epoch 176/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1421 - accuracy: 0.9789 - val_loss: 0.2108 - val_accuracy: 0.9605 Epoch 177/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1377 - accuracy: 0.9797 - val_loss: 0.2197 - val_accuracy: 0.9570 Epoch 178/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1377 - accuracy: 0.9796 - val_loss: 0.2128 - val_accuracy: 0.9588 Epoch 179/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9793 - val_loss: 0.2187 - val_accuracy: 0.9575 Epoch 180/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9787 - val_loss: 0.2230 - val_accuracy: 0.9559 Epoch 181/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1422 - accuracy: 0.9783 - val_loss: 0.1977 - val_accuracy: 0.9626 Epoch 182/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1430 - accuracy: 0.9786 - val_loss: 0.1993 - val_accuracy: 0.9618 Epoch 183/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9798 - val_loss: 0.2013 - val_accuracy: 0.9615 Epoch 184/200 235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9794 - val_loss: 0.2141 - val_accuracy: 0.9599 Epoch 185/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9788 - val_loss: 0.2185 - val_accuracy: 0.9582 Epoch 186/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1372 - accuracy: 0.9801 - val_loss: 0.2047 - val_accuracy: 0.9593 Epoch 187/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1393 - accuracy: 0.9789 - val_loss: 0.1874 - val_accuracy: 0.9672 Epoch 188/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1392 - accuracy: 0.9786 - val_loss: 0.2088 - val_accuracy: 0.9597 Epoch 189/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1427 - accuracy: 0.9787 - val_loss: 0.2622 - val_accuracy: 0.9447 Epoch 190/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1378 - accuracy: 0.9803 - val_loss: 0.2446 - val_accuracy: 0.9496 Epoch 191/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1457 - accuracy: 0.9776 - val_loss: 0.2227 - val_accuracy: 0.9574 Epoch 192/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1384 - accuracy: 0.9802 - val_loss: 0.2430 - val_accuracy: 0.9524 Epoch 193/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9789 - val_loss: 0.2285 - val_accuracy: 0.9548 Epoch 194/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1379 - accuracy: 0.9796 - val_loss: 0.2298 - val_accuracy: 0.9522 Epoch 195/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9797 - val_loss: 0.2863 - val_accuracy: 0.9400 Epoch 196/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1411 - accuracy: 0.9790 - val_loss: 0.2052 - val_accuracy: 0.9617 Epoch 197/200 235/235 [==============================] - 3s 14ms/step - loss: 0.1395 - accuracy: 0.9793 - val_loss: 0.2184 - val_accuracy: 0.9583 Epoch 198/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1416 - accuracy: 0.9790 - val_loss: 0.2374 - val_accuracy: 0.9501 Epoch 199/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1409 - accuracy: 0.9783 - val_loss: 0.2038 - val_accuracy: 0.9607 Epoch 200/200 235/235 [==============================] - 3s 13ms/step - loss: 0.1344 - accuracy: 0.9801 - val_loss: 0.2279 - val_accuracy: 0.9540 Epoch 1/200 235/235 [==============================] - 4s 13ms/step - loss: 0.2495 - accuracy: 0.9262 - val_loss: 0.2079 - val_accuracy: 0.9567 Epoch 2/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0868 - accuracy: 0.9753 - val_loss: 0.1044 - val_accuracy: 0.9674 Epoch 3/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0499 - accuracy: 0.9868 - val_loss: 0.0916 - val_accuracy: 0.9708 Epoch 4/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0306 - accuracy: 0.9921 - val_loss: 0.0944 - val_accuracy: 0.9699 Epoch 5/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0193 - accuracy: 0.9952 - val_loss: 0.1018 - val_accuracy: 0.9706 Epoch 6/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0140 - accuracy: 0.9967 - val_loss: 0.0871 - val_accuracy: 0.9748 Epoch 7/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0116 - accuracy: 0.9970 - val_loss: 0.0915 - val_accuracy: 0.9741 Epoch 8/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9973 - val_loss: 0.0917 - val_accuracy: 0.9750 Epoch 9/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0077 - accuracy: 0.9981 - val_loss: 0.0875 - val_accuracy: 0.9772 Epoch 10/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.0893 - val_accuracy: 0.9770 Epoch 11/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 0.1168 - val_accuracy: 0.9712 Epoch 12/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0114 - accuracy: 0.9963 - val_loss: 0.1047 - val_accuracy: 0.9737 Epoch 13/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0116 - accuracy: 0.9961 - val_loss: 0.0844 - val_accuracy: 0.9793 Epoch 14/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.0724 - val_accuracy: 0.9814 Epoch 15/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.0749 - val_accuracy: 0.9819 Epoch 16/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0735 - val_accuracy: 0.9839 Epoch 17/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.0803 - val_accuracy: 0.9808 Epoch 18/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.1033 - val_accuracy: 0.9764 Epoch 19/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0088 - accuracy: 0.9971 - val_loss: 0.1169 - val_accuracy: 0.9723 Epoch 20/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0137 - accuracy: 0.9950 - val_loss: 0.0989 - val_accuracy: 0.9776 Epoch 21/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0085 - accuracy: 0.9970 - val_loss: 0.0910 - val_accuracy: 0.9789 Epoch 22/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 0.0794 - val_accuracy: 0.9818 Epoch 23/200 235/235 [==============================] - 3s 14ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0701 - val_accuracy: 0.9825 Epoch 24/200 235/235 [==============================] - 3s 13ms/step - loss: 8.4521e-04 - accuracy: 0.9999 - val_loss: 0.0682 - val_accuracy: 0.9855 Epoch 25/200 235/235 [==============================] - 3s 13ms/step - loss: 8.4762e-04 - accuracy: 0.9998 - val_loss: 0.0719 - val_accuracy: 0.9837 Epoch 26/200 235/235 [==============================] - 3s 13ms/step - loss: 8.8288e-04 - accuracy: 0.9999 - val_loss: 0.0707 - val_accuracy: 0.9844 Epoch 27/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1002 - val_accuracy: 0.9779 Epoch 28/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0088 - accuracy: 0.9969 - val_loss: 0.1079 - val_accuracy: 0.9759 Epoch 29/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0132 - accuracy: 0.9954 - val_loss: 0.0908 - val_accuracy: 0.9802 Epoch 30/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.0784 - val_accuracy: 0.9824 Epoch 31/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0776 - val_accuracy: 0.9839 Epoch 32/200 235/235 [==============================] - 3s 13ms/step - loss: 9.7138e-04 - accuracy: 0.9998 - val_loss: 0.0833 - val_accuracy: 0.9819 Epoch 33/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.0676 - val_accuracy: 0.9845 Epoch 34/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0824 - val_accuracy: 0.9811 Epoch 35/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0812 - val_accuracy: 0.9830 Epoch 36/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.1075 - val_accuracy: 0.9761 Epoch 37/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0084 - accuracy: 0.9972 - val_loss: 0.1024 - val_accuracy: 0.9793 Epoch 38/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0058 - accuracy: 0.9982 - val_loss: 0.0927 - val_accuracy: 0.9792 Epoch 39/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.0835 - val_accuracy: 0.9817 Epoch 40/200 235/235 [==============================] - 3s 13ms/step - loss: 7.7238e-04 - accuracy: 0.9998 - val_loss: 0.0767 - val_accuracy: 0.9845 Epoch 41/200 235/235 [==============================] - 3s 13ms/step - loss: 2.9476e-04 - accuracy: 0.9999 - val_loss: 0.0728 - val_accuracy: 0.9853 Epoch 42/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1323e-04 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9856 Epoch 43/200 235/235 [==============================] - 3s 13ms/step - loss: 8.4404e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9857 Epoch 44/200 235/235 [==============================] - 3s 13ms/step - loss: 6.8946e-05 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9854 Epoch 45/200 235/235 [==============================] - 3s 13ms/step - loss: 5.6046e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9856 Epoch 46/200 235/235 [==============================] - 3s 13ms/step - loss: 6.0134e-05 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9851 Epoch 47/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.2116 - val_accuracy: 0.9594 Epoch 48/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0183 - accuracy: 0.9940 - val_loss: 0.1024 - val_accuracy: 0.9795 Epoch 49/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0058 - accuracy: 0.9979 - val_loss: 0.0822 - val_accuracy: 0.9820 Epoch 50/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0729 - val_accuracy: 0.9837 Epoch 51/200 235/235 [==============================] - 3s 13ms/step - loss: 3.9099e-04 - accuracy: 1.0000 - val_loss: 0.0691 - val_accuracy: 0.9841 Epoch 52/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4446e-04 - accuracy: 1.0000 - val_loss: 0.0684 - val_accuracy: 0.9849 Epoch 53/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1316e-04 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 0.9848 Epoch 54/200 235/235 [==============================] - 3s 13ms/step - loss: 9.1030e-05 - accuracy: 1.0000 - val_loss: 0.0692 - val_accuracy: 0.9849 Epoch 55/200 235/235 [==============================] - 3s 13ms/step - loss: 7.4178e-05 - accuracy: 1.0000 - val_loss: 0.0690 - val_accuracy: 0.9852 Epoch 56/200 235/235 [==============================] - 3s 13ms/step - loss: 5.9493e-05 - accuracy: 1.0000 - val_loss: 0.0695 - val_accuracy: 0.9854 Epoch 57/200 235/235 [==============================] - 3s 13ms/step - loss: 5.0939e-05 - accuracy: 1.0000 - val_loss: 0.0695 - val_accuracy: 0.9855 Epoch 58/200 235/235 [==============================] - 3s 13ms/step - loss: 5.2808e-05 - accuracy: 1.0000 - val_loss: 0.0697 - val_accuracy: 0.9856 Epoch 59/200 235/235 [==============================] - 3s 13ms/step - loss: 4.2974e-05 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9856 Epoch 60/200 235/235 [==============================] - 3s 13ms/step - loss: 3.6341e-05 - accuracy: 1.0000 - val_loss: 0.0699 - val_accuracy: 0.9856 Epoch 61/200 235/235 [==============================] - 3s 13ms/step - loss: 3.3374e-05 - accuracy: 1.0000 - val_loss: 0.0702 - val_accuracy: 0.9856 Epoch 62/200 235/235 [==============================] - 3s 13ms/step - loss: 2.9721e-05 - accuracy: 1.0000 - val_loss: 0.0703 - val_accuracy: 0.9857 Epoch 63/200 235/235 [==============================] - 3s 13ms/step - loss: 2.4953e-05 - accuracy: 1.0000 - val_loss: 0.0705 - val_accuracy: 0.9854 Epoch 64/200 235/235 [==============================] - 3s 13ms/step - loss: 2.3776e-05 - accuracy: 1.0000 - val_loss: 0.0704 - val_accuracy: 0.9858 Epoch 65/200 235/235 [==============================] - 3s 13ms/step - loss: 2.1326e-05 - accuracy: 1.0000 - val_loss: 0.0711 - val_accuracy: 0.9859 Epoch 66/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0112 - accuracy: 0.9966 - val_loss: 0.2397 - val_accuracy: 0.9558 Epoch 67/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0231 - accuracy: 0.9927 - val_loss: 0.0844 - val_accuracy: 0.9819 Epoch 68/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0783 - val_accuracy: 0.9820 Epoch 69/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0788 - val_accuracy: 0.9839 Epoch 70/200 235/235 [==============================] - 3s 13ms/step - loss: 5.1554e-04 - accuracy: 0.9999 - val_loss: 0.0765 - val_accuracy: 0.9840 Epoch 71/200 235/235 [==============================] - 3s 13ms/step - loss: 4.1956e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9845 Epoch 72/200 235/235 [==============================] - 3s 13ms/step - loss: 1.8338e-04 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9842 Epoch 73/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4750e-04 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9848 Epoch 74/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2276e-04 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9842 Epoch 75/200 235/235 [==============================] - 3s 13ms/step - loss: 9.5179e-05 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9847 Epoch 76/200 235/235 [==============================] - 3s 13ms/step - loss: 7.6908e-05 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9851 Epoch 77/200 235/235 [==============================] - 3s 13ms/step - loss: 6.8500e-05 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 0.9849 Epoch 78/200 235/235 [==============================] - 3s 13ms/step - loss: 5.9088e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9853 Epoch 79/200 235/235 [==============================] - 3s 13ms/step - loss: 4.9866e-05 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9851 Epoch 80/200 235/235 [==============================] - 3s 13ms/step - loss: 4.3015e-05 - accuracy: 1.0000 - val_loss: 0.0744 - val_accuracy: 0.9852 Epoch 81/200 235/235 [==============================] - 3s 13ms/step - loss: 3.7795e-05 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 0.9855 Epoch 82/200 235/235 [==============================] - 3s 13ms/step - loss: 3.6054e-05 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9851 Epoch 83/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1763 - val_accuracy: 0.9680 Epoch 84/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0207 - accuracy: 0.9933 - val_loss: 0.1008 - val_accuracy: 0.9811 Epoch 85/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0046 - accuracy: 0.9985 - val_loss: 0.0779 - val_accuracy: 0.9835 Epoch 86/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.0764 - val_accuracy: 0.9847 Epoch 87/200 235/235 [==============================] - 3s 13ms/step - loss: 8.4310e-04 - accuracy: 0.9998 - val_loss: 0.0794 - val_accuracy: 0.9847 Epoch 88/200 235/235 [==============================] - 3s 13ms/step - loss: 2.7549e-04 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9850 Epoch 89/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4048e-04 - accuracy: 1.0000 - val_loss: 0.0781 - val_accuracy: 0.9854 Epoch 90/200 235/235 [==============================] - 3s 13ms/step - loss: 9.5776e-05 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 0.9856 Epoch 91/200 235/235 [==============================] - 3s 13ms/step - loss: 9.1525e-05 - accuracy: 1.0000 - val_loss: 0.0791 - val_accuracy: 0.9857 Epoch 92/200 235/235 [==============================] - 3s 13ms/step - loss: 7.5744e-05 - accuracy: 1.0000 - val_loss: 0.0784 - val_accuracy: 0.9854 Epoch 93/200 235/235 [==============================] - 3s 13ms/step - loss: 9.7711e-05 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 0.9856 Epoch 94/200 235/235 [==============================] - 3s 13ms/step - loss: 6.1816e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9857 Epoch 95/200 235/235 [==============================] - 3s 13ms/step - loss: 5.2522e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9857 Epoch 96/200 235/235 [==============================] - 3s 13ms/step - loss: 4.1268e-05 - accuracy: 1.0000 - val_loss: 0.0806 - val_accuracy: 0.9858 Epoch 97/200 235/235 [==============================] - 3s 13ms/step - loss: 4.0060e-05 - accuracy: 1.0000 - val_loss: 0.0807 - val_accuracy: 0.9855 Epoch 98/200 235/235 [==============================] - 3s 13ms/step - loss: 3.5270e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9854 Epoch 99/200 235/235 [==============================] - 3s 13ms/step - loss: 3.2687e-05 - accuracy: 1.0000 - val_loss: 0.0826 - val_accuracy: 0.9856 Epoch 100/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0105 - accuracy: 0.9970 - val_loss: 0.1348 - val_accuracy: 0.9776 Epoch 101/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0099 - accuracy: 0.9967 - val_loss: 0.0933 - val_accuracy: 0.9818 Epoch 102/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.0836 - val_accuracy: 0.9852 Epoch 103/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0844 - val_accuracy: 0.9841 Epoch 104/200 235/235 [==============================] - 3s 13ms/step - loss: 4.8876e-04 - accuracy: 0.9999 - val_loss: 0.0888 - val_accuracy: 0.9844 Epoch 105/200 235/235 [==============================] - 3s 13ms/step - loss: 2.4737e-04 - accuracy: 0.9999 - val_loss: 0.0850 - val_accuracy: 0.9851 Epoch 106/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1254e-04 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 0.9847 Epoch 107/200 235/235 [==============================] - 3s 13ms/step - loss: 8.2048e-05 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9854 Epoch 108/200 235/235 [==============================] - 3s 13ms/step - loss: 9.6765e-05 - accuracy: 1.0000 - val_loss: 0.0851 - val_accuracy: 0.9854 Epoch 109/200 235/235 [==============================] - 3s 13ms/step - loss: 6.9901e-05 - accuracy: 1.0000 - val_loss: 0.0841 - val_accuracy: 0.9852 Epoch 110/200 235/235 [==============================] - 3s 13ms/step - loss: 2.3757e-04 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9832 Epoch 111/200 235/235 [==============================] - 3s 13ms/step - loss: 3.8595e-04 - accuracy: 0.9999 - val_loss: 0.0869 - val_accuracy: 0.9851 Epoch 112/200 235/235 [==============================] - 3s 13ms/step - loss: 7.6914e-04 - accuracy: 0.9998 - val_loss: 0.1115 - val_accuracy: 0.9791 Epoch 113/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 0.1308 - val_accuracy: 0.9792 Epoch 114/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.1129 - val_accuracy: 0.9784 Epoch 115/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9993 - val_loss: 0.0981 - val_accuracy: 0.9836 Epoch 116/200 235/235 [==============================] - 3s 13ms/step - loss: 4.8056e-04 - accuracy: 0.9999 - val_loss: 0.0938 - val_accuracy: 0.9835 Epoch 117/200 235/235 [==============================] - 3s 13ms/step - loss: 4.0246e-04 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9847 Epoch 118/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0179e-04 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9842 Epoch 119/200 235/235 [==============================] - 3s 13ms/step - loss: 6.1090e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9846 Epoch 120/200 235/235 [==============================] - 3s 13ms/step - loss: 4.8848e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9849 Epoch 121/200 235/235 [==============================] - 3s 13ms/step - loss: 4.5009e-05 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9850 Epoch 122/200 235/235 [==============================] - 3s 13ms/step - loss: 3.6391e-05 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9851 Epoch 123/200 235/235 [==============================] - 3s 13ms/step - loss: 6.0180e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9845 Epoch 124/200 235/235 [==============================] - 3s 13ms/step - loss: 1.7638e-04 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9846 Epoch 125/200 235/235 [==============================] - 3s 14ms/step - loss: 4.3902e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9849 Epoch 126/200 235/235 [==============================] - 3s 13ms/step - loss: 4.7388e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9849 Epoch 127/200 235/235 [==============================] - 3s 13ms/step - loss: 4.2317e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9843 Epoch 128/200 235/235 [==============================] - 3s 13ms/step - loss: 4.0008e-05 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9844 Epoch 129/200 235/235 [==============================] - 3s 13ms/step - loss: 4.5626e-05 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9848 Epoch 130/200 235/235 [==============================] - 3s 13ms/step - loss: 2.7936e-05 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9850 Epoch 131/200 235/235 [==============================] - 3s 13ms/step - loss: 1.8518e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9852 Epoch 132/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4753e-05 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9848 Epoch 133/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2536e-05 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9852 Epoch 134/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4484e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9850 Epoch 135/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0062 - accuracy: 0.9985 - val_loss: 0.2044 - val_accuracy: 0.9660 Epoch 136/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0137 - accuracy: 0.9954 - val_loss: 0.1035 - val_accuracy: 0.9813 Epoch 137/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0033 - accuracy: 0.9989 - val_loss: 0.0918 - val_accuracy: 0.9845 Epoch 138/200 235/235 [==============================] - 3s 13ms/step - loss: 6.6264e-04 - accuracy: 0.9998 - val_loss: 0.0902 - val_accuracy: 0.9848 Epoch 139/200 235/235 [==============================] - 3s 13ms/step - loss: 1.9397e-04 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 0.9847 Epoch 140/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0510e-04 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9847 Epoch 141/200 235/235 [==============================] - 3s 13ms/step - loss: 9.9239e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9851 Epoch 142/200 235/235 [==============================] - 3s 13ms/step - loss: 6.7293e-05 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9855 Epoch 143/200 235/235 [==============================] - 3s 13ms/step - loss: 5.5097e-05 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9858 Epoch 144/200 235/235 [==============================] - 3s 13ms/step - loss: 4.6285e-05 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 0.9856 Epoch 145/200 235/235 [==============================] - 3s 13ms/step - loss: 3.6648e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9856 Epoch 146/200 235/235 [==============================] - 3s 13ms/step - loss: 3.5971e-05 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9856 Epoch 147/200 235/235 [==============================] - 3s 13ms/step - loss: 1.5527e-04 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9847 Epoch 148/200 235/235 [==============================] - 3s 13ms/step - loss: 4.9190e-04 - accuracy: 0.9999 - val_loss: 0.1057 - val_accuracy: 0.9816 Epoch 149/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1167 - val_accuracy: 0.9806 Epoch 150/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1388 - val_accuracy: 0.9766 Epoch 151/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.1012 - val_accuracy: 0.9837 Epoch 152/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1001 - val_accuracy: 0.9839 Epoch 153/200 235/235 [==============================] - 3s 13ms/step - loss: 9.2819e-04 - accuracy: 0.9998 - val_loss: 0.0958 - val_accuracy: 0.9842 Epoch 154/200 235/235 [==============================] - 3s 13ms/step - loss: 4.6041e-04 - accuracy: 0.9998 - val_loss: 0.0938 - val_accuracy: 0.9849 Epoch 155/200 235/235 [==============================] - 3s 13ms/step - loss: 1.7391e-04 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9844 Epoch 156/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1294e-04 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9846 Epoch 157/200 235/235 [==============================] - 3s 13ms/step - loss: 4.4710e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9844 Epoch 158/200 235/235 [==============================] - 3s 14ms/step - loss: 3.8477e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9845 Epoch 159/200 235/235 [==============================] - 3s 14ms/step - loss: 2.5454e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9847 Epoch 160/200 235/235 [==============================] - 3s 13ms/step - loss: 5.5570e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9848 Epoch 161/200 235/235 [==============================] - 3s 13ms/step - loss: 2.6717e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9848 Epoch 162/200 235/235 [==============================] - 3s 13ms/step - loss: 1.6721e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9849 Epoch 163/200 235/235 [==============================] - 3s 13ms/step - loss: 1.5726e-05 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9851 Epoch 164/200 235/235 [==============================] - 3s 13ms/step - loss: 1.4019e-05 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9851 Epoch 165/200 235/235 [==============================] - 3s 13ms/step - loss: 1.2199e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9852 Epoch 166/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0879e-05 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9847 Epoch 167/200 235/235 [==============================] - 3s 13ms/step - loss: 9.7453e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9849 Epoch 168/200 235/235 [==============================] - 3s 13ms/step - loss: 9.1384e-06 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9852 Epoch 169/200 235/235 [==============================] - 3s 13ms/step - loss: 8.5547e-06 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9852 Epoch 170/200 235/235 [==============================] - 3s 13ms/step - loss: 9.2934e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9854 Epoch 171/200 235/235 [==============================] - 3s 13ms/step - loss: 1.0096e-05 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9855 Epoch 172/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0074 - accuracy: 0.9979 - val_loss: 0.1340 - val_accuracy: 0.9779 Epoch 173/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0069 - accuracy: 0.9978 - val_loss: 0.0964 - val_accuracy: 0.9823 Epoch 174/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0851 - val_accuracy: 0.9840 Epoch 175/200 235/235 [==============================] - 3s 13ms/step - loss: 3.5873e-04 - accuracy: 0.9999 - val_loss: 0.0860 - val_accuracy: 0.9846 Epoch 176/200 235/235 [==============================] - 3s 11ms/step - loss: 1.9366e-04 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9855 Epoch 177/200 235/235 [==============================] - 3s 13ms/step - loss: 1.7167e-04 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9841 Epoch 178/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1794e-04 - accuracy: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9849 Epoch 179/200 235/235 [==============================] - 3s 13ms/step - loss: 5.8223e-05 - accuracy: 1.0000 - val_loss: 0.0892 - val_accuracy: 0.9850 Epoch 180/200 235/235 [==============================] - 3s 13ms/step - loss: 3.9376e-05 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9855 Epoch 181/200 235/235 [==============================] - 3s 13ms/step - loss: 3.0428e-05 - accuracy: 1.0000 - val_loss: 0.0893 - val_accuracy: 0.9853 Epoch 182/200 235/235 [==============================] - 3s 13ms/step - loss: 1.1627e-04 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9850 Epoch 183/200 235/235 [==============================] - 3s 13ms/step - loss: 5.1923e-04 - accuracy: 0.9998 - val_loss: 0.0861 - val_accuracy: 0.9852 Epoch 184/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0956 - val_accuracy: 0.9834 Epoch 185/200 235/235 [==============================] - 3s 13ms/step - loss: 4.8009e-04 - accuracy: 0.9998 - val_loss: 0.1044 - val_accuracy: 0.9825 Epoch 186/200 235/235 [==============================] - 3s 13ms/step - loss: 3.4035e-04 - accuracy: 0.9999 - val_loss: 0.0965 - val_accuracy: 0.9843 Epoch 187/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.1210 - val_accuracy: 0.9818 Epoch 188/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1170 - val_accuracy: 0.9803 Epoch 189/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.1071 - val_accuracy: 0.9811 Epoch 190/200 235/235 [==============================] - 3s 11ms/step - loss: 8.0930e-04 - accuracy: 0.9998 - val_loss: 0.0968 - val_accuracy: 0.9842 Epoch 191/200 235/235 [==============================] - 3s 13ms/step - loss: 5.8281e-04 - accuracy: 0.9998 - val_loss: 0.0972 - val_accuracy: 0.9835 Epoch 192/200 235/235 [==============================] - 3s 15ms/step - loss: 1.0011e-04 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9852 Epoch 193/200 235/235 [==============================] - 3s 14ms/step - loss: 3.7858e-05 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 0.9854 Epoch 194/200 235/235 [==============================] - 3s 13ms/step - loss: 3.2141e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9853 Epoch 195/200 235/235 [==============================] - 3s 13ms/step - loss: 2.1073e-05 - accuracy: 1.0000 - val_loss: 0.0936 - val_accuracy: 0.9855 Epoch 196/200 235/235 [==============================] - 3s 13ms/step - loss: 4.3826e-04 - accuracy: 0.9999 - val_loss: 0.1007 - val_accuracy: 0.9839 Epoch 197/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1066 - val_accuracy: 0.9827 Epoch 198/200 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 0.1102 - val_accuracy: 0.9831 Epoch 199/200 235/235 [==============================] - 3s 13ms/step - loss: 8.2480e-04 - accuracy: 0.9997 - val_loss: 0.1104 - val_accuracy: 0.9845 Epoch 200/200 235/235 [==============================] - 3s 13ms/step - loss: 1.3436e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9841 Epoch 1/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5677 - accuracy: 0.8573 - val_loss: 0.9285 - val_accuracy: 0.9024 Epoch 2/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8756 - accuracy: 0.8970 - val_loss: 0.8291 - val_accuracy: 0.9011 Epoch 3/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8345 - accuracy: 0.8976 - val_loss: 0.8145 - val_accuracy: 0.8990 Epoch 4/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8238 - accuracy: 0.8982 - val_loss: 0.8064 - val_accuracy: 0.8990 Epoch 5/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8180 - accuracy: 0.8984 - val_loss: 0.8017 - val_accuracy: 0.8995 Epoch 6/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8151 - accuracy: 0.8983 - val_loss: 0.8003 - val_accuracy: 0.8990 Epoch 7/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8128 - accuracy: 0.8987 - val_loss: 0.7978 - val_accuracy: 0.9000 Epoch 8/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8111 - accuracy: 0.8988 - val_loss: 0.7963 - val_accuracy: 0.8999 Epoch 9/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8104 - accuracy: 0.8988 - val_loss: 0.7949 - val_accuracy: 0.9008 Epoch 10/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8095 - accuracy: 0.8989 - val_loss: 0.7950 - val_accuracy: 0.9003 Epoch 11/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8087 - accuracy: 0.8993 - val_loss: 0.7939 - val_accuracy: 0.9007 Epoch 12/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8083 - accuracy: 0.8993 - val_loss: 0.7942 - val_accuracy: 0.9006 Epoch 13/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8079 - accuracy: 0.8995 - val_loss: 0.7938 - val_accuracy: 0.8999 Epoch 14/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8077 - accuracy: 0.8990 - val_loss: 0.7937 - val_accuracy: 0.9011 Epoch 15/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8070 - accuracy: 0.9000 - val_loss: 0.7927 - val_accuracy: 0.9012 Epoch 16/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8071 - accuracy: 0.8997 - val_loss: 0.7920 - val_accuracy: 0.9016 Epoch 17/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8067 - accuracy: 0.8994 - val_loss: 0.7919 - val_accuracy: 0.9018 Epoch 18/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8062 - accuracy: 0.9001 - val_loss: 0.7919 - val_accuracy: 0.9016 Epoch 19/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8061 - accuracy: 0.8998 - val_loss: 0.7915 - val_accuracy: 0.9018 Epoch 20/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.8999 - val_loss: 0.7924 - val_accuracy: 0.9008 Epoch 21/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8059 - accuracy: 0.9001 - val_loss: 0.7909 - val_accuracy: 0.9013 Epoch 22/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8057 - accuracy: 0.9000 - val_loss: 0.7912 - val_accuracy: 0.9017 Epoch 23/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8055 - accuracy: 0.8997 - val_loss: 0.7907 - val_accuracy: 0.9016 Epoch 24/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8056 - accuracy: 0.9002 - val_loss: 0.7910 - val_accuracy: 0.9022 Epoch 25/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8052 - accuracy: 0.9002 - val_loss: 0.7903 - val_accuracy: 0.9023 Epoch 26/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9001 - val_loss: 0.7906 - val_accuracy: 0.9020 Epoch 27/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9002 - val_loss: 0.7906 - val_accuracy: 0.9021 Epoch 28/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9002 - val_loss: 0.7910 - val_accuracy: 0.9010 Epoch 29/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9008 - val_loss: 0.7892 - val_accuracy: 0.9034 Epoch 30/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8050 - accuracy: 0.9003 - val_loss: 0.7895 - val_accuracy: 0.9023 Epoch 31/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9006 - val_loss: 0.7904 - val_accuracy: 0.9025 Epoch 32/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8051 - accuracy: 0.9004 - val_loss: 0.7905 - val_accuracy: 0.9021 Epoch 33/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9001 - val_loss: 0.7904 - val_accuracy: 0.9019 Epoch 34/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8048 - accuracy: 0.9004 - val_loss: 0.7894 - val_accuracy: 0.9030 Epoch 35/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9006 - val_loss: 0.7892 - val_accuracy: 0.9028 Epoch 36/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8046 - accuracy: 0.9005 - val_loss: 0.7896 - val_accuracy: 0.9028 Epoch 37/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9007 - val_loss: 0.7893 - val_accuracy: 0.9030 Epoch 38/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9009 - val_loss: 0.7902 - val_accuracy: 0.9022 Epoch 39/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7893 - val_accuracy: 0.9026 Epoch 40/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9006 - val_loss: 0.7894 - val_accuracy: 0.9024 Epoch 41/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9005 - val_loss: 0.7900 - val_accuracy: 0.9019 Epoch 42/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9007 - val_loss: 0.7901 - val_accuracy: 0.9025 Epoch 43/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7896 - val_accuracy: 0.9021 Epoch 44/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9006 - val_loss: 0.7900 - val_accuracy: 0.9026 Epoch 45/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9005 - val_loss: 0.7885 - val_accuracy: 0.9034 Epoch 46/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7890 - val_accuracy: 0.9030 Epoch 47/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9003 - val_loss: 0.7883 - val_accuracy: 0.9025 Epoch 48/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8043 - accuracy: 0.9006 - val_loss: 0.7886 - val_accuracy: 0.9025 Epoch 49/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7904 - val_accuracy: 0.9019 Epoch 50/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7884 - val_accuracy: 0.9032 Epoch 51/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9003 - val_loss: 0.7886 - val_accuracy: 0.9023 Epoch 52/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9008 - val_loss: 0.7891 - val_accuracy: 0.9027 Epoch 53/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8042 - accuracy: 0.9004 - val_loss: 0.7893 - val_accuracy: 0.9031 Epoch 54/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9004 - val_loss: 0.7887 - val_accuracy: 0.9030 Epoch 55/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7888 - val_accuracy: 0.9034 Epoch 56/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9007 - val_loss: 0.7899 - val_accuracy: 0.9021 Epoch 57/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8044 - accuracy: 0.9005 - val_loss: 0.7877 - val_accuracy: 0.9037 Epoch 58/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9032 Epoch 59/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9032 Epoch 60/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8041 - accuracy: 0.9007 - val_loss: 0.7888 - val_accuracy: 0.9030 Epoch 61/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8040 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9034 Epoch 62/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7884 - val_accuracy: 0.9030 Epoch 63/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9033 Epoch 64/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9013 - val_loss: 0.7890 - val_accuracy: 0.9031 Epoch 65/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7893 - val_accuracy: 0.9026 Epoch 66/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 67/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9035 Epoch 68/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7891 - val_accuracy: 0.9030 Epoch 69/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7887 - val_accuracy: 0.9029 Epoch 70/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7888 - val_accuracy: 0.9032 Epoch 71/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7892 - val_accuracy: 0.9035 Epoch 72/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9034 Epoch 73/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7882 - val_accuracy: 0.9028 Epoch 74/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9030 Epoch 75/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9027 Epoch 76/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9030 Epoch 77/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9027 Epoch 78/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7884 - val_accuracy: 0.9024 Epoch 79/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9032 Epoch 80/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7883 - val_accuracy: 0.9031 Epoch 81/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9020 Epoch 82/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8039 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9031 Epoch 83/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9005 - val_loss: 0.7889 - val_accuracy: 0.9027 Epoch 84/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7889 - val_accuracy: 0.9029 Epoch 85/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7893 - val_accuracy: 0.9025 Epoch 86/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9027 Epoch 87/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7888 - val_accuracy: 0.9030 Epoch 88/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9003 - val_loss: 0.7888 - val_accuracy: 0.9023 Epoch 89/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9028 Epoch 90/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9029 Epoch 91/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7888 - val_accuracy: 0.9024 Epoch 92/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7890 - val_accuracy: 0.9026 Epoch 93/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7891 - val_accuracy: 0.9025 Epoch 94/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9007 - val_loss: 0.7889 - val_accuracy: 0.9029 Epoch 95/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9006 - val_loss: 0.7883 - val_accuracy: 0.9024 Epoch 96/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9008 - val_loss: 0.7892 - val_accuracy: 0.9032 Epoch 97/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7881 - val_accuracy: 0.9027 Epoch 98/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7882 - val_accuracy: 0.9026 Epoch 99/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9028 Epoch 100/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9005 - val_loss: 0.7893 - val_accuracy: 0.9026 Epoch 101/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9025 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9005 - val_loss: 0.7882 - val_accuracy: 0.9036 Epoch 103/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9029 Epoch 104/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7886 - val_accuracy: 0.9028 Epoch 105/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7896 - val_accuracy: 0.9025 Epoch 106/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9024 Epoch 107/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8033 - accuracy: 0.9008 - val_loss: 0.7883 - val_accuracy: 0.9034 Epoch 108/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9006 - val_loss: 0.7890 - val_accuracy: 0.9032 Epoch 109/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7890 - val_accuracy: 0.9024 Epoch 110/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9030 Epoch 111/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9033 Epoch 112/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9025 Epoch 113/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9014 - val_loss: 0.7892 - val_accuracy: 0.9018 Epoch 114/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9013 - val_loss: 0.7884 - val_accuracy: 0.9032 Epoch 115/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9023 Epoch 116/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7887 - val_accuracy: 0.9025 Epoch 117/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7883 - val_accuracy: 0.9028 Epoch 118/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9029 Epoch 119/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7878 - val_accuracy: 0.9029 Epoch 120/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7887 - val_accuracy: 0.9026 Epoch 121/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9031 Epoch 122/200 235/235 [==============================] - 2s 9ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7889 - val_accuracy: 0.9027 Epoch 123/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9013 - val_loss: 0.7888 - val_accuracy: 0.9025 Epoch 124/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7881 - val_accuracy: 0.9032 Epoch 125/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7898 - val_accuracy: 0.9016 Epoch 126/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9007 - val_loss: 0.7885 - val_accuracy: 0.9031 Epoch 127/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9033 Epoch 128/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7892 - val_accuracy: 0.9029 Epoch 129/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7881 - val_accuracy: 0.9022 Epoch 130/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9008 - val_loss: 0.7889 - val_accuracy: 0.9031 Epoch 131/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9027 Epoch 132/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7882 - val_accuracy: 0.9029 Epoch 133/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9029 Epoch 134/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9024 Epoch 135/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7884 - val_accuracy: 0.9029 Epoch 136/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7893 - val_accuracy: 0.9023 Epoch 137/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7888 - val_accuracy: 0.9024 Epoch 138/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9013 - val_loss: 0.7883 - val_accuracy: 0.9030 Epoch 139/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9021 Epoch 140/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9006 - val_loss: 0.7899 - val_accuracy: 0.9024 Epoch 141/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8038 - accuracy: 0.9009 - val_loss: 0.7886 - val_accuracy: 0.9029 Epoch 142/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9006 - val_loss: 0.7888 - val_accuracy: 0.9028 Epoch 143/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7891 - val_accuracy: 0.9019 Epoch 144/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7887 - val_accuracy: 0.9024 Epoch 145/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7892 - val_accuracy: 0.9024 Epoch 146/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8037 - accuracy: 0.9010 - val_loss: 0.7888 - val_accuracy: 0.9028 Epoch 147/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9029 Epoch 148/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7880 - val_accuracy: 0.9027 Epoch 149/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7880 - val_accuracy: 0.9024 Epoch 150/200 235/235 [==============================] - 2s 7ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7901 - val_accuracy: 0.9017 Epoch 151/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9005 - val_loss: 0.7887 - val_accuracy: 0.9028 Epoch 152/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9009 - val_loss: 0.7889 - val_accuracy: 0.9030 Epoch 153/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9029 Epoch 154/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7890 - val_accuracy: 0.9026 Epoch 155/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9008 - val_loss: 0.7893 - val_accuracy: 0.9026 Epoch 156/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7891 - val_accuracy: 0.9029 Epoch 157/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9032 Epoch 158/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7873 - val_accuracy: 0.9034 Epoch 159/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7885 - val_accuracy: 0.9028 Epoch 160/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7878 - val_accuracy: 0.9030 Epoch 161/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8029 - accuracy: 0.9012 - val_loss: 0.7884 - val_accuracy: 0.9034 Epoch 162/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9007 - val_loss: 0.7886 - val_accuracy: 0.9027 Epoch 163/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9009 - val_loss: 0.7887 - val_accuracy: 0.9030 Epoch 164/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7884 - val_accuracy: 0.9029 Epoch 165/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7887 - val_accuracy: 0.9028 Epoch 166/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9011 - val_loss: 0.7887 - val_accuracy: 0.9027 Epoch 167/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9017 Epoch 168/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9005 - val_loss: 0.7897 - val_accuracy: 0.9024 Epoch 169/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9006 - val_loss: 0.7885 - val_accuracy: 0.9033 Epoch 170/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9032 Epoch 171/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7883 - val_accuracy: 0.9024 Epoch 172/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9009 - val_loss: 0.7880 - val_accuracy: 0.9026 Epoch 173/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9035 Epoch 174/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9010 - val_loss: 0.7889 - val_accuracy: 0.9029 Epoch 175/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8031 - accuracy: 0.9011 - val_loss: 0.7885 - val_accuracy: 0.9028 Epoch 176/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9029 Epoch 177/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9008 - val_loss: 0.7897 - val_accuracy: 0.9025 Epoch 178/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9007 - val_loss: 0.7882 - val_accuracy: 0.9025 Epoch 179/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8029 - accuracy: 0.9011 - val_loss: 0.7878 - val_accuracy: 0.9024 Epoch 180/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9007 - val_loss: 0.7893 - val_accuracy: 0.9022 Epoch 181/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8036 - accuracy: 0.9008 - val_loss: 0.7892 - val_accuracy: 0.9025 Epoch 182/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9010 - val_loss: 0.7883 - val_accuracy: 0.9032 Epoch 183/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9006 - val_loss: 0.7881 - val_accuracy: 0.9034 Epoch 184/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9008 - val_loss: 0.7886 - val_accuracy: 0.9023 Epoch 185/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9011 - val_loss: 0.7886 - val_accuracy: 0.9017 Epoch 186/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7876 - val_accuracy: 0.9042 Epoch 187/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7881 - val_accuracy: 0.9032 Epoch 188/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9012 - val_loss: 0.7872 - val_accuracy: 0.9042 Epoch 189/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8030 - accuracy: 0.9011 - val_loss: 0.7879 - val_accuracy: 0.9028 Epoch 190/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9012 - val_loss: 0.7889 - val_accuracy: 0.9031 Epoch 191/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8035 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9027 Epoch 192/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7877 - val_accuracy: 0.9031 Epoch 193/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7891 - val_accuracy: 0.9022 Epoch 194/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7882 - val_accuracy: 0.9029 Epoch 195/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8034 - accuracy: 0.9010 - val_loss: 0.7885 - val_accuracy: 0.9032 Epoch 196/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7879 - val_accuracy: 0.9036 Epoch 197/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9010 - val_loss: 0.7875 - val_accuracy: 0.9036 Epoch 198/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9013 - val_loss: 0.7874 - val_accuracy: 0.9043 Epoch 199/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8033 - accuracy: 0.9010 - val_loss: 0.7878 - val_accuracy: 0.9027 Epoch 200/200 235/235 [==============================] - 2s 8ms/step - loss: 0.8032 - accuracy: 0.9012 - val_loss: 0.7880 - val_accuracy: 0.9029 Epoch 1/200 235/235 [==============================] - 2s 8ms/step - loss: 0.4638 - accuracy: 0.8712 - val_loss: 0.2498 - val_accuracy: 0.9265 Epoch 2/200 235/235 [==============================] - 2s 8ms/step - loss: 0.2248 - accuracy: 0.9355 - val_loss: 0.1868 - val_accuracy: 0.9447 Epoch 3/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1700 - accuracy: 0.9509 - val_loss: 0.1531 - val_accuracy: 0.9532 Epoch 4/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1361 - accuracy: 0.9603 - val_loss: 0.1330 - val_accuracy: 0.9584 Epoch 5/200 235/235 [==============================] - 2s 8ms/step - loss: 0.1122 - accuracy: 0.9676 - val_loss: 0.1196 - val_accuracy: 0.9624 Epoch 6/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0941 - accuracy: 0.9723 - val_loss: 0.1110 - val_accuracy: 0.9649 Epoch 7/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0801 - accuracy: 0.9768 - val_loss: 0.1045 - val_accuracy: 0.9665 Epoch 8/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0690 - accuracy: 0.9801 - val_loss: 0.0999 - val_accuracy: 0.9679 Epoch 9/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0599 - accuracy: 0.9832 - val_loss: 0.0988 - val_accuracy: 0.9684 Epoch 10/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0522 - accuracy: 0.9856 - val_loss: 0.0983 - val_accuracy: 0.9690 Epoch 11/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0453 - accuracy: 0.9879 - val_loss: 0.0983 - val_accuracy: 0.9693 Epoch 12/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0396 - accuracy: 0.9896 - val_loss: 0.1005 - val_accuracy: 0.9690 Epoch 13/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0346 - accuracy: 0.9911 - val_loss: 0.1027 - val_accuracy: 0.9693 Epoch 14/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0300 - accuracy: 0.9926 - val_loss: 0.1043 - val_accuracy: 0.9690 Epoch 15/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0259 - accuracy: 0.9939 - val_loss: 0.1056 - val_accuracy: 0.9704 Epoch 16/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0225 - accuracy: 0.9949 - val_loss: 0.1075 - val_accuracy: 0.9698 Epoch 17/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0195 - accuracy: 0.9956 - val_loss: 0.1099 - val_accuracy: 0.9702 Epoch 18/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0170 - accuracy: 0.9964 - val_loss: 0.1143 - val_accuracy: 0.9699 Epoch 19/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0146 - accuracy: 0.9971 - val_loss: 0.1145 - val_accuracy: 0.9702 Epoch 20/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0124 - accuracy: 0.9977 - val_loss: 0.1195 - val_accuracy: 0.9691 Epoch 21/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0109 - accuracy: 0.9981 - val_loss: 0.1280 - val_accuracy: 0.9676 Epoch 22/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0095 - accuracy: 0.9984 - val_loss: 0.1367 - val_accuracy: 0.9672 Epoch 23/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0087 - accuracy: 0.9987 - val_loss: 0.1303 - val_accuracy: 0.9692 Epoch 24/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0086 - accuracy: 0.9983 - val_loss: 0.1309 - val_accuracy: 0.9697 Epoch 25/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0093 - accuracy: 0.9976 - val_loss: 0.1278 - val_accuracy: 0.9715 Epoch 26/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.1296 - val_accuracy: 0.9724 Epoch 27/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0089 - accuracy: 0.9975 - val_loss: 0.1314 - val_accuracy: 0.9713 Epoch 28/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9975 - val_loss: 0.1380 - val_accuracy: 0.9726 Epoch 29/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.1569 - val_accuracy: 0.9666 Epoch 30/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0063 - accuracy: 0.9983 - val_loss: 0.1258 - val_accuracy: 0.9731 Epoch 31/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0048 - accuracy: 0.9989 - val_loss: 0.1397 - val_accuracy: 0.9710 Epoch 32/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0035 - accuracy: 0.9994 - val_loss: 0.1303 - val_accuracy: 0.9744 Epoch 33/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9994 - val_loss: 0.1317 - val_accuracy: 0.9737 Epoch 34/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9997 - val_loss: 0.1424 - val_accuracy: 0.9721 Epoch 35/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 0.1484 - val_accuracy: 0.9711 Epoch 36/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.1349 - val_accuracy: 0.9754 Epoch 37/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1460 - val_accuracy: 0.9731 Epoch 38/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9974 - val_loss: 0.1567 - val_accuracy: 0.9712 Epoch 39/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0090 - accuracy: 0.9971 - val_loss: 0.1587 - val_accuracy: 0.9720 Epoch 40/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.1458 - val_accuracy: 0.9742 Epoch 41/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.1435 - val_accuracy: 0.9747 Epoch 42/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.1354 - val_accuracy: 0.9758 Epoch 43/200 235/235 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9998 - val_loss: 0.1322 - val_accuracy: 0.9771 Epoch 44/200 235/235 [==============================] - 2s 8ms/step - loss: 8.4953e-04 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9764 Epoch 45/200 235/235 [==============================] - 2s 8ms/step - loss: 5.0979e-04 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9768 Epoch 46/200 235/235 [==============================] - 2s 8ms/step - loss: 3.7012e-04 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9764 Epoch 47/200 235/235 [==============================] - 2s 9ms/step - loss: 2.9299e-04 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9766 Epoch 48/200 235/235 [==============================] - 2s 9ms/step - loss: 2.5237e-04 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9766 Epoch 49/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2193e-04 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9764 Epoch 50/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9869e-04 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9765 Epoch 51/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7906e-04 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9767 Epoch 52/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6246e-04 - accuracy: 1.0000 - val_loss: 0.1399 - val_accuracy: 0.9767 Epoch 53/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4785e-04 - accuracy: 1.0000 - val_loss: 0.1409 - val_accuracy: 0.9768 Epoch 54/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3440e-04 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9768 Epoch 55/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2254e-04 - accuracy: 1.0000 - val_loss: 0.1431 - val_accuracy: 0.9767 Epoch 56/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1139e-04 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9765 Epoch 57/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0141e-04 - accuracy: 1.0000 - val_loss: 0.1455 - val_accuracy: 0.9765 Epoch 58/200 235/235 [==============================] - 2s 8ms/step - loss: 9.2156e-05 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9765 Epoch 59/200 235/235 [==============================] - 2s 8ms/step - loss: 8.3571e-05 - accuracy: 1.0000 - val_loss: 0.1481 - val_accuracy: 0.9765 Epoch 60/200 235/235 [==============================] - 2s 8ms/step - loss: 7.5801e-05 - accuracy: 1.0000 - val_loss: 0.1495 - val_accuracy: 0.9763 Epoch 61/200 235/235 [==============================] - 2s 8ms/step - loss: 6.8629e-05 - accuracy: 1.0000 - val_loss: 0.1508 - val_accuracy: 0.9762 Epoch 62/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2011e-05 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9761 Epoch 63/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5940e-05 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9760 Epoch 64/200 235/235 [==============================] - 2s 8ms/step - loss: 5.0451e-05 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9759 Epoch 65/200 235/235 [==============================] - 2s 8ms/step - loss: 4.5384e-05 - accuracy: 1.0000 - val_loss: 0.1567 - val_accuracy: 0.9759 Epoch 66/200 235/235 [==============================] - 2s 8ms/step - loss: 4.0767e-05 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9759 Epoch 67/200 235/235 [==============================] - 2s 8ms/step - loss: 3.6582e-05 - accuracy: 1.0000 - val_loss: 0.1598 - val_accuracy: 0.9759 Epoch 68/200 235/235 [==============================] - 2s 8ms/step - loss: 3.2753e-05 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9757 Epoch 69/200 235/235 [==============================] - 2s 8ms/step - loss: 2.9328e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9757 Epoch 70/200 235/235 [==============================] - 2s 8ms/step - loss: 2.6200e-05 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9758 Epoch 71/200 235/235 [==============================] - 2s 8ms/step - loss: 2.3433e-05 - accuracy: 1.0000 - val_loss: 0.1664 - val_accuracy: 0.9758 Epoch 72/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0853e-05 - accuracy: 1.0000 - val_loss: 0.1681 - val_accuracy: 0.9758 Epoch 73/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8606e-05 - accuracy: 1.0000 - val_loss: 0.1698 - val_accuracy: 0.9760 Epoch 74/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6546e-05 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9760 Epoch 75/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4721e-05 - accuracy: 1.0000 - val_loss: 0.1733 - val_accuracy: 0.9760 Epoch 76/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3059e-05 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.9760 Epoch 77/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1588e-05 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.9759 Epoch 78/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0296e-05 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9759 Epoch 79/200 235/235 [==============================] - 2s 8ms/step - loss: 9.1215e-06 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9758 Epoch 80/200 235/235 [==============================] - 2s 8ms/step - loss: 8.0643e-06 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9758 Epoch 81/200 235/235 [==============================] - 2s 8ms/step - loss: 7.1555e-06 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9758 Epoch 82/200 235/235 [==============================] - 2s 8ms/step - loss: 6.3320e-06 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9758 Epoch 83/200 235/235 [==============================] - 2s 8ms/step - loss: 5.6113e-06 - accuracy: 1.0000 - val_loss: 0.1876 - val_accuracy: 0.9758 Epoch 84/200 235/235 [==============================] - 2s 8ms/step - loss: 4.9556e-06 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9758 Epoch 85/200 235/235 [==============================] - 2s 8ms/step - loss: 4.3823e-06 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9757 Epoch 86/200 235/235 [==============================] - 2s 8ms/step - loss: 3.8796e-06 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9756 Epoch 87/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4264e-06 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9754 Epoch 88/200 235/235 [==============================] - 2s 8ms/step - loss: 3.0346e-06 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9754 Epoch 89/200 235/235 [==============================] - 2s 8ms/step - loss: 2.6798e-06 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9754 Epoch 90/200 235/235 [==============================] - 2s 8ms/step - loss: 2.3682e-06 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9753 Epoch 91/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0967e-06 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9754 Epoch 92/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8517e-06 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9755 Epoch 93/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6378e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9755 Epoch 94/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4507e-06 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9756 Epoch 95/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2838e-06 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9755 Epoch 96/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1365e-06 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9755 Epoch 97/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0071e-06 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9756 Epoch 98/200 235/235 [==============================] - 2s 8ms/step - loss: 8.9258e-07 - accuracy: 1.0000 - val_loss: 0.2144 - val_accuracy: 0.9754 Epoch 99/200 235/235 [==============================] - 2s 8ms/step - loss: 7.9194e-07 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9752 Epoch 100/200 235/235 [==============================] - 2s 8ms/step - loss: 7.0228e-07 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9751 Epoch 101/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2214e-07 - accuracy: 1.0000 - val_loss: 0.2194 - val_accuracy: 0.9752 Epoch 102/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5315e-07 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9751 Epoch 103/200 235/235 [==============================] - 2s 8ms/step - loss: 4.9180e-07 - accuracy: 1.0000 - val_loss: 0.2228 - val_accuracy: 0.9751 Epoch 104/200 235/235 [==============================] - 2s 8ms/step - loss: 4.3821e-07 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9751 Epoch 105/200 235/235 [==============================] - 2s 8ms/step - loss: 3.9014e-07 - accuracy: 1.0000 - val_loss: 0.2261 - val_accuracy: 0.9751 Epoch 106/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4907e-07 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9750 Epoch 107/200 235/235 [==============================] - 2s 8ms/step - loss: 3.1133e-07 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9751 Epoch 108/200 235/235 [==============================] - 2s 8ms/step - loss: 2.7862e-07 - accuracy: 1.0000 - val_loss: 0.2309 - val_accuracy: 0.9751 Epoch 109/200 235/235 [==============================] - 2s 8ms/step - loss: 2.4944e-07 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9750 Epoch 110/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2375e-07 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.9751 Epoch 111/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0102e-07 - accuracy: 1.0000 - val_loss: 0.2355 - val_accuracy: 0.9752 Epoch 112/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8113e-07 - accuracy: 1.0000 - val_loss: 0.2370 - val_accuracy: 0.9750 Epoch 113/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6325e-07 - accuracy: 1.0000 - val_loss: 0.2383 - val_accuracy: 0.9750 Epoch 114/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4751e-07 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9749 Epoch 115/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3340e-07 - accuracy: 1.0000 - val_loss: 0.2410 - val_accuracy: 0.9749 Epoch 116/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2088e-07 - accuracy: 1.0000 - val_loss: 0.2425 - val_accuracy: 0.9748 Epoch 117/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0987e-07 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9749 Epoch 118/200 235/235 [==============================] - 2s 8ms/step - loss: 9.9933e-08 - accuracy: 1.0000 - val_loss: 0.2448 - val_accuracy: 0.9748 Epoch 119/200 235/235 [==============================] - 2s 8ms/step - loss: 9.1151e-08 - accuracy: 1.0000 - val_loss: 0.2461 - val_accuracy: 0.9748 Epoch 120/200 235/235 [==============================] - 2s 8ms/step - loss: 8.3413e-08 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9748 Epoch 121/200 235/235 [==============================] - 2s 8ms/step - loss: 7.6330e-08 - accuracy: 1.0000 - val_loss: 0.2482 - val_accuracy: 0.9748 Epoch 122/200 235/235 [==============================] - 2s 8ms/step - loss: 7.0268e-08 - accuracy: 1.0000 - val_loss: 0.2495 - val_accuracy: 0.9747 Epoch 123/200 235/235 [==============================] - 2s 8ms/step - loss: 6.4804e-08 - accuracy: 1.0000 - val_loss: 0.2507 - val_accuracy: 0.9748 Epoch 124/200 235/235 [==============================] - 2s 8ms/step - loss: 5.9696e-08 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9748 Epoch 125/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5347e-08 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9747 Epoch 126/200 235/235 [==============================] - 2s 8ms/step - loss: 5.1389e-08 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9748 Epoch 127/200 235/235 [==============================] - 2s 8ms/step - loss: 4.7648e-08 - accuracy: 1.0000 - val_loss: 0.2543 - val_accuracy: 0.9748 Epoch 128/200 235/235 [==============================] - 2s 8ms/step - loss: 4.4401e-08 - accuracy: 1.0000 - val_loss: 0.2552 - val_accuracy: 0.9750 Epoch 129/200 235/235 [==============================] - 2s 8ms/step - loss: 4.1570e-08 - accuracy: 1.0000 - val_loss: 0.2561 - val_accuracy: 0.9747 Epoch 130/200 235/235 [==============================] - 2s 8ms/step - loss: 3.8848e-08 - accuracy: 1.0000 - val_loss: 0.2569 - val_accuracy: 0.9748 Epoch 131/200 235/235 [==============================] - 2s 8ms/step - loss: 3.6373e-08 - accuracy: 1.0000 - val_loss: 0.2576 - val_accuracy: 0.9747 Epoch 132/200 235/235 [==============================] - 2s 8ms/step - loss: 3.4205e-08 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9746 Epoch 133/200 235/235 [==============================] - 2s 8ms/step - loss: 3.2196e-08 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9746 Epoch 134/200 235/235 [==============================] - 2s 8ms/step - loss: 3.0398e-08 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9746 Epoch 135/200 235/235 [==============================] - 2s 8ms/step - loss: 2.8725e-08 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9747 Epoch 136/200 235/235 [==============================] - 2s 8ms/step - loss: 2.7271e-08 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9747 Epoch 137/200 235/235 [==============================] - 2s 8ms/step - loss: 2.5934e-08 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9747 Epoch 138/200 235/235 [==============================] - 2s 8ms/step - loss: 2.4686e-08 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9747 Epoch 139/200 235/235 [==============================] - 2s 8ms/step - loss: 2.3409e-08 - accuracy: 1.0000 - val_loss: 0.2629 - val_accuracy: 0.9747 Epoch 140/200 235/235 [==============================] - 2s 8ms/step - loss: 2.2366e-08 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9747 Epoch 141/200 235/235 [==============================] - 2s 8ms/step - loss: 2.1380e-08 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9746 Epoch 142/200 235/235 [==============================] - 2s 8ms/step - loss: 2.0470e-08 - accuracy: 1.0000 - val_loss: 0.2645 - val_accuracy: 0.9745 Epoch 143/200 235/235 [==============================] - 2s 8ms/step - loss: 1.9560e-08 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9745 Epoch 144/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8742e-08 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9745 Epoch 145/200 235/235 [==============================] - 2s 8ms/step - loss: 1.8015e-08 - accuracy: 1.0000 - val_loss: 0.2659 - val_accuracy: 0.9745 Epoch 146/200 235/235 [==============================] - 2s 8ms/step - loss: 1.7315e-08 - accuracy: 1.0000 - val_loss: 0.2664 - val_accuracy: 0.9745 Epoch 147/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6709e-08 - accuracy: 1.0000 - val_loss: 0.2667 - val_accuracy: 0.9744 Epoch 148/200 235/235 [==============================] - 2s 8ms/step - loss: 1.6183e-08 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9744 Epoch 149/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5581e-08 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9744 Epoch 150/200 235/235 [==============================] - 2s 8ms/step - loss: 1.5094e-08 - accuracy: 1.0000 - val_loss: 0.2681 - val_accuracy: 0.9745 Epoch 151/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4516e-08 - accuracy: 1.0000 - val_loss: 0.2684 - val_accuracy: 0.9745 Epoch 152/200 235/235 [==============================] - 2s 8ms/step - loss: 1.4065e-08 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9745 Epoch 153/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3592e-08 - accuracy: 1.0000 - val_loss: 0.2692 - val_accuracy: 0.9745 Epoch 154/200 235/235 [==============================] - 2s 8ms/step - loss: 1.3212e-08 - accuracy: 1.0000 - val_loss: 0.2696 - val_accuracy: 0.9743 Epoch 155/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2785e-08 - accuracy: 1.0000 - val_loss: 0.2699 - val_accuracy: 0.9744 Epoch 156/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2416e-08 - accuracy: 1.0000 - val_loss: 0.2703 - val_accuracy: 0.9743 Epoch 157/200 235/235 [==============================] - 2s 8ms/step - loss: 1.2068e-08 - accuracy: 1.0000 - val_loss: 0.2706 - val_accuracy: 0.9744 Epoch 158/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1754e-08 - accuracy: 1.0000 - val_loss: 0.2709 - val_accuracy: 0.9744 Epoch 159/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1361e-08 - accuracy: 1.0000 - val_loss: 0.2711 - val_accuracy: 0.9743 Epoch 160/200 235/235 [==============================] - 2s 8ms/step - loss: 1.1057e-08 - accuracy: 1.0000 - val_loss: 0.2714 - val_accuracy: 0.9744 Epoch 161/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0788e-08 - accuracy: 1.0000 - val_loss: 0.2716 - val_accuracy: 0.9743 Epoch 162/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0478e-08 - accuracy: 1.0000 - val_loss: 0.2719 - val_accuracy: 0.9743 Epoch 163/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0276e-08 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9744 Epoch 164/200 235/235 [==============================] - 2s 8ms/step - loss: 1.0024e-08 - accuracy: 1.0000 - val_loss: 0.2723 - val_accuracy: 0.9742 Epoch 165/200 235/235 [==============================] - 2s 8ms/step - loss: 9.7613e-09 - accuracy: 1.0000 - val_loss: 0.2725 - val_accuracy: 0.9743 Epoch 166/200 235/235 [==============================] - 2s 8ms/step - loss: 9.5526e-09 - accuracy: 1.0000 - val_loss: 0.2727 - val_accuracy: 0.9743 Epoch 167/200 235/235 [==============================] - 2s 9ms/step - loss: 9.3182e-09 - accuracy: 1.0000 - val_loss: 0.2729 - val_accuracy: 0.9744 Epoch 168/200 235/235 [==============================] - 2s 9ms/step - loss: 9.1553e-09 - accuracy: 1.0000 - val_loss: 0.2731 - val_accuracy: 0.9744 Epoch 169/200 235/235 [==============================] - 2s 8ms/step - loss: 8.9208e-09 - accuracy: 1.0000 - val_loss: 0.2733 - val_accuracy: 0.9744 Epoch 170/200 235/235 [==============================] - 2s 8ms/step - loss: 8.7440e-09 - accuracy: 1.0000 - val_loss: 0.2734 - val_accuracy: 0.9743 Epoch 171/200 235/235 [==============================] - 2s 8ms/step - loss: 8.5572e-09 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9743 Epoch 172/200 235/235 [==============================] - 2s 8ms/step - loss: 8.3784e-09 - accuracy: 1.0000 - val_loss: 0.2737 - val_accuracy: 0.9743 Epoch 173/200 235/235 [==============================] - 2s 8ms/step - loss: 8.2155e-09 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9743 Epoch 174/200 235/235 [==============================] - 2s 8ms/step - loss: 8.0367e-09 - accuracy: 1.0000 - val_loss: 0.2740 - val_accuracy: 0.9745 Epoch 175/200 235/235 [==============================] - 2s 8ms/step - loss: 7.8460e-09 - accuracy: 1.0000 - val_loss: 0.2741 - val_accuracy: 0.9745 Epoch 176/200 235/235 [==============================] - 2s 8ms/step - loss: 7.7049e-09 - accuracy: 1.0000 - val_loss: 0.2743 - val_accuracy: 0.9746 Epoch 177/200 235/235 [==============================] - 2s 8ms/step - loss: 7.5698e-09 - accuracy: 1.0000 - val_loss: 0.2745 - val_accuracy: 0.9746 Epoch 178/200 235/235 [==============================] - 2s 8ms/step - loss: 7.4367e-09 - accuracy: 1.0000 - val_loss: 0.2746 - val_accuracy: 0.9745 Epoch 179/200 235/235 [==============================] - 2s 8ms/step - loss: 7.2996e-09 - accuracy: 1.0000 - val_loss: 0.2747 - val_accuracy: 0.9746 Epoch 180/200 235/235 [==============================] - 2s 8ms/step - loss: 7.1645e-09 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9746 Epoch 181/200 235/235 [==============================] - 2s 8ms/step - loss: 6.9857e-09 - accuracy: 1.0000 - val_loss: 0.2750 - val_accuracy: 0.9746 Epoch 182/200 235/235 [==============================] - 2s 8ms/step - loss: 6.9022e-09 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9746 Epoch 183/200 235/235 [==============================] - 2s 8ms/step - loss: 6.7830e-09 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9746 Epoch 184/200 235/235 [==============================] - 2s 8ms/step - loss: 6.6141e-09 - accuracy: 1.0000 - val_loss: 0.2753 - val_accuracy: 0.9747 Epoch 185/200 235/235 [==============================] - 2s 8ms/step - loss: 6.5943e-09 - accuracy: 1.0000 - val_loss: 0.2754 - val_accuracy: 0.9748 Epoch 186/200 235/235 [==============================] - 2s 9ms/step - loss: 6.4552e-09 - accuracy: 1.0000 - val_loss: 0.2755 - val_accuracy: 0.9748 Epoch 187/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2724e-09 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9748 Epoch 188/200 235/235 [==============================] - 2s 8ms/step - loss: 6.2466e-09 - accuracy: 1.0000 - val_loss: 0.2758 - val_accuracy: 0.9748 Epoch 189/200 235/235 [==============================] - 2s 8ms/step - loss: 6.1115e-09 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9750 Epoch 190/200 235/235 [==============================] - 2s 8ms/step - loss: 6.0002e-09 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9750 Epoch 191/200 235/235 [==============================] - 2s 8ms/step - loss: 5.8929e-09 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9750 Epoch 192/200 235/235 [==============================] - 2s 8ms/step - loss: 5.8194e-09 - accuracy: 1.0000 - val_loss: 0.2761 - val_accuracy: 0.9750 Epoch 193/200 235/235 [==============================] - 2s 8ms/step - loss: 5.7121e-09 - accuracy: 1.0000 - val_loss: 0.2762 - val_accuracy: 0.9750 Epoch 194/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5989e-09 - accuracy: 1.0000 - val_loss: 0.2763 - val_accuracy: 0.9750 Epoch 195/200 235/235 [==============================] - 2s 8ms/step - loss: 5.5532e-09 - accuracy: 1.0000 - val_loss: 0.2763 - val_accuracy: 0.9749 Epoch 196/200 235/235 [==============================] - 2s 8ms/step - loss: 5.4896e-09 - accuracy: 1.0000 - val_loss: 0.2764 - val_accuracy: 0.9749 Epoch 197/200 235/235 [==============================] - 2s 8ms/step - loss: 5.3465e-09 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9748 Epoch 198/200 235/235 [==============================] - 2s 8ms/step - loss: 5.2909e-09 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9748 Epoch 199/200 235/235 [==============================] - 2s 8ms/step - loss: 5.1936e-09 - accuracy: 1.0000 - val_loss: 0.2766 - val_accuracy: 0.9748 Epoch 200/200 235/235 [==============================] - 2s 8ms/step - loss: 5.1379e-09 - accuracy: 1.0000 - val_loss: 0.2767 - val_accuracy: 0.9748 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03714788891375065 Thresholhold 0.06566019356250763 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.5 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 1. ... 1. 0. 0.] [0. 0. 1. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 1. 1.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 1. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.06050238758325577 Thresholhold -0.09211743623018265 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.1136733777821064 Thresholhold 0.0008880794048309326 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 5/235 [..............................] - ETA: 3s - loss: 7.3886 - accuracy: 0.3617 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0138s vs `on_train_batch_begin` time: 11.2385s). Check your callbacks. 235/235 [==============================] - 71s 13ms/step - loss: 2.2185 - accuracy: 0.9145 - val_loss: 1.8999 - val_accuracy: 0.6117 [1.6624929e-06 3.2097237e-06 2.1899720e-07 ... 9.3255341e-02 1.3094884e-01 2.7217984e-02] Sparsity at: 0.44178812922614574 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4550 - accuracy: 0.9610 - val_loss: 0.6002 - val_accuracy: 0.9487 [2.8944019e-12 9.3174340e-12 1.2026992e-12 ... 4.5141589e-02 8.6929470e-02 3.0727102e-03] Sparsity at: 0.44178812922614574 Epoch 3/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2909 - accuracy: 0.9676 - val_loss: 0.3207 - val_accuracy: 0.9528 [ 2.0241179e-17 4.9652535e-17 3.3510400e-19 ... 1.2248518e-02 6.9727145e-02 -1.1147665e-02] Sparsity at: 0.44178812922614574 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2583 - accuracy: 0.9688 - val_loss: 0.2643 - val_accuracy: 0.9624 [-1.0361830e-22 3.6511893e-22 3.9754488e-23 ... -5.1917536e-03 5.8777627e-02 -1.5626829e-02] Sparsity at: 0.44178812922614574 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2394 - accuracy: 0.9707 - val_loss: 0.2734 - val_accuracy: 0.9583 [-6.5022505e-28 -1.0774632e-27 4.2780378e-29 ... -1.5886651e-02 4.9290005e-02 -1.8585239e-02] Sparsity at: 0.44178812922614574 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2287 - accuracy: 0.9716 - val_loss: 0.2598 - val_accuracy: 0.9590 [ 3.3639677e-33 -3.9719219e-33 3.0523619e-34 ... -1.8727832e-02 3.4742780e-02 -1.4041513e-02] Sparsity at: 0.44178812922614574 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2180 - accuracy: 0.9733 - val_loss: 0.2510 - val_accuracy: 0.9593 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.5441591e-02 2.8049611e-02 -1.0453678e-02] Sparsity at: 0.4417918858001503 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2125 - accuracy: 0.9729 - val_loss: 0.2344 - val_accuracy: 0.9648 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.6361793e-02 7.1320422e-03 -7.4196788e-03] Sparsity at: 0.44179564237415475 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2087 - accuracy: 0.9737 - val_loss: 0.2479 - val_accuracy: 0.9576 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.9167842e-02 -5.3540105e-03 -4.9064197e-03] Sparsity at: 0.44179564237415475 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2023 - accuracy: 0.9747 - val_loss: 0.2367 - val_accuracy: 0.9615 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.8079091e-02 -1.5780313e-02 -5.0926567e-03] Sparsity at: 0.44179564237415475 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1996 - accuracy: 0.9738 - val_loss: 0.2331 - val_accuracy: 0.9621 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.7705369e-02 -2.1619046e-02 -1.3931784e-03] Sparsity at: 0.4417993989481593 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1917 - accuracy: 0.9749 - val_loss: 0.2242 - val_accuracy: 0.9635 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.0437108e-02 -2.9820310e-02 -2.9129798e-03] Sparsity at: 0.4417993989481593 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1874 - accuracy: 0.9751 - val_loss: 0.2072 - val_accuracy: 0.9687 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.2768909e-02 -3.3885043e-02 -1.2541483e-03] Sparsity at: 0.4417993989481593 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1856 - accuracy: 0.9752 - val_loss: 0.2300 - val_accuracy: 0.9603 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.0562755e-02 -3.5466906e-02 -1.0007619e-03] Sparsity at: 0.44180315552216376 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1821 - accuracy: 0.9757 - val_loss: 0.2253 - val_accuracy: 0.9602 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.5053778e-02 -3.3153541e-02 -1.7603629e-04] Sparsity at: 0.44180315552216376 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1836 - accuracy: 0.9750 - val_loss: 0.2137 - val_accuracy: 0.9655 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.0226177e-02 -3.1429261e-02 -6.3466090e-03] Sparsity at: 0.44180315552216376 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1754 - accuracy: 0.9768 - val_loss: 0.2126 - val_accuracy: 0.9628 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.0496808e-02 -2.9177219e-02 9.8887214e-04] Sparsity at: 0.44180315552216376 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1743 - accuracy: 0.9764 - val_loss: 0.2120 - val_accuracy: 0.9640 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.7454864e-02 -3.3434249e-02 1.7972835e-03] Sparsity at: 0.4417993989481593 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1728 - accuracy: 0.9762 - val_loss: 0.2190 - val_accuracy: 0.9610 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.6760485e-02 -3.3223599e-02 2.9191773e-03] Sparsity at: 0.4417993989481593 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1683 - accuracy: 0.9771 - val_loss: 0.2002 - val_accuracy: 0.9676 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.5979055e-02 -3.1245666e-02 4.0384433e-03] Sparsity at: 0.4417993989481593 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1699 - accuracy: 0.9758 - val_loss: 0.2247 - val_accuracy: 0.9579 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.8438160e-02 -2.7736817e-02 3.9808271e-03] Sparsity at: 0.4417993989481593 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1657 - accuracy: 0.9779 - val_loss: 0.2125 - val_accuracy: 0.9612 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.4062922e-02 -3.0230768e-02 4.2583174e-03] Sparsity at: 0.4417993989481593 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1651 - accuracy: 0.9768 - val_loss: 0.2082 - val_accuracy: 0.9636 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -3.1682074e-02 -2.8027184e-02 2.7294154e-03] Sparsity at: 0.4417993989481593 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9777 - val_loss: 0.2271 - val_accuracy: 0.9569 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.7358564e-02 -3.1847972e-02 5.3230035e-03] Sparsity at: 0.4417993989481593 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1626 - accuracy: 0.9772 - val_loss: 0.1951 - val_accuracy: 0.9649 [-5.9413406e-34 -3.3333806e-34 3.0523619e-34 ... -2.3396580e-02 -2.7448196e-02 2.4411208e-03] Sparsity at: 0.4417993989481593 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1590 - accuracy: 0.9783 - val_loss: 0.2211 - val_accuracy: 0.9602 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3923736e-02 -2.7993280e-02 3.6120310e-03] Sparsity at: 0.4417993989481593 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1597 - accuracy: 0.9774 - val_loss: 0.2095 - val_accuracy: 0.9639 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.5788357e-02 -2.4359895e-02 8.9152381e-03] Sparsity at: 0.4417993989481593 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1611 - accuracy: 0.9776 - val_loss: 0.2147 - val_accuracy: 0.9614 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.9488184e-02 -2.5801526e-02 8.3187940e-03] Sparsity at: 0.4417993989481593 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1538 - accuracy: 0.9789 - val_loss: 0.1973 - val_accuracy: 0.9664 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3674978e-02 -2.3095697e-02 5.4526119e-03] Sparsity at: 0.4417993989481593 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1576 - accuracy: 0.9776 - val_loss: 0.1999 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.5240008e-02 -2.6234364e-02 2.8713746e-03] Sparsity at: 0.4417993989481593 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1554 - accuracy: 0.9785 - val_loss: 0.2043 - val_accuracy: 0.9626 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.2578320e-02 -2.8285490e-02 1.8022847e-03] Sparsity at: 0.4417993989481593 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1545 - accuracy: 0.9782 - val_loss: 0.1982 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7176179e-02 -2.6150517e-02 2.4725511e-03] Sparsity at: 0.4417993989481593 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1528 - accuracy: 0.9783 - val_loss: 0.2193 - val_accuracy: 0.9614 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2632781e-02 -2.2746364e-02 -1.7931310e-03] Sparsity at: 0.4417993989481593 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1533 - accuracy: 0.9781 - val_loss: 0.1895 - val_accuracy: 0.9658 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5802259e-02 -2.4988521e-02 4.7082440e-03] Sparsity at: 0.4417993989481593 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1529 - accuracy: 0.9781 - val_loss: 0.1874 - val_accuracy: 0.9664 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2290499e-02 -2.1302354e-02 -8.3416700e-05] Sparsity at: 0.4417993989481593 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1523 - accuracy: 0.9784 - val_loss: 0.2378 - val_accuracy: 0.9542 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.9026682e-03 -2.1507312e-02 4.2525008e-03] Sparsity at: 0.4417993989481593 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1508 - accuracy: 0.9785 - val_loss: 0.2200 - val_accuracy: 0.9575 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.3448283e-03 -2.1004416e-02 3.9713625e-03] Sparsity at: 0.4417993989481593 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1497 - accuracy: 0.9780 - val_loss: 0.1950 - val_accuracy: 0.9651 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1725971e-02 -2.5112376e-02 8.9863949e-03] Sparsity at: 0.4417993989481593 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1480 - accuracy: 0.9786 - val_loss: 0.1933 - val_accuracy: 0.9654 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8046100e-02 -2.6297163e-02 8.2598040e-03] Sparsity at: 0.4417993989481593 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1512 - accuracy: 0.9775 - val_loss: 0.1989 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.4174120e-02 -2.5194939e-02 6.1688498e-03] Sparsity at: 0.4417993989481593 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1464 - accuracy: 0.9793 - val_loss: 0.2063 - val_accuracy: 0.9622 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4970554e-02 -2.2326620e-02 5.6374054e-03] Sparsity at: 0.4417993989481593 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1501 - accuracy: 0.9778 - val_loss: 0.2102 - val_accuracy: 0.9588 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7817944e-02 -2.5036179e-02 7.9446919e-03] Sparsity at: 0.4417993989481593 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1505 - accuracy: 0.9783 - val_loss: 0.2065 - val_accuracy: 0.9629 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6724825e-02 -2.1994717e-02 7.2345664e-03] Sparsity at: 0.4417993989481593 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1483 - accuracy: 0.9786 - val_loss: 0.2036 - val_accuracy: 0.9623 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6407726e-02 -2.1818722e-02 1.1958867e-02] Sparsity at: 0.4417993989481593 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1453 - accuracy: 0.9793 - val_loss: 0.2011 - val_accuracy: 0.9645 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8652368e-02 -2.1983163e-02 1.5262198e-02] Sparsity at: 0.4417993989481593 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1440 - accuracy: 0.9796 - val_loss: 0.2065 - val_accuracy: 0.9608 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1827088e-02 -1.6235866e-02 1.0372529e-02] Sparsity at: 0.4417993989481593 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1481 - accuracy: 0.9783 - val_loss: 0.1985 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5075979e-02 -1.3160142e-02 8.8828318e-03] Sparsity at: 0.4417993989481593 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1476 - accuracy: 0.9779 - val_loss: 0.1923 - val_accuracy: 0.9659 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8134743e-02 -1.2282557e-02 5.5955360e-03] Sparsity at: 0.4417993989481593 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1452 - accuracy: 0.9784 - val_loss: 0.1963 - val_accuracy: 0.9645 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3187683e-02 -1.8805426e-02 8.3560804e-03] Sparsity at: 0.4417993989481593 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1416 - accuracy: 0.9803 - val_loss: 0.2029 - val_accuracy: 0.9638 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4784321e-02 -2.1386396e-02 1.0538180e-02] Sparsity at: 0.4417993989481593 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 1.951142549968065e-34 Thresholhold 4.774037043704479e-34 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.0001698937390694627 Thresholhold 7.183215302575263e-07 Using suggest threshold. Applying new mask Percentage zeros 0.3452 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.028607948215186862 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 208s 12ms/step - loss: 0.1440 - accuracy: 0.9788 - val_loss: 0.1923 - val_accuracy: 0.9637 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3009785e-02 -2.1778932e-02 1.0672458e-02] Sparsity at: 0.609564237415477 Epoch 52/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1410 - accuracy: 0.9798 - val_loss: 0.1800 - val_accuracy: 0.9692 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1169086e-02 -2.0858640e-02 5.8950577e-03] Sparsity at: 0.609564237415477 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9797 - val_loss: 0.1787 - val_accuracy: 0.9675 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4858063e-02 -1.6606886e-02 8.7690596e-03] Sparsity at: 0.609564237415477 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9800 - val_loss: 0.1853 - val_accuracy: 0.9674 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6757669e-02 -2.1829134e-02 1.0400970e-02] Sparsity at: 0.609564237415477 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1443 - accuracy: 0.9786 - val_loss: 0.2177 - val_accuracy: 0.9564 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8442413e-02 -2.3665896e-02 1.4300141e-02] Sparsity at: 0.609564237415477 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1401 - accuracy: 0.9802 - val_loss: 0.2033 - val_accuracy: 0.9616 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5399406e-02 -2.4490070e-02 7.9507884e-03] Sparsity at: 0.609564237415477 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1402 - accuracy: 0.9793 - val_loss: 0.1954 - val_accuracy: 0.9654 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4826988e-02 -2.1621186e-02 1.0209977e-02] Sparsity at: 0.609564237415477 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9804 - val_loss: 0.1915 - val_accuracy: 0.9662 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1701760e-02 -1.9501088e-02 7.5450740e-03] Sparsity at: 0.609564237415477 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1424 - accuracy: 0.9792 - val_loss: 0.1952 - val_accuracy: 0.9647 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.0480463e-02 -1.5338058e-02 6.5389788e-03] Sparsity at: 0.609564237415477 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9805 - val_loss: 0.1839 - val_accuracy: 0.9670 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.6285473e-02 -1.9495077e-02 2.8717907e-03] Sparsity at: 0.609564237415477 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1424 - accuracy: 0.9788 - val_loss: 0.2238 - val_accuracy: 0.9551 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1886345e-02 -1.2139132e-02 -2.6857383e-03] Sparsity at: 0.609564237415477 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9801 - val_loss: 0.1849 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8650474e-02 -9.2257550e-03 -4.3910984e-03] Sparsity at: 0.609564237415477 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9795 - val_loss: 0.1921 - val_accuracy: 0.9658 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3111241e-02 -6.0814638e-03 5.2646971e-03] Sparsity at: 0.609564237415477 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1437 - accuracy: 0.9787 - val_loss: 0.2307 - val_accuracy: 0.9501 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3017526e-02 -1.1560500e-02 7.4404064e-03] Sparsity at: 0.609564237415477 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1429 - accuracy: 0.9789 - val_loss: 0.1797 - val_accuracy: 0.9686 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3956325e-02 -1.1294445e-02 8.2174537e-04] Sparsity at: 0.609564237415477 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9817 - val_loss: 0.1830 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5785404e-02 -8.9000156e-03 8.1034834e-03] Sparsity at: 0.609564237415477 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1399 - accuracy: 0.9796 - val_loss: 0.1915 - val_accuracy: 0.9651 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4858944e-02 -1.5344942e-02 8.3576106e-03] Sparsity at: 0.609564237415477 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9797 - val_loss: 0.1873 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6639698e-02 -1.4613044e-02 6.3805543e-03] Sparsity at: 0.609564237415477 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9798 - val_loss: 0.1986 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7299695e-02 -2.2622205e-02 1.3763225e-02] Sparsity at: 0.609564237415477 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1403 - accuracy: 0.9792 - val_loss: 0.2194 - val_accuracy: 0.9576 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.9209603e-02 -1.7317712e-02 7.2026351e-03] Sparsity at: 0.609564237415477 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9797 - val_loss: 0.1869 - val_accuracy: 0.9673 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.2166521e-03 -1.6770680e-02 3.1982108e-03] Sparsity at: 0.609564237415477 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1411 - accuracy: 0.9791 - val_loss: 0.2114 - val_accuracy: 0.9582 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.9663267e-03 -1.5658598e-02 6.7525562e-03] Sparsity at: 0.609564237415477 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9815 - val_loss: 0.2225 - val_accuracy: 0.9562 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.9517953e-03 -2.5125485e-02 9.5243147e-03] Sparsity at: 0.609564237415477 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9803 - val_loss: 0.1934 - val_accuracy: 0.9629 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2206450e-02 -1.8285898e-02 9.9423276e-03] Sparsity at: 0.609564237415477 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9793 - val_loss: 0.2201 - val_accuracy: 0.9557 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8073155e-02 -1.0135039e-02 4.3367790e-03] Sparsity at: 0.609564237415477 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9804 - val_loss: 0.1884 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3424759e-02 -6.7816973e-03 1.5085875e-03] Sparsity at: 0.609564237415477 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9801 - val_loss: 0.1994 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.4456926e-03 -1.6168116e-02 -3.2373641e-03] Sparsity at: 0.609564237415477 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9800 - val_loss: 0.2038 - val_accuracy: 0.9608 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -1.55844782e-02 -1.43230595e-02 1.94526673e-03] Sparsity at: 0.609564237415477 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9802 - val_loss: 0.1872 - val_accuracy: 0.9645 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5931122e-02 -1.4050139e-02 8.1684312e-04] Sparsity at: 0.609564237415477 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9802 - val_loss: 0.2077 - val_accuracy: 0.9614 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4816974e-02 -1.1518358e-02 -7.6088663e-03] Sparsity at: 0.609564237415477 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9796 - val_loss: 0.1904 - val_accuracy: 0.9647 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5567947e-02 -2.0023316e-02 -1.0892926e-03] Sparsity at: 0.609564237415477 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9802 - val_loss: 0.2020 - val_accuracy: 0.9602 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.0700304e-03 -1.1783031e-02 -1.0664175e-02] Sparsity at: 0.609564237415477 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9795 - val_loss: 0.2073 - val_accuracy: 0.9589 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5626241e-02 -9.4826575e-03 5.9962687e-03] Sparsity at: 0.609564237415477 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9807 - val_loss: 0.1922 - val_accuracy: 0.9644 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7109467e-02 -1.6862454e-02 2.9079670e-03] Sparsity at: 0.609564237415477 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9806 - val_loss: 0.1842 - val_accuracy: 0.9679 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1482121e-02 -1.2760659e-02 1.0351932e-02] Sparsity at: 0.609564237415477 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9803 - val_loss: 0.1878 - val_accuracy: 0.9643 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7338617e-02 -1.2926840e-02 8.9103477e-03] Sparsity at: 0.609564237415477 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9801 - val_loss: 0.1984 - val_accuracy: 0.9616 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6082766e-02 -6.8079256e-03 1.3587286e-03] Sparsity at: 0.609564237415477 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9791 - val_loss: 0.1841 - val_accuracy: 0.9655 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -1.33801885e-02 -9.66754742e-04 -1.67849252e-03] Sparsity at: 0.609564237415477 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9803 - val_loss: 0.1868 - val_accuracy: 0.9663 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1817823e-02 -7.4084168e-03 -4.1128765e-03] Sparsity at: 0.609564237415477 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9810 - val_loss: 0.1896 - val_accuracy: 0.9651 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.6747297e-03 -7.8829834e-03 -1.4410415e-03] Sparsity at: 0.609564237415477 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9802 - val_loss: 0.2034 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.7388102e-03 -1.1696183e-02 -4.7276444e-03] Sparsity at: 0.609564237415477 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9805 - val_loss: 0.1947 - val_accuracy: 0.9628 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0497353e-02 -1.1285525e-02 -1.0306014e-02] Sparsity at: 0.609564237415477 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9814 - val_loss: 0.2067 - val_accuracy: 0.9573 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.6336232e-04 -1.1688329e-02 -1.2427630e-02] Sparsity at: 0.609564237415477 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9802 - val_loss: 0.2315 - val_accuracy: 0.9477 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.9722578e-03 -1.3162703e-02 -7.8375265e-03] Sparsity at: 0.609564237415477 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9803 - val_loss: 0.2116 - val_accuracy: 0.9568 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.2654590e-03 -1.2726340e-02 -2.5439321e-03] Sparsity at: 0.609564237415477 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9807 - val_loss: 0.2018 - val_accuracy: 0.9608 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.7463215e-03 -1.8807502e-02 1.4351456e-03] Sparsity at: 0.609564237415477 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9811 - val_loss: 0.2265 - val_accuracy: 0.9535 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.4009713e-03 -2.3617532e-02 4.5516384e-03] Sparsity at: 0.609564237415477 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9809 - val_loss: 0.1988 - val_accuracy: 0.9619 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.9528639e-03 -2.0288454e-02 1.5092628e-03] Sparsity at: 0.609564237415477 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9793 - val_loss: 0.1737 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5291902e-03 -2.1310160e-02 3.9539309e-03] Sparsity at: 0.609564237415477 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9808 - val_loss: 0.1900 - val_accuracy: 0.9645 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.5850094e-03 -2.4055630e-02 1.0568907e-02] Sparsity at: 0.609564237415477 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 3.143354577822847e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.0001877470280517192 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.3452 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [0. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.03581394905041746 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 181s 12ms/step - loss: 0.1336 - accuracy: 0.9799 - val_loss: 0.2019 - val_accuracy: 0.9608 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.4823935e-04 -2.3401303e-02 4.6648826e-03] Sparsity at: 0.609564237415477 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9812 - val_loss: 0.2020 - val_accuracy: 0.9604 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.8889233e-05 -1.8374164e-02 6.2184692e-03] Sparsity at: 0.609564237415477 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1313 - accuracy: 0.9807 - val_loss: 0.1869 - val_accuracy: 0.9655 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.5065197e-03 -1.9441303e-02 5.5381665e-03] Sparsity at: 0.609564237415477 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1319 - accuracy: 0.9812 - val_loss: 0.2105 - val_accuracy: 0.9589 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.6966999e-03 -1.9471336e-02 5.4654172e-03] Sparsity at: 0.609564237415477 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9816 - val_loss: 0.1755 - val_accuracy: 0.9683 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.1432833e-03 -2.2547727e-02 4.8612226e-03] Sparsity at: 0.609564237415477 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1322 - accuracy: 0.9808 - val_loss: 0.1912 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.6854007e-03 -2.9992647e-02 -2.2462234e-03] Sparsity at: 0.609564237415477 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9807 - val_loss: 0.1757 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.7667999e-03 -2.1794068e-02 -6.7512627e-04] Sparsity at: 0.609564237415477 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1310 - accuracy: 0.9811 - val_loss: 0.2024 - val_accuracy: 0.9590 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.9457803e-03 -2.3391439e-02 -1.6904756e-03] Sparsity at: 0.609564237415477 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9808 - val_loss: 0.1784 - val_accuracy: 0.9668 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.9724092e-03 -1.7304018e-02 1.4380713e-03] Sparsity at: 0.609564237415477 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1320 - accuracy: 0.9810 - val_loss: 0.1874 - val_accuracy: 0.9667 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.3984419e-03 -2.0233029e-02 8.3106281e-03] Sparsity at: 0.609564237415477 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9807 - val_loss: 0.2073 - val_accuracy: 0.9596 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0145960e-02 -2.0343944e-02 1.3624638e-02] Sparsity at: 0.609564237415477 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1324 - accuracy: 0.9811 - val_loss: 0.1789 - val_accuracy: 0.9670 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.0046390e-03 -1.7215738e-02 1.5086930e-02] Sparsity at: 0.609564237415477 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1308 - accuracy: 0.9811 - val_loss: 0.1902 - val_accuracy: 0.9637 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.0807937e-03 -1.5181925e-02 1.1114713e-02] Sparsity at: 0.609564237415477 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1325 - accuracy: 0.9805 - val_loss: 0.1711 - val_accuracy: 0.9701 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.5210806e-03 -2.4222001e-02 1.0298101e-02] Sparsity at: 0.609564237415477 Epoch 115/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9815 - val_loss: 0.1973 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.3107986e-03 -2.2243725e-02 1.0053537e-02] Sparsity at: 0.609564237415477 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1874 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.3004605e-02 -2.7334949e-02 1.0722823e-03] Sparsity at: 0.609564237415477 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9811 - val_loss: 0.1782 - val_accuracy: 0.9681 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.03437435e-02 -2.56974343e-02 1.09554990e-03] Sparsity at: 0.609564237415477 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1309 - accuracy: 0.9803 - val_loss: 0.2044 - val_accuracy: 0.9602 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.7269507e-03 -2.8701954e-02 -4.6813823e-03] Sparsity at: 0.609564237415477 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1328 - accuracy: 0.9800 - val_loss: 0.1990 - val_accuracy: 0.9617 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.2997838e-03 -1.4830215e-02 -1.7447248e-02] Sparsity at: 0.609564237415477 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9814 - val_loss: 0.2033 - val_accuracy: 0.9612 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.3973093e-03 -1.8140521e-02 -1.9303140e-03] Sparsity at: 0.609564237415477 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9800 - val_loss: 0.1821 - val_accuracy: 0.9689 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0888088e-02 -1.7075807e-02 -5.5667997e-04] Sparsity at: 0.609564237415477 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9807 - val_loss: 0.2255 - val_accuracy: 0.9517 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.2573063e-02 -2.0306544e-02 -2.9153950e-03] Sparsity at: 0.609564237415477 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9810 - val_loss: 0.1816 - val_accuracy: 0.9639 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.4670531e-03 -2.3215013e-02 4.2451546e-03] Sparsity at: 0.609564237415477 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9813 - val_loss: 0.2023 - val_accuracy: 0.9595 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.4331760e-03 -1.8110007e-02 3.7053684e-03] Sparsity at: 0.609564237415477 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9811 - val_loss: 0.2223 - val_accuracy: 0.9545 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0646124e-02 -1.9441204e-02 -4.5627235e-03] Sparsity at: 0.609564237415477 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9814 - val_loss: 0.1882 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5151498e-02 -2.0334542e-02 -1.2307422e-02] Sparsity at: 0.609564237415477 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9809 - val_loss: 0.2036 - val_accuracy: 0.9608 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4600339e-02 -8.7510487e-03 -1.2940705e-02] Sparsity at: 0.609564237415477 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1303 - accuracy: 0.9812 - val_loss: 0.1869 - val_accuracy: 0.9635 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8031683e-02 -1.7309848e-02 -9.7919749e-03] Sparsity at: 0.609564237415477 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9803 - val_loss: 0.1950 - val_accuracy: 0.9629 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.1298809e-03 -9.9104326e-03 -2.0670968e-03] Sparsity at: 0.609564237415477 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9816 - val_loss: 0.1865 - val_accuracy: 0.9661 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.1596423e-03 -9.3852123e-03 2.2791282e-03] Sparsity at: 0.609564237415477 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9821 - val_loss: 0.1914 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.6036219e-03 -7.3813237e-03 7.4062804e-03] Sparsity at: 0.609564237415477 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9804 - val_loss: 0.1770 - val_accuracy: 0.9685 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.4174972e-04 -1.0090131e-02 6.4816782e-03] Sparsity at: 0.609564237415477 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1292 - accuracy: 0.9813 - val_loss: 0.1863 - val_accuracy: 0.9641 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.7401869e-03 -1.5400289e-02 8.1983460e-03] Sparsity at: 0.609564237415477 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9806 - val_loss: 0.1790 - val_accuracy: 0.9695 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.9802860e-03 -2.4115946e-03 -8.4511638e-03] Sparsity at: 0.609564237415477 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9818 - val_loss: 0.1879 - val_accuracy: 0.9644 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.2199311e-03 -8.0453418e-03 1.0639328e-02] Sparsity at: 0.609564237415477 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9807 - val_loss: 0.1842 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.0330224e-03 -6.7035807e-03 -1.1171241e-03] Sparsity at: 0.609564237415477 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9813 - val_loss: 0.2048 - val_accuracy: 0.9587 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1704954e-02 -3.7501610e-03 -6.6961451e-03] Sparsity at: 0.609564237415477 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1306 - accuracy: 0.9810 - val_loss: 0.1840 - val_accuracy: 0.9651 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.7679686e-02 -8.5701933e-03 -7.8159301e-03] Sparsity at: 0.609564237415477 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1280 - accuracy: 0.9815 - val_loss: 0.1936 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5709355e-02 -1.5282063e-02 -1.1058649e-02] Sparsity at: 0.609564237415477 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1315 - accuracy: 0.9802 - val_loss: 0.2136 - val_accuracy: 0.9569 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9556612e-02 -1.2737467e-02 -9.5474264e-03] Sparsity at: 0.609564237415477 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9812 - val_loss: 0.1848 - val_accuracy: 0.9665 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5110875e-02 -1.4725075e-02 -1.3271408e-02] Sparsity at: 0.609564237415477 Epoch 142/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1317 - accuracy: 0.9805 - val_loss: 0.1909 - val_accuracy: 0.9633 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6393173e-02 -4.2465837e-03 -4.7134715e-03] Sparsity at: 0.609564237415477 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9823 - val_loss: 0.2004 - val_accuracy: 0.9626 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9536164e-02 -9.0902001e-03 -7.4065295e-03] Sparsity at: 0.609564237415477 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1302 - accuracy: 0.9805 - val_loss: 0.1898 - val_accuracy: 0.9639 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1743926e-02 -3.2926616e-03 -4.1449949e-04] Sparsity at: 0.609564237415477 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9799 - val_loss: 0.1891 - val_accuracy: 0.9642 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.2724106e-02 -7.9840105e-03 -4.0265890e-03] Sparsity at: 0.609564237415477 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9813 - val_loss: 0.1984 - val_accuracy: 0.9625 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1116811e-02 -2.6243231e-03 -8.8118725e-03] Sparsity at: 0.609564237415477 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9814 - val_loss: 0.1738 - val_accuracy: 0.9693 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0035450e-02 -5.5858968e-03 -4.2165699e-03] Sparsity at: 0.609564237415477 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1311 - accuracy: 0.9805 - val_loss: 0.1900 - val_accuracy: 0.9653 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1338446e-02 -1.0841298e-02 -4.2213215e-03] Sparsity at: 0.609564237415477 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9814 - val_loss: 0.1887 - val_accuracy: 0.9650 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.5217479e-03 -8.2926173e-03 -5.6361225e-03] Sparsity at: 0.609564237415477 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9813 - val_loss: 0.1778 - val_accuracy: 0.9665 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.5717590e-03 -1.2287069e-02 -1.3745189e-02] Sparsity at: 0.609564237415477 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 4.286602193017832e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.0006942666406397138 Thresholhold 4.403433062248983e-34 Using suggest threshold. Applying new mask Percentage zeros 0.49263334 tf.Tensor( [[1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] ... [0. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.046016209979620415 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 173s 12ms/step - loss: 0.1238 - accuracy: 0.9820 - val_loss: 0.1909 - val_accuracy: 0.9634 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5519885e-02 -9.4163073e-03 -5.7254005e-03] Sparsity at: 0.6261795642374155 Epoch 152/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1311 - accuracy: 0.9799 - val_loss: 0.1908 - val_accuracy: 0.9620 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5231688e-02 -3.2589317e-03 -5.2067703e-03] Sparsity at: 0.6261795642374155 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9814 - val_loss: 0.2210 - val_accuracy: 0.9554 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5006252e-02 9.2909153e-04 -1.1809093e-02] Sparsity at: 0.6261795642374155 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1307 - accuracy: 0.9803 - val_loss: 0.1957 - val_accuracy: 0.9618 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.7560357e-02 -4.0490050e-03 -4.7095772e-03] Sparsity at: 0.6261795642374155 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1271 - accuracy: 0.9813 - val_loss: 0.2034 - val_accuracy: 0.9592 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6040945e-02 -8.1000123e-03 -1.6245890e-02] Sparsity at: 0.6261795642374155 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9801 - val_loss: 0.2014 - val_accuracy: 0.9616 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.0611338e-02 -8.1535401e-03 -1.6556516e-02] Sparsity at: 0.6261795642374155 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9816 - val_loss: 0.1921 - val_accuracy: 0.9625 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4411822e-02 4.8476253e-03 -2.3491455e-02] Sparsity at: 0.6261795642374155 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9809 - val_loss: 0.1705 - val_accuracy: 0.9695 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.01361685e-02 2.61429953e-03 -1.35191055e-02] Sparsity at: 0.6261795642374155 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9809 - val_loss: 0.1960 - val_accuracy: 0.9618 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.4342676e-03 5.6050955e-03 -9.1069788e-03] Sparsity at: 0.6261795642374155 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9815 - val_loss: 0.1799 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.8714428e-03 -5.7288347e-04 -2.2965923e-02] Sparsity at: 0.6261795642374155 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1305 - accuracy: 0.9803 - val_loss: 0.1837 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6158879e-02 9.2642391e-03 -1.3545258e-02] Sparsity at: 0.6261795642374155 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9813 - val_loss: 0.1823 - val_accuracy: 0.9662 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.2168765e-03 3.2511912e-03 -1.1982426e-02] Sparsity at: 0.6261795642374155 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9806 - val_loss: 0.1931 - val_accuracy: 0.9643 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.3157080e-03 3.8613491e-03 -1.0941069e-02] Sparsity at: 0.6261795642374155 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9814 - val_loss: 0.1812 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.5654183e-03 -1.0532666e-03 -1.8705919e-02] Sparsity at: 0.6261795642374155 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1297 - accuracy: 0.9804 - val_loss: 0.2060 - val_accuracy: 0.9604 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.1441427e-03 1.8915758e-02 -1.4099847e-02] Sparsity at: 0.6261795642374155 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9813 - val_loss: 0.1806 - val_accuracy: 0.9667 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.2066663e-03 4.6999636e-03 -1.3829215e-03] Sparsity at: 0.6261795642374155 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1267 - accuracy: 0.9814 - val_loss: 0.1732 - val_accuracy: 0.9690 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.4425271e-03 3.0999986e-04 -3.3983466e-04] Sparsity at: 0.6261795642374155 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9819 - val_loss: 0.1776 - val_accuracy: 0.9673 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.4769482e-03 1.7992296e-04 -4.5202480e-04] Sparsity at: 0.6261795642374155 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9812 - val_loss: 0.1806 - val_accuracy: 0.9642 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.8535316e-04 1.6661637e-02 -4.6046562e-03] Sparsity at: 0.6261795642374155 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9802 - val_loss: 0.1748 - val_accuracy: 0.9679 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.3108786e-03 5.8515370e-03 -3.8334096e-03] Sparsity at: 0.6261795642374155 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1293 - accuracy: 0.9804 - val_loss: 0.1983 - val_accuracy: 0.9634 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.4222871e-03 8.6901216e-03 -7.2909202e-03] Sparsity at: 0.6261795642374155 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9799 - val_loss: 0.1992 - val_accuracy: 0.9613 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4708571e-03 8.6650234e-03 -1.3376051e-02] Sparsity at: 0.6261795642374155 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9822 - val_loss: 0.1830 - val_accuracy: 0.9656 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.9831867e-03 5.7156947e-03 -9.5424950e-03] Sparsity at: 0.62618332081142 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9808 - val_loss: 0.1952 - val_accuracy: 0.9633 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.7980115e-03 6.2589627e-03 -9.4819246e-03] Sparsity at: 0.62618332081142 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1277 - accuracy: 0.9810 - val_loss: 0.1999 - val_accuracy: 0.9595 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.2533810e-03 8.6162658e-03 -1.6495805e-02] Sparsity at: 0.62618332081142 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1282 - accuracy: 0.9804 - val_loss: 0.1872 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.5227003e-03 1.4301439e-02 -2.1752520e-02] Sparsity at: 0.62618332081142 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9814 - val_loss: 0.2079 - val_accuracy: 0.9577 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2052265e-03 1.8723499e-02 -1.7717831e-02] Sparsity at: 0.62618332081142 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1326 - accuracy: 0.9799 - val_loss: 0.2121 - val_accuracy: 0.9565 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.8268504e-03 2.0275349e-02 -1.1362681e-02] Sparsity at: 0.62618332081142 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9814 - val_loss: 0.1906 - val_accuracy: 0.9638 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.2024892e-03 2.5076723e-02 -1.3171487e-02] Sparsity at: 0.62618332081142 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1286 - accuracy: 0.9812 - val_loss: 0.1834 - val_accuracy: 0.9664 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.1486600e-03 2.2469476e-02 -1.9878138e-02] Sparsity at: 0.62618332081142 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1290 - accuracy: 0.9805 - val_loss: 0.1881 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.4482042e-03 1.6082549e-02 -1.5716419e-02] Sparsity at: 0.62618332081142 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1301 - accuracy: 0.9804 - val_loss: 0.2043 - val_accuracy: 0.9582 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.4179320e-03 1.4549229e-02 -2.4454787e-02] Sparsity at: 0.62618332081142 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9819 - val_loss: 0.2166 - val_accuracy: 0.9552 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.9060830e-03 1.5420340e-02 -2.3104817e-02] Sparsity at: 0.62618332081142 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9813 - val_loss: 0.1682 - val_accuracy: 0.9695 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.0513197e-04 1.4882351e-02 -2.1866664e-02] Sparsity at: 0.62618332081142 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9817 - val_loss: 0.1895 - val_accuracy: 0.9634 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.1987975e-03 1.2093968e-02 -3.0487541e-02] Sparsity at: 0.62618332081142 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9815 - val_loss: 0.1826 - val_accuracy: 0.9669 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.2185082e-03 1.6403848e-02 -2.5547681e-02] Sparsity at: 0.62618332081142 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9818 - val_loss: 0.2186 - val_accuracy: 0.9565 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1358790e-03 1.2848096e-02 -2.0030247e-02] Sparsity at: 0.62618332081142 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1266 - accuracy: 0.9808 - val_loss: 0.1914 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.6328462e-04 2.1268273e-02 -1.3198181e-02] Sparsity at: 0.62618332081142 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1265 - accuracy: 0.9812 - val_loss: 0.1843 - val_accuracy: 0.9655 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 3.41131189e-03 1.49679985e-02 -1.98633596e-02] Sparsity at: 0.62618332081142 Epoch 190/500 235/235 [==============================] - 3s 12ms/step - loss: 0.1239 - accuracy: 0.9818 - val_loss: 0.1741 - val_accuracy: 0.9702 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6764369e-03 2.2871250e-02 -1.8276498e-02] Sparsity at: 0.62618332081142 Epoch 191/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1268 - accuracy: 0.9810 - val_loss: 0.1949 - val_accuracy: 0.9609 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1533813e-03 2.5014626e-02 -1.1585600e-02] Sparsity at: 0.62618332081142 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1288 - accuracy: 0.9804 - val_loss: 0.2037 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.3686553e-04 2.8828869e-02 -1.5381028e-02] Sparsity at: 0.62618332081142 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1279 - accuracy: 0.9815 - val_loss: 0.1783 - val_accuracy: 0.9658 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.3907274e-04 1.9462006e-02 -1.0560043e-02] Sparsity at: 0.62618332081142 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9809 - val_loss: 0.1785 - val_accuracy: 0.9676 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.0319581e-03 9.5394058e-03 -2.2121089e-02] Sparsity at: 0.62618332081142 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1274 - accuracy: 0.9811 - val_loss: 0.2113 - val_accuracy: 0.9593 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.2561303e-02 1.4265097e-02 -1.9453447e-02] Sparsity at: 0.62618332081142 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1257 - accuracy: 0.9816 - val_loss: 0.1694 - val_accuracy: 0.9694 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9396411e-03 2.2716163e-02 -1.5544225e-02] Sparsity at: 0.62618332081142 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9805 - val_loss: 0.2196 - val_accuracy: 0.9552 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.8276341e-03 1.9026967e-02 -1.4434419e-02] Sparsity at: 0.62618332081142 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9818 - val_loss: 0.1949 - val_accuracy: 0.9624 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.4475386e-03 1.0460013e-02 -8.3177444e-03] Sparsity at: 0.62618332081142 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1257 - accuracy: 0.9809 - val_loss: 0.1899 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.0418394e-04 1.7433405e-02 -1.4085965e-02] Sparsity at: 0.62618332081142 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9808 - val_loss: 0.1827 - val_accuracy: 0.9641 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.6393368e-03 2.1374164e-02 -1.1409851e-02] Sparsity at: 0.62618332081142 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 5.425175404986997e-34 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.004295962923096641 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.49263334 tf.Tensor( [[1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] ... [0. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.05768549657616262 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 172s 12ms/step - loss: 0.1241 - accuracy: 0.9822 - val_loss: 0.1918 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.3261542e-03 1.9397806e-02 -9.1561060e-03] Sparsity at: 0.62618332081142 Epoch 202/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1289 - accuracy: 0.9804 - val_loss: 0.1930 - val_accuracy: 0.9619 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.0386746e-03 1.7317858e-02 -1.2023304e-02] Sparsity at: 0.62618332081142 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9817 - val_loss: 0.1944 - val_accuracy: 0.9608 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5512047e-02 1.0508889e-02 -8.6801825e-03] Sparsity at: 0.62618332081142 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1258 - accuracy: 0.9808 - val_loss: 0.1853 - val_accuracy: 0.9641 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.3857188e-03 1.4301135e-02 -1.3361643e-02] Sparsity at: 0.62618332081142 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1250 - accuracy: 0.9819 - val_loss: 0.2109 - val_accuracy: 0.9582 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.7737503e-03 5.8528553e-03 -1.1496742e-02] Sparsity at: 0.62618332081142 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9804 - val_loss: 0.2018 - val_accuracy: 0.9619 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.1104310e-03 1.0868287e-02 -7.7997963e-03] Sparsity at: 0.62618332081142 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9817 - val_loss: 0.1722 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.5278788e-03 4.3465844e-03 -6.4720227e-03] Sparsity at: 0.62618332081142 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9812 - val_loss: 0.2055 - val_accuracy: 0.9578 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.0254121e-04 7.5755953e-03 -9.6710622e-03] Sparsity at: 0.62618332081142 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9817 - val_loss: 0.2088 - val_accuracy: 0.9590 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.2469594e-03 3.0613991e-03 -4.3878113e-03] Sparsity at: 0.62618332081142 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9813 - val_loss: 0.1696 - val_accuracy: 0.9707 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.0439180e-03 1.2659993e-04 2.0115450e-03] Sparsity at: 0.62618332081142 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9815 - val_loss: 0.2015 - val_accuracy: 0.9590 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.8356230e-03 3.3753214e-03 -6.5748538e-03] Sparsity at: 0.62618332081142 Epoch 212/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9816 - val_loss: 0.2061 - val_accuracy: 0.9594 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.3798134e-03 1.0486190e-03 4.3362859e-03] Sparsity at: 0.62618332081142 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9820 - val_loss: 0.1762 - val_accuracy: 0.9681 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.17129332e-03 3.42493202e-03 -1.11116385e-02] Sparsity at: 0.62618332081142 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1273 - accuracy: 0.9805 - val_loss: 0.1959 - val_accuracy: 0.9623 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.5408812e-03 6.0132970e-03 -1.3358512e-02] Sparsity at: 0.62618332081142 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9814 - val_loss: 0.1880 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.6388051e-03 1.3041613e-02 -9.4592283e-03] Sparsity at: 0.62618332081142 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9820 - val_loss: 0.2253 - val_accuracy: 0.9557 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.6695828e-03 7.7870898e-03 -1.4348381e-02] Sparsity at: 0.62618332081142 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9811 - val_loss: 0.1836 - val_accuracy: 0.9674 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.8147050e-03 -1.6465879e-03 -8.8195875e-03] Sparsity at: 0.62618332081142 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9821 - val_loss: 0.1962 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.2033186e-04 -3.7436301e-05 -1.7933849e-02] Sparsity at: 0.62618332081142 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9822 - val_loss: 0.2013 - val_accuracy: 0.9624 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.2709750e-03 -2.6593131e-03 -1.7878983e-02] Sparsity at: 0.62618332081142 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9819 - val_loss: 0.1914 - val_accuracy: 0.9609 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.6397514e-04 9.8603533e-04 -1.7742151e-02] Sparsity at: 0.62618332081142 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9818 - val_loss: 0.2112 - val_accuracy: 0.9584 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.4710206e-03 -1.4860501e-03 -1.7573990e-02] Sparsity at: 0.62618332081142 Epoch 222/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1296 - accuracy: 0.9805 - val_loss: 0.2092 - val_accuracy: 0.9605 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4352625e-02 4.5264703e-03 -1.3222669e-02] Sparsity at: 0.62618332081142 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9824 - val_loss: 0.2125 - val_accuracy: 0.9581 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0451489e-02 4.1457117e-03 -4.7546444e-03] Sparsity at: 0.62618332081142 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9813 - val_loss: 0.2047 - val_accuracy: 0.9602 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0195646e-02 -3.2532644e-03 -1.0184709e-02] Sparsity at: 0.62618332081142 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9807 - val_loss: 0.1922 - val_accuracy: 0.9652 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.07967425e-02 1.56634522e-03 -8.91325437e-03] Sparsity at: 0.62618332081142 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9819 - val_loss: 0.2015 - val_accuracy: 0.9591 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.7093113e-03 -5.9368419e-03 5.8954568e-03] Sparsity at: 0.62618332081142 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9821 - val_loss: 0.2153 - val_accuracy: 0.9588 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0180203e-02 -2.8415869e-03 -7.4210786e-04] Sparsity at: 0.62618332081142 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9811 - val_loss: 0.2141 - val_accuracy: 0.9560 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.2034571e-03 -8.9245511e-04 -2.0890515e-02] Sparsity at: 0.62618332081142 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9821 - val_loss: 0.2160 - val_accuracy: 0.9571 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.6546267e-03 -7.3704490e-04 -2.2584060e-02] Sparsity at: 0.62618332081142 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9815 - val_loss: 0.1796 - val_accuracy: 0.9680 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.8043345e-03 -1.4098302e-02 -6.0914732e-03] Sparsity at: 0.62618332081142 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1278 - accuracy: 0.9811 - val_loss: 0.1780 - val_accuracy: 0.9672 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.5598390e-03 -1.6809989e-02 -2.4797142e-04] Sparsity at: 0.62618332081142 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9818 - val_loss: 0.2093 - val_accuracy: 0.9591 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6214581e-02 -1.3725693e-02 4.2767334e-03] Sparsity at: 0.62618332081142 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1275 - accuracy: 0.9808 - val_loss: 0.1850 - val_accuracy: 0.9665 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1406381e-02 -1.3063782e-03 7.0335576e-04] Sparsity at: 0.62618332081142 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9665 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.2443610e-03 5.2379095e-04 7.3178867e-03] Sparsity at: 0.62618332081142 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9810 - val_loss: 0.1929 - val_accuracy: 0.9635 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.9272211e-03 2.3080837e-03 -7.4494244e-03] Sparsity at: 0.62618332081142 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9817 - val_loss: 0.2098 - val_accuracy: 0.9595 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.03148995e-02 -3.30290012e-03 -3.74076422e-03] Sparsity at: 0.62618332081142 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1283 - accuracy: 0.9804 - val_loss: 0.1933 - val_accuracy: 0.9639 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1077386e-03 -3.5976588e-03 -2.0820191e-02] Sparsity at: 0.62618332081142 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9821 - val_loss: 0.1740 - val_accuracy: 0.9677 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.6579880e-03 -1.4505644e-03 -1.0685987e-02] Sparsity at: 0.62618332081142 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9822 - val_loss: 0.1929 - val_accuracy: 0.9615 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.7821847e-03 9.7606806e-03 -4.7083753e-03] Sparsity at: 0.62618332081142 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9810 - val_loss: 0.1888 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.8382086e-03 5.0148163e-03 -1.2554180e-02] Sparsity at: 0.62618332081142 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1281 - accuracy: 0.9802 - val_loss: 0.2015 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.1008080e-03 2.6247201e-03 -1.4206396e-02] Sparsity at: 0.62618332081142 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9808 - val_loss: 0.1818 - val_accuracy: 0.9671 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.3446617e-03 -1.7197767e-03 -1.6599132e-02] Sparsity at: 0.62618332081142 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9818 - val_loss: 0.1874 - val_accuracy: 0.9624 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.1932829e-04 1.9987579e-02 -1.2273332e-02] Sparsity at: 0.62618332081142 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9816 - val_loss: 0.1947 - val_accuracy: 0.9634 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1086864e-03 4.4933497e-03 -1.1754180e-03] Sparsity at: 0.62618332081142 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1247 - accuracy: 0.9814 - val_loss: 0.1871 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.6874881e-03 1.3728549e-03 -1.0242327e-02] Sparsity at: 0.62618332081142 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1258 - accuracy: 0.9808 - val_loss: 0.2025 - val_accuracy: 0.9603 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.3569218e-03 7.2018970e-03 -4.4557173e-03] Sparsity at: 0.62618332081142 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1237 - accuracy: 0.9812 - val_loss: 0.2012 - val_accuracy: 0.9596 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.0460682e-03 4.2725466e-03 -1.5053412e-02] Sparsity at: 0.62618332081142 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9807 - val_loss: 0.1716 - val_accuracy: 0.9690 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.9643390e-05 1.2978757e-03 -5.5835755e-03] Sparsity at: 0.62618332081142 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1276 - accuracy: 0.9805 - val_loss: 0.2177 - val_accuracy: 0.9573 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.3013643e-03 2.4729588e-03 -1.7711516e-02] Sparsity at: 0.62618332081142 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9813 - val_loss: 0.1934 - val_accuracy: 0.9619 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.8091870e-03 1.0649992e-02 -1.1276876e-02] Sparsity at: 0.62618332081142 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.0016325676645354698 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.013562465444595162 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.49263334 tf.Tensor( [[1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] ... [0. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.06700058851578738 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 176s 12ms/step - loss: 0.1241 - accuracy: 0.9813 - val_loss: 0.1865 - val_accuracy: 0.9643 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -1.36007834e-02 1.03300745e-02 -1.20017789e-02] Sparsity at: 0.62618332081142 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1227 - accuracy: 0.9819 - val_loss: 0.1952 - val_accuracy: 0.9616 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.0257553e-03 1.5583335e-02 -1.5734153e-02] Sparsity at: 0.62618332081142 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9813 - val_loss: 0.2208 - val_accuracy: 0.9537 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.0893583e-03 4.7535687e-03 -1.1814730e-02] Sparsity at: 0.62618332081142 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9814 - val_loss: 0.1919 - val_accuracy: 0.9623 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2156360e-04 -2.1419533e-04 -1.2901199e-02] Sparsity at: 0.62618332081142 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9814 - val_loss: 0.1900 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.3655390e-03 -1.0269799e-02 -5.2906433e-03] Sparsity at: 0.62618332081142 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9811 - val_loss: 0.2297 - val_accuracy: 0.9522 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.1479555e-03 -9.4493385e-03 -3.9317450e-03] Sparsity at: 0.62618332081142 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9819 - val_loss: 0.2418 - val_accuracy: 0.9508 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.9159542e-03 -9.0247201e-04 -1.9772795e-03] Sparsity at: 0.62618332081142 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1268 - accuracy: 0.9808 - val_loss: 0.1921 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.9747595e-03 -2.0622911e-03 1.4803831e-03] Sparsity at: 0.62618332081142 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9823 - val_loss: 0.1696 - val_accuracy: 0.9702 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9401954e-03 1.1356978e-03 9.1278035e-04] Sparsity at: 0.62618332081142 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9817 - val_loss: 0.1919 - val_accuracy: 0.9641 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.0165551e-04 -1.2073382e-03 1.7780502e-03] Sparsity at: 0.62618332081142 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1258 - accuracy: 0.9809 - val_loss: 0.2438 - val_accuracy: 0.9490 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.4383984e-04 1.4280566e-04 -3.7241151e-04] Sparsity at: 0.62618332081142 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9811 - val_loss: 0.2036 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0069177e-03 -1.9545346e-03 1.8310361e-03] Sparsity at: 0.62618332081142 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9815 - val_loss: 0.2373 - val_accuracy: 0.9519 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.3566113e-04 -5.2661251e-04 -2.6152132e-04] Sparsity at: 0.62618332081142 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9815 - val_loss: 0.2053 - val_accuracy: 0.9601 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6779182e-03 2.2994676e-03 7.4217940e-04] Sparsity at: 0.62618332081142 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9812 - val_loss: 0.2069 - val_accuracy: 0.9620 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3075378e-03 1.2018655e-04 1.6090585e-04] Sparsity at: 0.62618332081142 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9813 - val_loss: 0.1802 - val_accuracy: 0.9655 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.5354944e-04 -1.3296193e-03 -3.3454865e-04] Sparsity at: 0.62618332081142 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9821 - val_loss: 0.1782 - val_accuracy: 0.9666 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.8861600e-04 -3.3175960e-04 3.0204447e-03] Sparsity at: 0.62618332081142 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9815 - val_loss: 0.2057 - val_accuracy: 0.9570 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.6831126e-04 -7.8248733e-04 3.2432235e-03] Sparsity at: 0.62618332081142 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9818 - val_loss: 0.2239 - val_accuracy: 0.9524 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.2685211e-04 1.1002128e-03 7.9990551e-04] Sparsity at: 0.62618332081142 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9819 - val_loss: 0.2065 - val_accuracy: 0.9591 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5090625e-03 -2.1318310e-04 -4.4188061e-04] Sparsity at: 0.62618332081142 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1272 - accuracy: 0.9802 - val_loss: 0.2002 - val_accuracy: 0.9639 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.9291797e-04 7.9376285e-04 6.5323984e-04] Sparsity at: 0.62618332081142 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1244 - accuracy: 0.9811 - val_loss: 0.1835 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0484312e-04 1.2608478e-03 4.6374853e-04] Sparsity at: 0.62618332081142 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9818 - val_loss: 0.2055 - val_accuracy: 0.9600 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.0306240e-04 5.5919634e-04 1.4188844e-03] Sparsity at: 0.62618332081142 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1285 - accuracy: 0.9800 - val_loss: 0.1713 - val_accuracy: 0.9679 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.2447572e-05 7.8261219e-05 6.5835548e-04] Sparsity at: 0.62618332081142 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9818 - val_loss: 0.2013 - val_accuracy: 0.9594 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.0110240e-05 -3.3821564e-04 -2.7876886e-05] Sparsity at: 0.62618332081142 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9815 - val_loss: 0.2113 - val_accuracy: 0.9566 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5102809e-04 7.7305587e-05 -4.2653430e-04] Sparsity at: 0.62618332081142 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1243 - accuracy: 0.9811 - val_loss: 0.1948 - val_accuracy: 0.9615 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -1.03467784e-04 4.70967789e-05 1.82280797e-04] Sparsity at: 0.62618332081142 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9807 - val_loss: 0.1754 - val_accuracy: 0.9696 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4890644e-05 -1.8937873e-05 -1.1591794e-05] Sparsity at: 0.62618332081142 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9808 - val_loss: 0.1851 - val_accuracy: 0.9651 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8048511e-05 -1.5665177e-05 -1.0994325e-05] Sparsity at: 0.62618332081142 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9811 - val_loss: 0.1812 - val_accuracy: 0.9650 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.6204299e-06 -8.9500300e-06 8.7827866e-06] Sparsity at: 0.62618332081142 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9812 - val_loss: 0.1878 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.3256264e-06 -4.4084408e-07 -9.7197500e-08] Sparsity at: 0.62618332081142 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9817 - val_loss: 0.1859 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7798621e-07 2.3471124e-08 2.6843422e-08] Sparsity at: 0.62618332081142 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9821 - val_loss: 0.1964 - val_accuracy: 0.9595 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.6548379e-10 6.4321561e-09 -4.5154702e-09] Sparsity at: 0.62618332081142 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9822 - val_loss: 0.1944 - val_accuracy: 0.9624 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.2624654e-12 -6.0257927e-12 1.5398592e-11] Sparsity at: 0.62618332081142 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9803 - val_loss: 0.1751 - val_accuracy: 0.9680 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 4.26731201e-14 -1.21835415e-14 -3.95867556e-14] Sparsity at: 0.62618332081142 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9817 - val_loss: 0.2062 - val_accuracy: 0.9595 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.3930374e-18 2.0098957e-17 -2.9837325e-17] Sparsity at: 0.62618332081142 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9816 - val_loss: 0.1960 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.3413001e-20 -5.7890720e-20 -8.1707568e-21] Sparsity at: 0.62618332081142 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9817 - val_loss: 0.1897 - val_accuracy: 0.9653 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.4285402e-22 6.7802552e-22 5.0615827e-23] Sparsity at: 0.62618332081142 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9820 - val_loss: 0.1945 - val_accuracy: 0.9633 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.7099418e-25 6.5863895e-25 5.8208823e-25] Sparsity at: 0.62618332081142 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9806 - val_loss: 0.1957 - val_accuracy: 0.9623 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.0947868e-26 -1.0519708e-25 3.2940696e-26] Sparsity at: 0.62618332081142 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1263 - accuracy: 0.9807 - val_loss: 0.1755 - val_accuracy: 0.9666 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.7800488e-27 1.0427074e-26 -2.4608433e-27] Sparsity at: 0.62618332081142 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9819 - val_loss: 0.1993 - val_accuracy: 0.9590 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.3013629e-28 1.3340842e-27 -4.5087233e-28] Sparsity at: 0.62618332081142 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9820 - val_loss: 0.1691 - val_accuracy: 0.9698 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.5594622e-29 -1.5813824e-27 1.6074361e-27] Sparsity at: 0.62618332081142 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9817 - val_loss: 0.1903 - val_accuracy: 0.9622 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.0317882e-28 -1.9824429e-27 2.2826260e-27] Sparsity at: 0.6261870773854245 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9822 - val_loss: 0.1816 - val_accuracy: 0.9678 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.6416768e-27 5.7386118e-27 -3.5225436e-27] Sparsity at: 0.6261870773854245 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1259 - accuracy: 0.9806 - val_loss: 0.2033 - val_accuracy: 0.9592 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.6979961e-25 1.3025396e-24 -6.7200398e-25] Sparsity at: 0.6261870773854245 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9815 - val_loss: 0.1916 - val_accuracy: 0.9613 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6373226e-24 4.4082466e-24 -5.5904778e-24] Sparsity at: 0.6261870773854245 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9822 - val_loss: 0.1969 - val_accuracy: 0.9604 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.0260517e-22 8.0440275e-22 -2.4066397e-22] Sparsity at: 0.6261870773854245 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9816 - val_loss: 0.1950 - val_accuracy: 0.9591 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 6.18865919e-21 -1.49034989e-20 1.03465795e-20] Sparsity at: 0.6261870773854245 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9815 - val_loss: 0.1890 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3620004e-17 2.5221280e-17 6.1532357e-18] Sparsity at: 0.6261870773854245 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.008926816751783417 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.023912666991317888 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.49263334 tf.Tensor( [[1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] ... [0. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.0773397535943765 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 176s 12ms/step - loss: 0.1219 - accuracy: 0.9816 - val_loss: 0.1964 - val_accuracy: 0.9619 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.2738165e-15 7.7147939e-15 3.9899339e-16] Sparsity at: 0.6261870773854245 Epoch 302/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1211 - accuracy: 0.9822 - val_loss: 0.1736 - val_accuracy: 0.9659 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8041202e-11 -1.5425322e-10 5.8118906e-11] Sparsity at: 0.6261870773854245 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9807 - val_loss: 0.2200 - val_accuracy: 0.9552 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.9847009e-06 4.7622884e-06 -8.1369199e-06] Sparsity at: 0.6261870773854245 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9803 - val_loss: 0.1820 - val_accuracy: 0.9672 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8025470e-03 3.3352962e-03 -1.7578114e-03] Sparsity at: 0.6261870773854245 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1252 - accuracy: 0.9806 - val_loss: 0.1891 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.6869064e-05 1.3899141e-03 -7.2385202e-04] Sparsity at: 0.6261870773854245 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9818 - val_loss: 0.1860 - val_accuracy: 0.9641 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.9526684e-06 -2.8877095e-05 1.0870221e-04] Sparsity at: 0.6261870773854245 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9820 - val_loss: 0.2222 - val_accuracy: 0.9552 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.2533588e-04 1.0467250e-04 -2.1782349e-04] Sparsity at: 0.6261870773854245 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9812 - val_loss: 0.2039 - val_accuracy: 0.9592 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.8660154e-04 -7.1801146e-04 5.0761353e-04] Sparsity at: 0.6261870773854245 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9814 - val_loss: 0.1837 - val_accuracy: 0.9653 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.9951999e-04 -1.9011059e-03 6.7118858e-04] Sparsity at: 0.6261870773854245 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9821 - val_loss: 0.1917 - val_accuracy: 0.9637 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.2753360e-06 -1.0912327e-05 -6.7855799e-06] Sparsity at: 0.6261870773854245 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9821 - val_loss: 0.1925 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.8384658e-06 1.3288690e-05 -1.5786109e-05] Sparsity at: 0.6261870773854245 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9818 - val_loss: 0.2087 - val_accuracy: 0.9590 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.8873877e-05 7.4357486e-05 -1.3246863e-06] Sparsity at: 0.6261870773854245 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9810 - val_loss: 0.1899 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.5693011e-03 7.6196087e-04 -1.4167209e-03] Sparsity at: 0.6261870773854245 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9826 - val_loss: 0.1930 - val_accuracy: 0.9611 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.4455798e-05 -3.3750996e-04 3.1208617e-04] Sparsity at: 0.6261870773854245 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9825 - val_loss: 0.1884 - val_accuracy: 0.9624 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.3372552e-06 4.4128861e-05 -3.8046983e-05] Sparsity at: 0.6261870773854245 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9810 - val_loss: 0.2202 - val_accuracy: 0.9569 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.8588313e-08 1.1908221e-06 -1.2511344e-07] Sparsity at: 0.6261870773854245 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9812 - val_loss: 0.1892 - val_accuracy: 0.9635 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4118820e-06 -1.8683791e-06 -4.2766817e-07] Sparsity at: 0.6261870773854245 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9821 - val_loss: 0.1835 - val_accuracy: 0.9649 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6742255e-05 -3.9471192e-06 -2.5228610e-06] Sparsity at: 0.6261870773854245 Epoch 319/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1254 - accuracy: 0.9808 - val_loss: 0.1833 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1417631e-03 -6.8913342e-04 -1.1971162e-03] Sparsity at: 0.6261870773854245 Epoch 320/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1244 - accuracy: 0.9813 - val_loss: 0.1773 - val_accuracy: 0.9676 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.7696171e-06 3.5594916e-04 -4.7122332e-04] Sparsity at: 0.6261870773854245 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9818 - val_loss: 0.2454 - val_accuracy: 0.9486 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.4351084e-05 3.4836259e-05 -5.7605641e-05] Sparsity at: 0.6261870773854245 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9807 - val_loss: 0.1810 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8291770e-07 4.4326944e-07 -3.3495209e-07] Sparsity at: 0.6261870773854245 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9801 - val_loss: 0.2066 - val_accuracy: 0.9598 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.8536533e-10 2.4985121e-09 -1.5308512e-09] Sparsity at: 0.6261870773854245 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9816 - val_loss: 0.2076 - val_accuracy: 0.9577 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.9931994e-13 -9.4603332e-11 8.7120450e-11] Sparsity at: 0.6261870773854245 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9822 - val_loss: 0.2154 - val_accuracy: 0.9569 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.08697414e-13 3.74635707e-13 -2.58976461e-13] Sparsity at: 0.6261870773854245 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9815 - val_loss: 0.1798 - val_accuracy: 0.9653 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.8934929e-15 2.5194495e-13 2.5717210e-13] Sparsity at: 0.6261870773854245 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9804 - val_loss: 0.1709 - val_accuracy: 0.9686 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.6717186e-13 1.3715066e-12 -3.9171945e-13] Sparsity at: 0.6261870773854245 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9812 - val_loss: 0.2190 - val_accuracy: 0.9571 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0156342e-11 3.4194644e-11 3.1782414e-11] Sparsity at: 0.6261870773854245 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9812 - val_loss: 0.1857 - val_accuracy: 0.9641 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.5535848e-11 -3.6869674e-11 7.9269855e-12] Sparsity at: 0.6261870773854245 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9814 - val_loss: 0.1725 - val_accuracy: 0.9685 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.2052292e-10 1.1079103e-09 2.6166798e-09] Sparsity at: 0.6261870773854245 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1171 - accuracy: 0.9828 - val_loss: 0.2078 - val_accuracy: 0.9574 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1187367e-07 1.6515718e-07 6.1671290e-07] Sparsity at: 0.6261870773854245 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9816 - val_loss: 0.1882 - val_accuracy: 0.9628 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.1856587e-04 -1.5323196e-05 1.3223820e-04] Sparsity at: 0.6261870773854245 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1206 - accuracy: 0.9821 - val_loss: 0.1959 - val_accuracy: 0.9634 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.4338312e-04 -3.4625272e-04 -1.6054850e-03] Sparsity at: 0.6261870773854245 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9813 - val_loss: 0.1919 - val_accuracy: 0.9603 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.7541933e-04 6.4889708e-04 -3.8442525e-04] Sparsity at: 0.6261870773854245 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9821 - val_loss: 0.1898 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.5082167e-06 -8.2695269e-06 6.1761648e-06] Sparsity at: 0.6261870773854245 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9821 - val_loss: 0.1922 - val_accuracy: 0.9616 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.8460225e-08 3.2468051e-08 -5.9829389e-08] Sparsity at: 0.6261870773854245 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9818 - val_loss: 0.2019 - val_accuracy: 0.9614 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9019851e-08 -2.5484098e-07 2.4295900e-07] Sparsity at: 0.6261870773854245 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9814 - val_loss: 0.2220 - val_accuracy: 0.9523 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.1292232e-08 -3.5855411e-08 2.5434628e-09] Sparsity at: 0.6261870773854245 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9826 - val_loss: 0.2040 - val_accuracy: 0.9581 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.9230287e-08 -2.5214524e-07 1.5871755e-07] Sparsity at: 0.6261870773854245 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9819 - val_loss: 0.1960 - val_accuracy: 0.9630 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.9071865e-06 -6.4912333e-06 5.8411615e-08] Sparsity at: 0.6261870773854245 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9811 - val_loss: 0.1954 - val_accuracy: 0.9626 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.5610355e-04 -1.8952641e-03 9.0429024e-04] Sparsity at: 0.6261870773854245 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9816 - val_loss: 0.1748 - val_accuracy: 0.9687 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9881116e-04 3.2671183e-04 -9.9432786e-05] Sparsity at: 0.6261870773854245 Epoch 343/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1195 - accuracy: 0.9822 - val_loss: 0.1826 - val_accuracy: 0.9668 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.3091468e-07 -1.6314901e-06 4.5388042e-06] Sparsity at: 0.6261870773854245 Epoch 344/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1203 - accuracy: 0.9816 - val_loss: 0.1923 - val_accuracy: 0.9632 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.4757637e-08 6.1931885e-08 -1.8621314e-08] Sparsity at: 0.6261870773854245 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9816 - val_loss: 0.2053 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9891685e-08 2.3257110e-08 9.2476640e-09] Sparsity at: 0.6261870773854245 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9820 - val_loss: 0.1973 - val_accuracy: 0.9633 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.0301445e-08 -9.2350859e-08 1.1424606e-07] Sparsity at: 0.6261870773854245 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9824 - val_loss: 0.1960 - val_accuracy: 0.9614 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.9290087e-07 -1.5072590e-06 -8.1361600e-07] Sparsity at: 0.6261870773854245 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9809 - val_loss: 0.1741 - val_accuracy: 0.9674 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.6777303e-06 -3.0742922e-06 2.2224619e-06] Sparsity at: 0.6261870773854245 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9825 - val_loss: 0.1826 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.8931043e-05 -4.2349572e-04 6.4892709e-05] Sparsity at: 0.6261870773854245 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9805 - val_loss: 0.1970 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.6876666e-03 -1.1000779e-02 3.8385950e-03] Sparsity at: 0.6261870773854245 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.016510036329997257 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.032463779063679254 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.49263334 tf.Tensor( [[1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] ... [0. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.08349714231515382 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 241s 12ms/step - loss: 0.1226 - accuracy: 0.9812 - val_loss: 0.2095 - val_accuracy: 0.9587 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.9531977e-04 5.3317996e-04 -3.2300997e-04] Sparsity at: 0.6261870773854245 Epoch 352/500 235/235 [==============================] - 3s 12ms/step - loss: 0.1209 - accuracy: 0.9819 - val_loss: 0.1772 - val_accuracy: 0.9678 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.3880455e-07 1.1467820e-06 -5.6270508e-08] Sparsity at: 0.6261870773854245 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9808 - val_loss: 0.1756 - val_accuracy: 0.9697 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0081194e-08 -6.2615612e-08 3.6974178e-08] Sparsity at: 0.6261870773854245 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9823 - val_loss: 0.1855 - val_accuracy: 0.9662 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.7609330e-09 6.8751644e-09 -5.3175215e-09] Sparsity at: 0.6261870773854245 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9819 - val_loss: 0.1957 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4816058e-10 -1.1987249e-09 5.2496668e-10] Sparsity at: 0.6261870773854245 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9813 - val_loss: 0.1974 - val_accuracy: 0.9618 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1932011e-10 -2.5540496e-09 -4.0782278e-10] Sparsity at: 0.6261870773854245 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1185 - accuracy: 0.9824 - val_loss: 0.1844 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3855547e-08 -3.5656178e-08 7.5758670e-09] Sparsity at: 0.6261870773854245 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9823 - val_loss: 0.1996 - val_accuracy: 0.9593 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3251099e-07 -8.6960206e-08 1.3410200e-07] Sparsity at: 0.6261870773854245 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9811 - val_loss: 0.2020 - val_accuracy: 0.9597 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.0295259e-06 -2.7657692e-05 1.8722087e-05] Sparsity at: 0.6261870773854245 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9813 - val_loss: 0.1955 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6231709e-03 -4.3094899e-03 2.4324767e-03] Sparsity at: 0.6261870773854245 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9815 - val_loss: 0.1947 - val_accuracy: 0.9636 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.5039894e-05 -8.6653876e-05 -3.9276289e-05] Sparsity at: 0.6261870773854245 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9816 - val_loss: 0.1873 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.3990757e-06 -2.6400239e-05 8.3225477e-06] Sparsity at: 0.6261870773854245 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1212 - accuracy: 0.9819 - val_loss: 0.1920 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.3987320e-07 -1.0562367e-06 2.5708218e-06] Sparsity at: 0.6261870773854245 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9825 - val_loss: 0.2058 - val_accuracy: 0.9574 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.4820805e-08 -1.2265305e-07 6.9001878e-08] Sparsity at: 0.6261870773854245 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9811 - val_loss: 0.1809 - val_accuracy: 0.9666 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1365485e-08 -5.8898596e-08 4.0429018e-08] Sparsity at: 0.6261870773854245 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1178 - accuracy: 0.9829 - val_loss: 0.1827 - val_accuracy: 0.9651 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.5186194e-09 -7.3361397e-09 1.5544590e-08] Sparsity at: 0.6261870773854245 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9825 - val_loss: 0.1795 - val_accuracy: 0.9662 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1708516e-07 -2.6395290e-07 5.5364168e-08] Sparsity at: 0.6261870773854245 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9815 - val_loss: 0.1993 - val_accuracy: 0.9609 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.3384391e-07 1.6957705e-07 2.0388266e-07] Sparsity at: 0.6261870773854245 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9818 - val_loss: 0.2149 - val_accuracy: 0.9578 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.0046033e-05 -1.1024142e-05 2.1142159e-06] Sparsity at: 0.6261870773854245 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9819 - val_loss: 0.1875 - val_accuracy: 0.9642 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.5015073e-03 -9.2926165e-03 3.6455139e-03] Sparsity at: 0.6261870773854245 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9817 - val_loss: 0.2097 - val_accuracy: 0.9577 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9895568e-05 8.5564607e-05 2.4726311e-05] Sparsity at: 0.6261870773854245 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9821 - val_loss: 0.2126 - val_accuracy: 0.9561 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.4960627e-07 -9.0040328e-07 7.1273462e-07] Sparsity at: 0.6261870773854245 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9812 - val_loss: 0.1928 - val_accuracy: 0.9658 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.5436972e-09 9.1328197e-08 -3.2596944e-08] Sparsity at: 0.6261870773854245 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9812 - val_loss: 0.2106 - val_accuracy: 0.9583 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7077399e-10 4.0826223e-10 1.3260228e-09] Sparsity at: 0.6261870773854245 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9806 - val_loss: 0.1961 - val_accuracy: 0.9640 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.7743569e-11 8.0098629e-11 -2.5052777e-10] Sparsity at: 0.6261870773854245 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9831 - val_loss: 0.2043 - val_accuracy: 0.9585 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.9130689e-11 1.9376868e-11 -2.1291011e-11] Sparsity at: 0.6261870773854245 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9807 - val_loss: 0.1999 - val_accuracy: 0.9617 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4929843e-12 -1.4658622e-10 8.0232994e-11] Sparsity at: 0.6261870773854245 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9819 - val_loss: 0.1957 - val_accuracy: 0.9643 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0127725e-09 -3.4216001e-09 1.5104137e-09] Sparsity at: 0.6261870773854245 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9825 - val_loss: 0.1907 - val_accuracy: 0.9632 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.6618918e-08 -6.5728564e-08 -7.4243488e-08] Sparsity at: 0.6261870773854245 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9819 - val_loss: 0.1924 - val_accuracy: 0.9612 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.4558652e-08 -4.2482547e-08 2.1463874e-08] Sparsity at: 0.6261870773854245 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9819 - val_loss: 0.1856 - val_accuracy: 0.9666 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.4102834e-04 -6.7503972e-04 3.8109746e-04] Sparsity at: 0.6261870773854245 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9799 - val_loss: 0.1855 - val_accuracy: 0.9666 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.6282367e-04 2.9312423e-04 -7.3925342e-04] Sparsity at: 0.6261870773854245 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9812 - val_loss: 0.2045 - val_accuracy: 0.9600 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2994341e-04 -3.6185217e-04 8.5605127e-05] Sparsity at: 0.6261870773854245 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1217 - accuracy: 0.9818 - val_loss: 0.1726 - val_accuracy: 0.9682 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.0336897e-05 2.0385464e-04 -1.9895655e-04] Sparsity at: 0.6261870773854245 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9800 - val_loss: 0.1881 - val_accuracy: 0.9666 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.4276200e-05 -2.9844925e-04 5.5596058e-05] Sparsity at: 0.6261870773854245 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9827 - val_loss: 0.1989 - val_accuracy: 0.9621 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.0085348e-04 -4.0005415e-04 4.6760647e-04] Sparsity at: 0.6261870773854245 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9825 - val_loss: 0.1938 - val_accuracy: 0.9642 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.4465741e-05 -7.3872827e-05 3.5320216e-05] Sparsity at: 0.6261870773854245 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9818 - val_loss: 0.2511 - val_accuracy: 0.9441 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2384003e-04 1.0775219e-04 2.7143807e-04] Sparsity at: 0.6261870773854245 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9817 - val_loss: 0.1982 - val_accuracy: 0.9615 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.4222763e-05 -3.5958691e-04 3.6389907e-04] Sparsity at: 0.6261870773854245 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1251 - accuracy: 0.9806 - val_loss: 0.1852 - val_accuracy: 0.9644 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.7730907e-05 -1.7828708e-04 9.0911381e-05] Sparsity at: 0.6261870773854245 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9828 - val_loss: 0.1822 - val_accuracy: 0.9658 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.0261314e-04 -3.9816654e-04 2.7230865e-04] Sparsity at: 0.6261870773854245 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9814 - val_loss: 0.1850 - val_accuracy: 0.9642 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3339235e-05 1.1734862e-04 2.0584546e-03] Sparsity at: 0.6261870773854245 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1226 - accuracy: 0.9812 - val_loss: 0.1864 - val_accuracy: 0.9632 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.5511265e-06 -8.3721188e-06 1.7499680e-05] Sparsity at: 0.6261870773854245 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1165 - accuracy: 0.9836 - val_loss: 0.1970 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.9954527e-09 -1.4136415e-07 1.3934643e-07] Sparsity at: 0.6261870773854245 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1255 - accuracy: 0.9807 - val_loss: 0.1876 - val_accuracy: 0.9650 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.0663334e-08 -1.5606945e-07 8.0859479e-08] Sparsity at: 0.6261870773854245 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9826 - val_loss: 0.1763 - val_accuracy: 0.9676 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.9010654e-08 -2.7427653e-08 -5.3518118e-10] Sparsity at: 0.6261870773854245 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9810 - val_loss: 0.2041 - val_accuracy: 0.9600 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2713302e-07 1.1743823e-06 1.2525702e-06] Sparsity at: 0.6261870773854245 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9817 - val_loss: 0.1986 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.6987228e-06 -3.1854102e-05 1.8687613e-05] Sparsity at: 0.6261870773854245 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1195 - accuracy: 0.9822 - val_loss: 0.2027 - val_accuracy: 0.9611 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.6813243e-04 -3.5479618e-03 1.7110492e-03] Sparsity at: 0.6261870773854245 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9817 - val_loss: 0.1980 - val_accuracy: 0.9611 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.0105329e-05 -3.3844609e-04 6.6571374e-05] Sparsity at: 0.6261870773854245 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.020474764355928432 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.6458588 tf.Tensor( [[1. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.] [0. 0. 0. ... 0. 1. 0.] ... [0. 1. 1. ... 0. 0. 0.] [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 0.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.03751815616762588 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.49263334 tf.Tensor( [[1. 0. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.] ... [0. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 0. 1. 1.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.0868303992841506 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.004 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 0. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 0. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 235s 12ms/step - loss: 0.1243 - accuracy: 0.9813 - val_loss: 0.1925 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3655852e-07 -1.1837324e-06 6.9311528e-07] Sparsity at: 0.6261870773854245 Epoch 402/500 235/235 [==============================] - 3s 13ms/step - loss: 0.1209 - accuracy: 0.9821 - val_loss: 0.2176 - val_accuracy: 0.9577 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.0531508e-09 3.0644582e-09 3.0888985e-09] Sparsity at: 0.6261870773854245 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1224 - accuracy: 0.9807 - val_loss: 0.1914 - val_accuracy: 0.9630 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.8350095e-11 1.3681974e-10 -5.1871882e-11] Sparsity at: 0.6261870773854245 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1208 - accuracy: 0.9824 - val_loss: 0.1891 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3656733e-11 -8.9092053e-11 1.7147960e-11] Sparsity at: 0.6261870773854245 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1210 - accuracy: 0.9822 - val_loss: 0.1944 - val_accuracy: 0.9637 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.3320718e-14 2.9360889e-12 -1.7571143e-12] Sparsity at: 0.6261870773854245 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9815 - val_loss: 0.1962 - val_accuracy: 0.9622 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.4582606e-12 3.5340411e-12 -6.0484907e-12] Sparsity at: 0.6261870773854245 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9815 - val_loss: 0.1683 - val_accuracy: 0.9692 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.4846724e-12 4.1195992e-12 1.6674201e-12] Sparsity at: 0.6261870773854245 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9831 - val_loss: 0.2182 - val_accuracy: 0.9580 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8965916e-11 3.1729796e-11 -1.2170326e-11] Sparsity at: 0.6261870773854245 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9814 - val_loss: 0.1807 - val_accuracy: 0.9667 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.2945463e-10 4.5110888e-09 3.5747381e-09] Sparsity at: 0.6261870773854245 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9824 - val_loss: 0.2036 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.1630442e-08 8.0722259e-08 -6.0818820e-08] Sparsity at: 0.6261870773854245 Epoch 411/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9804 - val_loss: 0.1998 - val_accuracy: 0.9600 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8610057e-04 5.0290907e-04 -4.6582933e-05] Sparsity at: 0.6261870773854245 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9826 - val_loss: 0.1949 - val_accuracy: 0.9629 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8485835e-03 4.4665267e-03 -6.3190900e-04] Sparsity at: 0.6261870773854245 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9826 - val_loss: 0.1962 - val_accuracy: 0.9650 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -7.4259791e-05 -1.2143866e-04 1.2779946e-04] Sparsity at: 0.6261870773854245 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9822 - val_loss: 0.2031 - val_accuracy: 0.9602 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.7616647e-05 1.8563025e-05 -2.4891418e-05] Sparsity at: 0.6261870773854245 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9811 - val_loss: 0.2153 - val_accuracy: 0.9553 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.5368364e-07 -1.6331296e-06 2.8643328e-07] Sparsity at: 0.6261870773854245 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9826 - val_loss: 0.1788 - val_accuracy: 0.9688 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4618365e-07 3.0221217e-06 -1.2266655e-06] Sparsity at: 0.6261870773854245 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9819 - val_loss: 0.1960 - val_accuracy: 0.9617 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.1497204e-08 -1.0743433e-06 3.1983700e-07] Sparsity at: 0.6261870773854245 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1239 - accuracy: 0.9812 - val_loss: 0.2122 - val_accuracy: 0.9587 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.1215657e-07 5.8115938e-07 -1.3543797e-09] Sparsity at: 0.6261870773854245 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9812 - val_loss: 0.2161 - val_accuracy: 0.9568 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.1358805e-06 -8.3415325e-05 1.3039102e-05] Sparsity at: 0.6261870773854245 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1215 - accuracy: 0.9813 - val_loss: 0.2255 - val_accuracy: 0.9534 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.6890911e-04 -2.0496054e-03 7.9449115e-04] Sparsity at: 0.6261870773854245 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9810 - val_loss: 0.2052 - val_accuracy: 0.9589 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.9004872e-04 -3.3907319e-04 3.8326718e-04] Sparsity at: 0.6261870773854245 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9824 - val_loss: 0.1842 - val_accuracy: 0.9642 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.1958482e-06 -3.4224431e-05 1.6701235e-06] Sparsity at: 0.6261870773854245 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1175 - accuracy: 0.9826 - val_loss: 0.1986 - val_accuracy: 0.9618 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.5034656e-05 -1.3027662e-04 1.4647128e-04] Sparsity at: 0.6261870773854245 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1245 - accuracy: 0.9807 - val_loss: 0.1913 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.8158928e-04 -9.0000255e-04 -8.2751818e-04] Sparsity at: 0.6261870773854245 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9817 - val_loss: 0.1932 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6023814e-03 3.0240847e-03 -1.6541183e-03] Sparsity at: 0.6261870773854245 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9796 - val_loss: 0.1932 - val_accuracy: 0.9638 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.9944021e-05 -1.0959603e-04 6.0733058e-05] Sparsity at: 0.6261870773854245 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9826 - val_loss: 0.1974 - val_accuracy: 0.9630 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.6153046e-07 -3.5220025e-06 1.3992014e-06] Sparsity at: 0.6261870773854245 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9815 - val_loss: 0.1881 - val_accuracy: 0.9645 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.1976826e-08 -6.4389283e-08 -4.6575477e-09] Sparsity at: 0.6261870773854245 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9816 - val_loss: 0.1902 - val_accuracy: 0.9629 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.8809073e-09 2.9563136e-08 -2.8069392e-08] Sparsity at: 0.6261870773854245 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9819 - val_loss: 0.1915 - val_accuracy: 0.9644 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.0323533e-08 -3.5640181e-08 2.7647818e-08] Sparsity at: 0.6261870773854245 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9819 - val_loss: 0.2474 - val_accuracy: 0.9507 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3004569e-07 -2.5500796e-08 2.2905246e-07] Sparsity at: 0.6261870773854245 Epoch 432/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1231 - accuracy: 0.9811 - val_loss: 0.2023 - val_accuracy: 0.9606 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -1.06880634e-07 -3.53378340e-07 -4.57105841e-07] Sparsity at: 0.6261870773854245 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9829 - val_loss: 0.2089 - val_accuracy: 0.9581 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6122502e-05 -2.5339850e-05 1.4253271e-05] Sparsity at: 0.6261870773854245 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9822 - val_loss: 0.2101 - val_accuracy: 0.9605 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.8770240e-04 1.2434192e-04 1.1543169e-03] Sparsity at: 0.6261870773854245 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9804 - val_loss: 0.2040 - val_accuracy: 0.9603 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.6606188e-04 1.4322897e-02 -1.4225943e-02] Sparsity at: 0.6261870773854245 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9819 - val_loss: 0.1798 - val_accuracy: 0.9664 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.7763979e-04 9.0713607e-04 -9.4500756e-05] Sparsity at: 0.6261870773854245 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9811 - val_loss: 0.2078 - val_accuracy: 0.9600 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.1781408e-05 5.8634032e-05 -2.7287941e-05] Sparsity at: 0.6261870773854245 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9819 - val_loss: 0.2033 - val_accuracy: 0.9617 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.6274080e-07 -2.5273908e-07 4.9004385e-08] Sparsity at: 0.6261870773854245 Epoch 439/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1209 - accuracy: 0.9823 - val_loss: 0.2109 - val_accuracy: 0.9561 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1140786e-09 2.1387829e-09 1.8099465e-10] Sparsity at: 0.6261870773854245 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9821 - val_loss: 0.2195 - val_accuracy: 0.9566 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.7802224e-10 -2.4276109e-10 3.9868029e-11] Sparsity at: 0.6261870773854245 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9816 - val_loss: 0.1966 - val_accuracy: 0.9620 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.7571757e-12 -8.7592059e-13 -2.9176485e-12] Sparsity at: 0.6261870773854245 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1233 - accuracy: 0.9812 - val_loss: 0.2242 - val_accuracy: 0.9546 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.1258848e-13 5.1406069e-13 -8.2724609e-13] Sparsity at: 0.6261870773854245 Epoch 443/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1230 - accuracy: 0.9812 - val_loss: 0.2372 - val_accuracy: 0.9500 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.1517095e-13 -3.2449702e-13 4.5773091e-13] Sparsity at: 0.6261870773854245 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9826 - val_loss: 0.2119 - val_accuracy: 0.9581 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.5527575e-13 -2.4795072e-13 2.4899074e-13] Sparsity at: 0.6261870773854245 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9818 - val_loss: 0.1848 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.2018537e-14 -2.5366848e-13 -1.4930739e-13] Sparsity at: 0.6261870773854245 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9814 - val_loss: 0.2099 - val_accuracy: 0.9605 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.4189448e-12 -4.7730353e-13 5.3890638e-12] Sparsity at: 0.6261870773854245 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9814 - val_loss: 0.1914 - val_accuracy: 0.9624 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.5428935e-10 -8.1295304e-10 3.5037806e-10] Sparsity at: 0.6261870773854245 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1231 - accuracy: 0.9809 - val_loss: 0.1963 - val_accuracy: 0.9632 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.3212157e-08 -3.2464115e-08 7.0028420e-08] Sparsity at: 0.6261870773854245 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9821 - val_loss: 0.1773 - val_accuracy: 0.9678 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1695812e-04 -2.9856834e-04 9.5314455e-05] Sparsity at: 0.6261870773854245 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1228 - accuracy: 0.9811 - val_loss: 0.2233 - val_accuracy: 0.9562 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.6348332e-04 -3.3171699e-04 4.9628050e-04] Sparsity at: 0.6261870773854245 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9811 - val_loss: 0.1846 - val_accuracy: 0.9655 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -7.74102518e-05 -2.59405322e-04 1.10905414e-04] Sparsity at: 0.6261870773854245 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1205 - accuracy: 0.9819 - val_loss: 0.1846 - val_accuracy: 0.9657 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.8358597e-05 -9.7059037e-06 4.9182665e-05] Sparsity at: 0.6261870773854245 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9818 - val_loss: 0.2096 - val_accuracy: 0.9577 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.6698266e-03 -4.9069775e-03 4.5817667e-03] Sparsity at: 0.6261870773854245 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9803 - val_loss: 0.1874 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.1737018e-04 7.3223858e-04 -3.2825355e-04] Sparsity at: 0.6261870773854245 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1227 - accuracy: 0.9816 - val_loss: 0.1916 - val_accuracy: 0.9615 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.7965823e-05 5.6329271e-05 -6.5956730e-05] Sparsity at: 0.6261870773854245 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9819 - val_loss: 0.1917 - val_accuracy: 0.9643 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.7323512e-06 -3.8164759e-05 1.0086858e-05] Sparsity at: 0.6261870773854245 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1256 - accuracy: 0.9801 - val_loss: 0.1852 - val_accuracy: 0.9669 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.9808927e-05 3.7349731e-05 -3.5346897e-05] Sparsity at: 0.6261870773854245 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9816 - val_loss: 0.1872 - val_accuracy: 0.9625 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.6425590e-05 -4.4926524e-05 4.6440087e-05] Sparsity at: 0.6261870773854245 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1223 - accuracy: 0.9811 - val_loss: 0.2054 - val_accuracy: 0.9594 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0927357e-03 -6.2026009e-03 5.4529822e-03] Sparsity at: 0.6261870773854245 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9817 - val_loss: 0.1866 - val_accuracy: 0.9658 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.9960027e-04 -3.3034716e-04 -7.3041134e-05] Sparsity at: 0.6261870773854245 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1192 - accuracy: 0.9824 - val_loss: 0.1936 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.4566958e-06 1.5686150e-05 -6.8722593e-06] Sparsity at: 0.6261870773854245 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9821 - val_loss: 0.1927 - val_accuracy: 0.9637 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.14674075e-08 5.52240706e-08 -1.84081905e-08] Sparsity at: 0.6261870773854245 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9819 - val_loss: 0.1928 - val_accuracy: 0.9628 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... -1.47609125e-08 -1.38054261e-08 3.24301723e-08] Sparsity at: 0.6261870773854245 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1189 - accuracy: 0.9822 - val_loss: 0.1960 - val_accuracy: 0.9614 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 6.7346173e-09 2.3804260e-08 -2.5602670e-08] Sparsity at: 0.6261870773854245 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9801 - val_loss: 0.1924 - val_accuracy: 0.9632 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -5.7261254e-09 -2.4134216e-08 2.2805558e-08] Sparsity at: 0.6261870773854245 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9805 - val_loss: 0.1949 - val_accuracy: 0.9627 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.5028095e-08 -4.2201656e-08 2.1726066e-07] Sparsity at: 0.6261870773854245 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9824 - val_loss: 0.1882 - val_accuracy: 0.9635 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.5888820e-07 8.3858038e-07 -5.3474452e-08] Sparsity at: 0.6261870773854245 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9815 - val_loss: 0.1767 - val_accuracy: 0.9688 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -6.5361692e-06 1.0307149e-05 -8.6042819e-06] Sparsity at: 0.6261870773854245 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9813 - val_loss: 0.1909 - val_accuracy: 0.9631 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.7489677e-04 8.2126469e-05 8.9422778e-05] Sparsity at: 0.6261870773854245 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9809 - val_loss: 0.1926 - val_accuracy: 0.9630 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.7484671e-04 6.3625691e-03 -2.7935002e-03] Sparsity at: 0.6261870773854245 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9825 - val_loss: 0.2120 - val_accuracy: 0.9571 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -8.7932922e-06 5.7491590e-05 -4.0772579e-06] Sparsity at: 0.6261870773854245 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9809 - val_loss: 0.1984 - val_accuracy: 0.9600 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3095249e-07 -6.3485606e-07 2.7989262e-07] Sparsity at: 0.6261870773854245 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1220 - accuracy: 0.9813 - val_loss: 0.1938 - val_accuracy: 0.9635 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.5383223e-11 -1.5748814e-09 -3.1654665e-10] Sparsity at: 0.6261870773854245 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9815 - val_loss: 0.1750 - val_accuracy: 0.9686 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.4670797e-11 -4.6456564e-11 1.4698331e-11] Sparsity at: 0.6261870773854245 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1176 - accuracy: 0.9827 - val_loss: 0.1793 - val_accuracy: 0.9648 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.6470230e-14 9.3751829e-13 -5.1819406e-14] Sparsity at: 0.6261870773854245 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9821 - val_loss: 0.1835 - val_accuracy: 0.9637 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1831476e-14 -4.1584093e-14 3.9630764e-14] Sparsity at: 0.6261870773854245 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9811 - val_loss: 0.1901 - val_accuracy: 0.9630 [ 4.77403704e-34 -3.33338055e-34 3.05236187e-34 ... 1.04044835e-16 -3.12714294e-16 -1.01114865e-15] Sparsity at: 0.6261870773854245 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9813 - val_loss: 0.2042 - val_accuracy: 0.9580 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 1.0857882e-17 1.6774048e-17 -1.7847906e-17] Sparsity at: 0.6261870773854245 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9820 - val_loss: 0.2019 - val_accuracy: 0.9583 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1867780e-18 -2.5999674e-18 2.2984882e-18] Sparsity at: 0.6261870773854245 Epoch 480/500 235/235 [==============================] - 3s 12ms/step - loss: 0.1225 - accuracy: 0.9811 - val_loss: 0.1971 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0571067e-17 -2.5372296e-17 1.0218783e-17] Sparsity at: 0.6261870773854245 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9811 - val_loss: 0.1813 - val_accuracy: 0.9669 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.1598795e-17 1.2874081e-15 1.3142595e-15] Sparsity at: 0.6261870773854245 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9819 - val_loss: 0.1940 - val_accuracy: 0.9644 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0109051e-13 -1.7352532e-13 5.7039910e-14] Sparsity at: 0.6261870773854245 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1225 - accuracy: 0.9816 - val_loss: 0.1824 - val_accuracy: 0.9674 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.1375887e-12 -1.3811724e-11 7.6892234e-12] Sparsity at: 0.6261870773854245 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9814 - val_loss: 0.1922 - val_accuracy: 0.9652 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.2137538e-07 -5.2418752e-08 9.4872050e-08] Sparsity at: 0.6261870773854245 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9825 - val_loss: 0.1916 - val_accuracy: 0.9625 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.3055704e-05 1.0003652e-04 -2.2111813e-05] Sparsity at: 0.6261870773854245 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9808 - val_loss: 0.1880 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -4.9724868e-03 -9.1233030e-03 6.6645974e-03] Sparsity at: 0.6261870773854245 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1270 - accuracy: 0.9804 - val_loss: 0.1936 - val_accuracy: 0.9639 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 2.1798498e-05 4.4860793e-04 5.2090283e-05] Sparsity at: 0.6261870773854245 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9821 - val_loss: 0.1955 - val_accuracy: 0.9606 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 4.7741697e-08 7.0689929e-08 -4.6588973e-08] Sparsity at: 0.6261870773854245 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9819 - val_loss: 0.1827 - val_accuracy: 0.9660 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.0700405e-10 -3.2731223e-10 -1.2186370e-10] Sparsity at: 0.6261870773854245 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9821 - val_loss: 0.1870 - val_accuracy: 0.9646 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.3028921e-12 -5.9507980e-12 4.5671158e-12] Sparsity at: 0.6261870773854245 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9822 - val_loss: 0.1963 - val_accuracy: 0.9610 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 7.2621440e-14 3.6937890e-13 -3.2164638e-13] Sparsity at: 0.6261870773854245 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1261 - accuracy: 0.9802 - val_loss: 0.1830 - val_accuracy: 0.9682 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -2.3568306e-14 -3.4070670e-14 3.0928551e-14] Sparsity at: 0.6261870773854245 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9811 - val_loss: 0.1920 - val_accuracy: 0.9647 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.1635828e-16 9.1994247e-16 -3.5308238e-16] Sparsity at: 0.6261870773854245 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9808 - val_loss: 0.1833 - val_accuracy: 0.9678 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 9.5777635e-17 9.6112074e-17 -1.2069399e-16] Sparsity at: 0.6261870773854245 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9817 - val_loss: 0.1945 - val_accuracy: 0.9616 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -1.0933997e-16 -1.3684917e-16 1.0053309e-16] Sparsity at: 0.6261870773854245 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1230 - accuracy: 0.9804 - val_loss: 0.1829 - val_accuracy: 0.9662 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -3.6710675e-15 -3.3810262e-14 1.7806262e-14] Sparsity at: 0.6261870773854245 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9815 - val_loss: 0.1760 - val_accuracy: 0.9681 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 8.0980692e-13 -4.3334577e-13 -7.8565653e-13] Sparsity at: 0.6261870773854245 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9820 - val_loss: 0.1861 - val_accuracy: 0.9650 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 3.7641068e-10 1.0870197e-09 -2.9902397e-10] Sparsity at: 0.6261870773854245 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9809 - val_loss: 0.1853 - val_accuracy: 0.9662 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... 5.6010452e-10 5.5396737e-10 -6.4949551e-10] Sparsity at: 0.6261870773854245 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1242 - accuracy: 0.9802 - val_loss: 0.1920 - val_accuracy: 0.9635 [ 4.7740370e-34 -3.3333806e-34 3.0523619e-34 ... -9.4685001e-06 2.6315218e-05 -4.6686155e-06] Sparsity at: 0.6261870773854245 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.03716909512877464 Thresholhold 0.01880677044391632 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.061059337109327316 Thresholhold 0.049408040940761566 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.12204661220312119 Thresholhold -0.13929115235805511 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 5/235 [..............................] - ETA: 3s - loss: 2.0911 - accuracy: 0.3438 WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0133s vs `on_train_batch_begin` time: 11.2313s). Check your callbacks. 235/235 [==============================] - 71s 12ms/step - loss: 0.2822 - accuracy: 0.9165 - val_loss: 0.2323 - val_accuracy: 0.9565 [ 0.01880677 0. 0.03845737 ... 0.23230197 -0.12916717 0.17103295] Sparsity at: 0.2700864012021037 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0972 - accuracy: 0.9724 - val_loss: 0.1042 - val_accuracy: 0.9701 [ 0.01880677 0. 0.03845737 ... 0.25154665 -0.1502766 0.17470135] Sparsity at: 0.2700864012021037 Epoch 3/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0568 - accuracy: 0.9842 - val_loss: 0.0897 - val_accuracy: 0.9719 [ 0.01880677 0. 0.03845737 ... 0.26642194 -0.16245458 0.17647526] Sparsity at: 0.2700864012021037 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0342 - accuracy: 0.9912 - val_loss: 0.0872 - val_accuracy: 0.9730 [ 0.01880677 0. 0.03845737 ... 0.2833718 -0.17415844 0.17902444] Sparsity at: 0.2700864012021037 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0212 - accuracy: 0.9956 - val_loss: 0.0861 - val_accuracy: 0.9740 [ 0.01880677 0. 0.03845737 ... 0.2940272 -0.18124813 0.18278676] Sparsity at: 0.2700864012021037 Epoch 6/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0123 - accuracy: 0.9980 - val_loss: 0.0830 - val_accuracy: 0.9762 [ 0.01880677 0. 0.03845737 ... 0.3043624 -0.18816443 0.19039059] Sparsity at: 0.2700864012021037 Epoch 7/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0097 - accuracy: 0.9981 - val_loss: 0.0868 - val_accuracy: 0.9753 [ 0.01880677 0. 0.03845737 ... 0.3132718 -0.19224674 0.19410321] Sparsity at: 0.2700864012021037 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0084 - accuracy: 0.9986 - val_loss: 0.0925 - val_accuracy: 0.9760 [ 0.01880677 0. 0.03845737 ... 0.32388616 -0.19788942 0.20015681] Sparsity at: 0.2700864012021037 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9981 - val_loss: 0.1065 - val_accuracy: 0.9707 [ 0.01880677 0. 0.03845737 ... 0.32894272 -0.19394982 0.20317557] Sparsity at: 0.2700864012021037 Epoch 10/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0125 - accuracy: 0.9963 - val_loss: 0.1081 - val_accuracy: 0.9706 [ 0.01880677 0. 0.03845737 ... 0.3307135 -0.20972607 0.20700717] Sparsity at: 0.2700864012021037 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0137 - accuracy: 0.9959 - val_loss: 0.0955 - val_accuracy: 0.9742 [ 0.01880677 0. 0.03845737 ... 0.3430643 -0.2280049 0.20173776] Sparsity at: 0.2700864012021037 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 0.0976 - val_accuracy: 0.9766 [ 0.01880677 0. 0.03845737 ... 0.34481898 -0.24098162 0.20202838] Sparsity at: 0.2700864012021037 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.0830 - val_accuracy: 0.9785 [ 0.01880677 0. 0.03845737 ... 0.33959004 -0.24173242 0.21162002] Sparsity at: 0.2700864012021037 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.0930 - val_accuracy: 0.9782 [ 0.01880677 0. 0.03845737 ... 0.3554757 -0.24013168 0.21411751] Sparsity at: 0.2700864012021037 Epoch 15/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.1005 - val_accuracy: 0.9748 [ 0.01880677 0. 0.03845737 ... 0.3582253 -0.23785211 0.21446487] Sparsity at: 0.2700864012021037 Epoch 16/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.1081 - val_accuracy: 0.9739 [ 0.01880677 0. 0.03845737 ... 0.35811278 -0.23401527 0.21904224] Sparsity at: 0.2700864012021037 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9995 - val_loss: 0.0896 - val_accuracy: 0.9801 [ 0.01880677 0. 0.03845737 ... 0.36331508 -0.24353647 0.21864751] Sparsity at: 0.2700864012021037 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0949 - val_accuracy: 0.9790 [ 0.01880677 0. 0.03845737 ... 0.3648974 -0.24765861 0.22203295] Sparsity at: 0.2700864012021037 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9979 - val_loss: 0.1297 - val_accuracy: 0.9683 [ 0.01880677 0. 0.03845737 ... 0.37055087 -0.23265326 0.22086513] Sparsity at: 0.2700864012021037 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0185 - accuracy: 0.9937 - val_loss: 0.1121 - val_accuracy: 0.9734 [ 0.01880677 0. 0.03845737 ... 0.36024556 -0.23035558 0.21201189] Sparsity at: 0.2700864012021037 Epoch 21/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0105 - accuracy: 0.9965 - val_loss: 0.1040 - val_accuracy: 0.9759 [ 0.01880677 0. 0.03845737 ... 0.35330984 -0.24664202 0.23289207] Sparsity at: 0.2700864012021037 Epoch 22/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0049 - accuracy: 0.9987 - val_loss: 0.0848 - val_accuracy: 0.9805 [ 0.01880677 0. 0.03845737 ... 0.35600254 -0.24224123 0.23009336] Sparsity at: 0.2700864012021037 Epoch 23/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 0.0832 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.36016792 -0.24645394 0.22845107] Sparsity at: 0.2700864012021037 Epoch 24/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0813 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.36635008 -0.25365627 0.22901575] Sparsity at: 0.2700864012021037 Epoch 25/500 235/235 [==============================] - 3s 13ms/step - loss: 3.9546e-04 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.36794826 -0.25475106 0.22977498] Sparsity at: 0.2700864012021037 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0581e-04 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9840 [ 0.01880677 0. 0.03845737 ... 0.3690376 -0.2554894 0.23062307] Sparsity at: 0.2700864012021037 Epoch 27/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6062e-04 - accuracy: 1.0000 - val_loss: 0.0773 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.36995986 -0.25572395 0.23165694] Sparsity at: 0.2700864012021037 Epoch 28/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3167e-04 - accuracy: 1.0000 - val_loss: 0.0780 - val_accuracy: 0.9841 [ 0.01880677 0. 0.03845737 ... 0.37123787 -0.2565263 0.23230918] Sparsity at: 0.2700864012021037 Epoch 29/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7690e-04 - accuracy: 0.9999 - val_loss: 0.0931 - val_accuracy: 0.9805 [ 0.01880677 0. 0.03845737 ... 0.38548762 -0.25822222 0.22202328] Sparsity at: 0.2700864012021037 Epoch 30/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.1964 - val_accuracy: 0.9596 [ 0.01880677 0. 0.03845737 ... 0.38521686 -0.25009054 0.21948905] Sparsity at: 0.2700864012021037 Epoch 31/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0260 - accuracy: 0.9911 - val_loss: 0.1129 - val_accuracy: 0.9739 [ 0.01880677 0. 0.03845737 ... 0.35372147 -0.23223694 0.21364167] Sparsity at: 0.2700864012021037 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.0824 - val_accuracy: 0.9804 [ 0.01880677 0. 0.03845737 ... 0.35754386 -0.24363075 0.22736974] Sparsity at: 0.2700864012021037 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.0790 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.35523343 -0.25388137 0.22773895] Sparsity at: 0.2700864012021037 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6699e-04 - accuracy: 0.9999 - val_loss: 0.0762 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.35688597 -0.25698718 0.22564442] Sparsity at: 0.2700864012021037 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5886e-04 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.35901588 -0.25960016 0.22549549] Sparsity at: 0.2700864012021037 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2731e-04 - accuracy: 1.0000 - val_loss: 0.0736 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.3602011 -0.26038894 0.22640407] Sparsity at: 0.2700864012021037 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0105e-04 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.3611924 -0.26196063 0.22725156] Sparsity at: 0.2700864012021037 Epoch 38/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6604e-04 - accuracy: 1.0000 - val_loss: 0.0731 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.36253938 -0.26318452 0.22772466] Sparsity at: 0.2700864012021037 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2356e-04 - accuracy: 1.0000 - val_loss: 0.0751 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36385235 -0.26307192 0.22799993] Sparsity at: 0.2700864012021037 Epoch 40/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1706e-04 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.36574307 -0.2628978 0.22846548] Sparsity at: 0.2700864012021037 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3216e-05 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.366388 -0.26367915 0.22872297] Sparsity at: 0.2700864012021037 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8997e-05 - accuracy: 1.0000 - val_loss: 0.0748 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.36807352 -0.26403603 0.22917792] Sparsity at: 0.2700864012021037 Epoch 43/500 235/235 [==============================] - 3s 13ms/step - loss: 8.7593e-05 - accuracy: 1.0000 - val_loss: 0.0755 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.3646239 -0.2634478 0.23352297] Sparsity at: 0.2700864012021037 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7897e-05 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9840 [ 0.01880677 0. 0.03845737 ... 0.36961648 -0.26444662 0.23028725] Sparsity at: 0.2700864012021037 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2560e-05 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9846 [ 0.01880677 0. 0.03845737 ... 0.37098816 -0.2641888 0.2308286 ] Sparsity at: 0.2700864012021037 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2305e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9845 [ 0.01880677 0. 0.03845737 ... 0.37239194 -0.26480266 0.23175713] Sparsity at: 0.2700864012021037 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6073e-05 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.373855 -0.26585743 0.23230873] Sparsity at: 0.2700864012021037 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8456e-05 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.37559715 -0.26602846 0.2321943 ] Sparsity at: 0.2700864012021037 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0379 - accuracy: 0.9889 - val_loss: 0.1178 - val_accuracy: 0.9728 [ 0.01880677 0. 0.03845737 ... 0.34024954 -0.31169555 0.2523747 ] Sparsity at: 0.2700864012021037 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0145 - accuracy: 0.9951 - val_loss: 0.0805 - val_accuracy: 0.9802 [ 0.01880677 0. 0.03845737 ... 0.33796552 -0.2952199 0.24179254] Sparsity at: 0.2700864012021037 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.06611566487076992 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.07814751184028879 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.29586061393585084 Thresholhold -0.3402203619480133 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 212s 12ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.0732 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.34649986 -0.30593488 0.24776769] Sparsity at: 0.2700864012021037 Epoch 52/500 235/235 [==============================] - 3s 12ms/step - loss: 9.4482e-04 - accuracy: 0.9999 - val_loss: 0.0708 - val_accuracy: 0.9843 [ 0.01880677 0. 0.03845737 ... 0.35014412 -0.30574608 0.24929197] Sparsity at: 0.2700864012021037 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0215e-04 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.34939146 -0.3053437 0.24822623] Sparsity at: 0.2700864012021037 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3638e-04 - accuracy: 1.0000 - val_loss: 0.0739 - val_accuracy: 0.9846 [ 0.01880677 0. 0.03845737 ... 0.35179278 -0.30652002 0.24977842] Sparsity at: 0.2700864012021037 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3570e-04 - accuracy: 1.0000 - val_loss: 0.0740 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.35326567 -0.30825225 0.24982753] Sparsity at: 0.2700864012021037 Epoch 56/500 235/235 [==============================] - 3s 13ms/step - loss: 2.2293e-04 - accuracy: 1.0000 - val_loss: 0.0754 - val_accuracy: 0.9840 [ 0.01880677 0. 0.03845737 ... 0.35434288 -0.310057 0.25146276] Sparsity at: 0.2700864012021037 Epoch 57/500 235/235 [==============================] - 3s 13ms/step - loss: 2.6789e-04 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 0.9843 [ 0.01880677 0. 0.03845737 ... 0.35687312 -0.31093258 0.24972314] Sparsity at: 0.2700864012021037 Epoch 58/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5531e-04 - accuracy: 1.0000 - val_loss: 0.0757 - val_accuracy: 0.9843 [ 0.01880677 0. 0.03845737 ... 0.3587526 -0.31249523 0.25078946] Sparsity at: 0.2700864012021037 Epoch 59/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2801e-04 - accuracy: 1.0000 - val_loss: 0.0762 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.35987198 -0.31298646 0.25120676] Sparsity at: 0.2700864012021037 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0518e-04 - accuracy: 1.0000 - val_loss: 0.0758 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.36058617 -0.3141509 0.2525968 ] Sparsity at: 0.2700864012021037 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2929e-05 - accuracy: 1.0000 - val_loss: 0.0765 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.36126772 -0.31457937 0.25347766] Sparsity at: 0.2700864012021037 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7364e-04 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.36554542 -0.32059965 0.25254095] Sparsity at: 0.2700864012021037 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0141 - accuracy: 0.9953 - val_loss: 0.1353 - val_accuracy: 0.9736 [ 0.01880677 0. 0.03845737 ... 0.37673658 -0.30792266 0.25823566] Sparsity at: 0.2700864012021037 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0165 - accuracy: 0.9943 - val_loss: 0.0898 - val_accuracy: 0.9803 [ 0.01880677 0. 0.03845737 ... 0.35412034 -0.30369896 0.23872036] Sparsity at: 0.2700864012021037 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.0831 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.355375 -0.3069879 0.24859676] Sparsity at: 0.2700864012021037 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0825 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.35351956 -0.30806434 0.2519983 ] Sparsity at: 0.2700864012021037 Epoch 67/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7384e-04 - accuracy: 1.0000 - val_loss: 0.0770 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.35471302 -0.30594563 0.25232986] Sparsity at: 0.2700864012021037 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2957e-04 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.35748875 -0.3065587 0.24906768] Sparsity at: 0.2700864012021037 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7658e-04 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.35796073 -0.30671787 0.25018468] Sparsity at: 0.2700864012021037 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4306e-04 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.3580548 -0.30622974 0.25115842] Sparsity at: 0.2700864012021037 Epoch 71/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1660e-04 - accuracy: 1.0000 - val_loss: 0.0786 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.35857388 -0.30798468 0.25219536] Sparsity at: 0.2700864012021037 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8954e-05 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.35941797 -0.30842283 0.25261715] Sparsity at: 0.2700864012021037 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6900e-04 - accuracy: 0.9999 - val_loss: 0.0796 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.35994643 -0.30888423 0.25311223] Sparsity at: 0.2700864012021037 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1509e-04 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.36516017 -0.3101393 0.25442067] Sparsity at: 0.2700864012021037 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1917e-05 - accuracy: 1.0000 - val_loss: 0.0797 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36551282 -0.31127137 0.25638562] Sparsity at: 0.2700864012021037 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4454e-05 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.3660598 -0.31175742 0.2564181 ] Sparsity at: 0.2700864012021037 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6115e-05 - accuracy: 1.0000 - val_loss: 0.0802 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36705652 -0.31253 0.2576863 ] Sparsity at: 0.2700864012021037 Epoch 78/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3647e-05 - accuracy: 1.0000 - val_loss: 0.0805 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.36782357 -0.31288725 0.25788078] Sparsity at: 0.2700864012021037 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9160e-05 - accuracy: 1.0000 - val_loss: 0.0810 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.36902595 -0.31310007 0.25819367] Sparsity at: 0.2700864012021037 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6321e-05 - accuracy: 1.0000 - val_loss: 0.0816 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.36952093 -0.31437287 0.25937843] Sparsity at: 0.2700864012021037 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1893e-05 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9838 [ 0.01880677 0. 0.03845737 ... 0.37053114 -0.3150092 0.25942394] Sparsity at: 0.2700864012021037 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4929e-05 - accuracy: 1.0000 - val_loss: 0.0831 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36897573 -0.31608197 0.262067 ] Sparsity at: 0.2700864012021037 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0179 - accuracy: 0.9947 - val_loss: 0.1481 - val_accuracy: 0.9695 [ 0.01880677 0. 0.03845737 ... 0.35340166 -0.31162125 0.26489103] Sparsity at: 0.2700864012021037 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0142 - accuracy: 0.9951 - val_loss: 0.1011 - val_accuracy: 0.9781 [ 0.01880677 0. 0.03845737 ... 0.34771538 -0.30399865 0.26014343] Sparsity at: 0.2700864012021037 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.0833 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.33571327 -0.31862366 0.25775436] Sparsity at: 0.2700864012021037 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.0813 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.33758464 -0.32563385 0.25469264] Sparsity at: 0.2700864012021037 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0645e-04 - accuracy: 1.0000 - val_loss: 0.0818 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.33737102 -0.32579282 0.2565311 ] Sparsity at: 0.2700864012021037 Epoch 88/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1240e-04 - accuracy: 1.0000 - val_loss: 0.0818 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.33733696 -0.32757583 0.25629613] Sparsity at: 0.2700864012021037 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4687e-04 - accuracy: 1.0000 - val_loss: 0.0824 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.33717707 -0.3288919 0.25795746] Sparsity at: 0.2700864012021037 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1503e-04 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.33816466 -0.33012235 0.257459 ] Sparsity at: 0.2700864012021037 Epoch 91/500 235/235 [==============================] - 3s 13ms/step - loss: 9.5318e-05 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.33882195 -0.33077592 0.25776714] Sparsity at: 0.2700864012021037 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4048e-05 - accuracy: 1.0000 - val_loss: 0.0840 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.33912402 -0.3321402 0.2589783 ] Sparsity at: 0.2700864012021037 Epoch 93/500 235/235 [==============================] - 3s 13ms/step - loss: 7.8743e-05 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.33953968 -0.33359125 0.26039138] Sparsity at: 0.2700864012021037 Epoch 94/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5555e-05 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.33987436 -0.3338481 0.2613152 ] Sparsity at: 0.2700864012021037 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3672e-05 - accuracy: 1.0000 - val_loss: 0.0847 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.3400828 -0.3348275 0.2617422 ] Sparsity at: 0.2700864012021037 Epoch 96/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4499e-05 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9841 [ 0.01880677 0. 0.03845737 ... 0.34050107 -0.33645937 0.2617213 ] Sparsity at: 0.2700864012021037 Epoch 97/500 235/235 [==============================] - 3s 13ms/step - loss: 6.3806e-05 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.34189215 -0.3368822 0.261293 ] Sparsity at: 0.2700864012021037 Epoch 98/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7061e-05 - accuracy: 1.0000 - val_loss: 0.0867 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.34146202 -0.33804733 0.26295632] Sparsity at: 0.2700864012021037 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0421e-05 - accuracy: 1.0000 - val_loss: 0.0874 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.34213364 -0.33804637 0.26357004] Sparsity at: 0.2700864012021037 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3490e-05 - accuracy: 1.0000 - val_loss: 0.0873 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.34299138 -0.33868858 0.26447505] Sparsity at: 0.2700864012021037 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.11399688154693344 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.13121018478452484 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.4002787557079337 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 208s 11ms/step - loss: 3.3624e-05 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.34355098 -0.33980036 0.26527673] Sparsity at: 0.2700864012021037 Epoch 102/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7624e-05 - accuracy: 1.0000 - val_loss: 0.0882 - val_accuracy: 0.9840 [ 0.01880677 0. 0.03845737 ... 0.34335235 -0.34064272 0.2672803 ] Sparsity at: 0.2700864012021037 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0451e-05 - accuracy: 1.0000 - val_loss: 0.0880 - val_accuracy: 0.9843 [ 0.01880677 0. 0.03845737 ... 0.34468177 -0.3413692 0.26692942] Sparsity at: 0.2700864012021037 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 0.2332 - val_accuracy: 0.9630 [ 0.01880677 0. 0.03845737 ... 0.36040077 -0.3347753 0.28032964] Sparsity at: 0.2700864012021037 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.1075 - val_accuracy: 0.9794 [ 0.01880677 0. 0.03845737 ... 0.32841897 -0.34730953 0.24894111] Sparsity at: 0.2700864012021037 Epoch 106/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0908 - val_accuracy: 0.9811 [ 0.01880677 0. 0.03845737 ... 0.32772934 -0.35196272 0.25824478] Sparsity at: 0.2700864012021037 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0885 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.32978287 -0.37093228 0.25648287] Sparsity at: 0.2700864012021037 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3949e-04 - accuracy: 1.0000 - val_loss: 0.0861 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.32799506 -0.3661913 0.25405964] Sparsity at: 0.2700864012021037 Epoch 109/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6075e-04 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.3300315 -0.36777917 0.2537363 ] Sparsity at: 0.2700864012021037 Epoch 110/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4888e-04 - accuracy: 1.0000 - val_loss: 0.0837 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.33057898 -0.36922738 0.25497517] Sparsity at: 0.2700864012021037 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2932e-04 - accuracy: 1.0000 - val_loss: 0.0838 - val_accuracy: 0.9838 [ 0.01880677 0. 0.03845737 ... 0.33290505 -0.3710852 0.2563007 ] Sparsity at: 0.2700864012021037 Epoch 112/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5849e-04 - accuracy: 1.0000 - val_loss: 0.0852 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.33359864 -0.3725762 0.25865397] Sparsity at: 0.2700864012021037 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 8.3597e-05 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.33404073 -0.37506932 0.25921378] Sparsity at: 0.2700864012021037 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1107e-05 - accuracy: 1.0000 - val_loss: 0.0864 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.33477712 -0.37590307 0.25919968] Sparsity at: 0.2700864012021037 Epoch 115/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0728e-05 - accuracy: 1.0000 - val_loss: 0.0866 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.3355483 -0.3761024 0.26005617] Sparsity at: 0.2700864012021037 Epoch 116/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1633e-05 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.336646 -0.37635362 0.26030508] Sparsity at: 0.2700864012021037 Epoch 117/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7865e-05 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9838 [ 0.01880677 0. 0.03845737 ... 0.3382502 -0.3771674 0.2609766 ] Sparsity at: 0.2700864012021037 Epoch 118/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5381e-05 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.33886024 -0.37758505 0.26143485] Sparsity at: 0.2700864012021037 Epoch 119/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0442e-04 - accuracy: 1.0000 - val_loss: 0.0870 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.3404003 -0.37835962 0.26037258] Sparsity at: 0.2700864012021037 Epoch 120/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.1375 - val_accuracy: 0.9756 [ 0.01880677 0. 0.03845737 ... 0.3375445 -0.3762832 0.28690943] Sparsity at: 0.2700864012021037 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0067 - accuracy: 0.9976 - val_loss: 0.1007 - val_accuracy: 0.9809 [ 0.01880677 0. 0.03845737 ... 0.3395634 -0.39015815 0.27647647] Sparsity at: 0.2700864012021037 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0950 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.3348401 -0.40696812 0.28352305] Sparsity at: 0.2700864012021037 Epoch 123/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8965e-04 - accuracy: 0.9999 - val_loss: 0.0938 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.3371955 -0.4054046 0.28151917] Sparsity at: 0.2700864012021037 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1092e-04 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.34081003 -0.40776658 0.2796952 ] Sparsity at: 0.2700864012021037 Epoch 125/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1504e-04 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.3406034 -0.40692627 0.2807455 ] Sparsity at: 0.2700864012021037 Epoch 126/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0141e-04 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.34120196 -0.40680203 0.28074947] Sparsity at: 0.2700864012021037 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 8.7916e-05 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.3409172 -0.40760893 0.28079218] Sparsity at: 0.2700864012021037 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5763e-05 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.34316167 -0.40584117 0.28331783] Sparsity at: 0.2700864012021037 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5958e-04 - accuracy: 0.9999 - val_loss: 0.0935 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.34004548 -0.40655473 0.28512457] Sparsity at: 0.2700864012021037 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6982e-05 - accuracy: 1.0000 - val_loss: 0.0900 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.3461929 -0.40691686 0.28500563] Sparsity at: 0.2700864012021037 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6271e-05 - accuracy: 1.0000 - val_loss: 0.0922 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.34648305 -0.40848494 0.2842884 ] Sparsity at: 0.2700864012021037 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6481e-05 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.3457973 -0.40725157 0.2840913 ] Sparsity at: 0.2700864012021037 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4592e-05 - accuracy: 1.0000 - val_loss: 0.0900 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.3462609 -0.40795615 0.28438127] Sparsity at: 0.2700864012021037 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5627e-04 - accuracy: 0.9999 - val_loss: 0.1054 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.35479066 -0.40877277 0.2864238 ] Sparsity at: 0.2700864012021037 Epoch 135/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0060 - accuracy: 0.9984 - val_loss: 0.1422 - val_accuracy: 0.9778 [ 0.01880677 0. 0.03845737 ... 0.36897647 -0.44003403 0.28236172] Sparsity at: 0.2700864012021037 Epoch 136/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0064 - accuracy: 0.9977 - val_loss: 0.0959 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.36256036 -0.4249798 0.28098148] Sparsity at: 0.2700864012021037 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0979 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.36129194 -0.4155263 0.27498567] Sparsity at: 0.2700864012021037 Epoch 138/500 235/235 [==============================] - 3s 13ms/step - loss: 6.7090e-04 - accuracy: 0.9998 - val_loss: 0.0978 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36150488 -0.4277539 0.27248868] Sparsity at: 0.2700864012021037 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5918e-04 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.35935318 -0.4291232 0.2744506 ] Sparsity at: 0.2700864012021037 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4314e-05 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.3617491 -0.43119362 0.2706803 ] Sparsity at: 0.2700864012021037 Epoch 141/500 235/235 [==============================] - 3s 13ms/step - loss: 7.5312e-05 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.36000967 -0.43136913 0.27359766] Sparsity at: 0.2700864012021037 Epoch 142/500 235/235 [==============================] - 3s 13ms/step - loss: 5.3974e-05 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36042088 -0.43318978 0.27439418] Sparsity at: 0.2700864012021037 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3083e-05 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.3607577 -0.4337899 0.2744164 ] Sparsity at: 0.2700864012021037 Epoch 144/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7338e-04 - accuracy: 0.9999 - val_loss: 0.0951 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.37011084 -0.4350443 0.27406392] Sparsity at: 0.2700864012021037 Epoch 145/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5814e-04 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.37632015 -0.4368607 0.27520758] Sparsity at: 0.2700864012021037 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3552e-05 - accuracy: 1.0000 - val_loss: 0.0981 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.37311575 -0.437021 0.27650645] Sparsity at: 0.2700864012021037 Epoch 147/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6147e-04 - accuracy: 0.9999 - val_loss: 0.1055 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.37655255 -0.43914235 0.27770877] Sparsity at: 0.2700864012021037 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6875e-04 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.37839386 -0.43755397 0.281912 ] Sparsity at: 0.2700864012021037 Epoch 149/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1201 - val_accuracy: 0.9808 [ 0.01880677 0. 0.03845737 ... 0.35995096 -0.4258499 0.2926219 ] Sparsity at: 0.2700864012021037 Epoch 150/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.1128 - val_accuracy: 0.9807 [ 0.01880677 0. 0.03845737 ... 0.34868708 -0.4287324 0.28939417] Sparsity at: 0.2700864012021037 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.18812474255891143 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.206513359169878 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.491107206467543 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 208s 11ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1037 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.3601177 -0.42047694 0.277014 ] Sparsity at: 0.2700864012021037 Epoch 152/500 235/235 [==============================] - 3s 12ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1092 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.3643385 -0.43071586 0.2699396 ] Sparsity at: 0.2700864012021037 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8272e-04 - accuracy: 0.9998 - val_loss: 0.1103 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.36669075 -0.42281324 0.27123058] Sparsity at: 0.2700864012021037 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1772e-04 - accuracy: 0.9998 - val_loss: 0.1016 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.36908308 -0.42386812 0.2698287 ] Sparsity at: 0.2700864012021037 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9805e-04 - accuracy: 0.9999 - val_loss: 0.1058 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.36343887 -0.43117967 0.2665424 ] Sparsity at: 0.2700864012021037 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1713e-04 - accuracy: 0.9999 - val_loss: 0.1082 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.36169255 -0.44398272 0.27015308] Sparsity at: 0.2700864012021037 Epoch 157/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9860e-04 - accuracy: 0.9998 - val_loss: 0.1112 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.36126116 -0.44341728 0.27187636] Sparsity at: 0.2700864012021037 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3008e-04 - accuracy: 0.9998 - val_loss: 0.1085 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.36051553 -0.451966 0.272434 ] Sparsity at: 0.2700864012021037 Epoch 159/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3101e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.35927668 -0.4410087 0.2706667 ] Sparsity at: 0.2700864012021037 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 5.1731e-05 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.36012492 -0.4401841 0.2708258 ] Sparsity at: 0.2700864012021037 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9982e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.36151013 -0.44185114 0.27136832] Sparsity at: 0.2700864012021037 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5551e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.36244118 -0.4421511 0.2711029 ] Sparsity at: 0.2700864012021037 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8482e-04 - accuracy: 0.9999 - val_loss: 0.1064 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.3640065 -0.44545192 0.274835 ] Sparsity at: 0.2700864012021037 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0920e-04 - accuracy: 0.9999 - val_loss: 0.1189 - val_accuracy: 0.9813 [ 0.01880677 0. 0.03845737 ... 0.35981256 -0.42686298 0.2642127 ] Sparsity at: 0.2700864012021037 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0071 - accuracy: 0.9980 - val_loss: 0.1256 - val_accuracy: 0.9782 [ 0.01880677 0. 0.03845737 ... 0.33363706 -0.44629386 0.29015514] Sparsity at: 0.2700864012021037 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1069 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.34243256 -0.44975555 0.29448718] Sparsity at: 0.2700864012021037 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1093 - val_accuracy: 0.9811 [ 0.01880677 0. 0.03845737 ... 0.33557335 -0.43532175 0.2882321 ] Sparsity at: 0.2700864012021037 Epoch 168/500 235/235 [==============================] - 3s 13ms/step - loss: 9.8776e-04 - accuracy: 0.9997 - val_loss: 0.1052 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.32133368 -0.44937596 0.3104836 ] Sparsity at: 0.2700864012021037 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7773e-04 - accuracy: 0.9999 - val_loss: 0.1008 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.32649454 -0.45285904 0.305842 ] Sparsity at: 0.2700864012021037 Epoch 170/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2340e-05 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.32736513 -0.45328802 0.3058304 ] Sparsity at: 0.2700864012021037 Epoch 171/500 235/235 [==============================] - 3s 13ms/step - loss: 5.1660e-05 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.3276849 -0.45351404 0.30513856] Sparsity at: 0.2700864012021037 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5364e-04 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.3285319 -0.455314 0.30560303] Sparsity at: 0.2700864012021037 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5150e-05 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.3289708 -0.45579734 0.30627987] Sparsity at: 0.2700864012021037 Epoch 174/500 235/235 [==============================] - 3s 13ms/step - loss: 2.9283e-05 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9838 [ 0.01880677 0. 0.03845737 ... 0.3295714 -0.45555884 0.30615643] Sparsity at: 0.2700864012021037 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3191e-05 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.3298288 -0.45581204 0.30725217] Sparsity at: 0.2700864012021037 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7074e-05 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.33048102 -0.45574743 0.3074661 ] Sparsity at: 0.2700864012021037 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0661e-05 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9838 [ 0.01880677 0. 0.03845737 ... 0.33148414 -0.45745006 0.30826765] Sparsity at: 0.2700864012021037 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9745e-05 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.3335649 -0.45762914 0.30664203] Sparsity at: 0.2700864012021037 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7588e-05 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.33367676 -0.45804408 0.30824685] Sparsity at: 0.2700864012021037 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7507e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9846 [ 0.01880677 0. 0.03845737 ... 0.33417374 -0.45997372 0.3081841 ] Sparsity at: 0.2700864012021037 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4932e-05 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9847 [ 0.01880677 0. 0.03845737 ... 0.33482525 -0.46087486 0.3086759 ] Sparsity at: 0.2700864012021037 Epoch 182/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3685e-05 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9845 [ 0.01880677 0. 0.03845737 ... 0.3344229 -0.46051115 0.30941352] Sparsity at: 0.2700864012021037 Epoch 183/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1003e-05 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.33476108 -0.46100658 0.3097497 ] Sparsity at: 0.2700864012021037 Epoch 184/500 235/235 [==============================] - 3s 13ms/step - loss: 9.8042e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9846 [ 0.01880677 0. 0.03845737 ... 0.3348471 -0.462014 0.31109118] Sparsity at: 0.2700864012021037 Epoch 185/500 235/235 [==============================] - 3s 13ms/step - loss: 7.8493e-06 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9845 [ 0.01880677 0. 0.03845737 ... 0.3358044 -0.46281505 0.31119043] Sparsity at: 0.2700864012021037 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1004e-06 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9845 [ 0.01880677 0. 0.03845737 ... 0.33712786 -0.4634458 0.31126857] Sparsity at: 0.2700864012021037 Epoch 187/500 235/235 [==============================] - 3s 13ms/step - loss: 6.8091e-06 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9849 [ 0.01880677 0. 0.03845737 ... 0.33780485 -0.46447983 0.31138745] Sparsity at: 0.2700864012021037 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5666e-06 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9847 [ 0.01880677 0. 0.03845737 ... 0.3385703 -0.4649353 0.31126598] Sparsity at: 0.2700864012021037 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8479e-06 - accuracy: 1.0000 - val_loss: 0.0978 - val_accuracy: 0.9846 [ 0.01880677 0. 0.03845737 ... 0.3395042 -0.46442014 0.31081635] Sparsity at: 0.2700864012021037 Epoch 190/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7895e-06 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9852 [ 0.01880677 0. 0.03845737 ... 0.33979148 -0.46555427 0.3122346 ] Sparsity at: 0.2700864012021037 Epoch 191/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3655e-06 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9847 [ 0.01880677 0. 0.03845737 ... 0.34040755 -0.46597618 0.3123224 ] Sparsity at: 0.2700864012021037 Epoch 192/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7726e-06 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.34118575 -0.46675992 0.3128848 ] Sparsity at: 0.2700864012021037 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6890e-06 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.3418691 -0.46705475 0.31337795] Sparsity at: 0.2700864012021037 Epoch 194/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0388e-06 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9849 [ 0.01880677 0. 0.03845737 ... 0.34258795 -0.46789283 0.3135271 ] Sparsity at: 0.2700864012021037 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9256e-06 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9844 [ 0.01880677 0. 0.03845737 ... 0.34350485 -0.46867213 0.3140597 ] Sparsity at: 0.2700864012021037 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8383e-06 - accuracy: 1.0000 - val_loss: 0.0994 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.34262195 -0.46986195 0.31702903] Sparsity at: 0.2700864012021037 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6226e-06 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.34476092 -0.47078562 0.31589088] Sparsity at: 0.2700864012021037 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0970e-06 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9852 [ 0.01880677 0. 0.03845737 ... 0.3455278 -0.47136638 0.31602263] Sparsity at: 0.2700864012021037 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1485e-06 - accuracy: 1.0000 - val_loss: 0.1004 - val_accuracy: 0.9850 [ 0.01880677 0. 0.03845737 ... 0.3460891 -0.47320247 0.3166219 ] Sparsity at: 0.2700864012021037 Epoch 200/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9395e-06 - accuracy: 1.0000 - val_loss: 0.1007 - val_accuracy: 0.9848 [ 0.01880677 0. 0.03845737 ... 0.3473236 -0.4727571 0.31633136] Sparsity at: 0.2700864012021037 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.25391200827743177 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.2734804470574659 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.584526818800029 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 209s 11ms/step - loss: 1.9554e-06 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9850 [ 0.01880677 0. 0.03845737 ... 0.34716374 -0.4705087 0.31762323] Sparsity at: 0.2700864012021037 Epoch 202/500 235/235 [==============================] - 3s 12ms/step - loss: 2.4538e-06 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9853 [ 0.01880677 0. 0.03845737 ... 0.3507001 -0.4713123 0.31804335] Sparsity at: 0.2700864012021037 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9883e-06 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9854 [ 0.01880677 0. 0.03845737 ... 0.35301724 -0.4728621 0.31954953] Sparsity at: 0.2700864012021037 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3723e-06 - accuracy: 1.0000 - val_loss: 0.1037 - val_accuracy: 0.9852 [ 0.01880677 0. 0.03845737 ... 0.3544895 -0.47318903 0.32081956] Sparsity at: 0.2700864012021037 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2705e-06 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9849 [ 0.01880677 0. 0.03845737 ... 0.3551191 -0.47363254 0.32090122] Sparsity at: 0.2700864012021037 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2754e-06 - accuracy: 1.0000 - val_loss: 0.1042 - val_accuracy: 0.9854 [ 0.01880677 0. 0.03845737 ... 0.35557815 -0.47504097 0.3197729 ] Sparsity at: 0.2700864012021037 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1830e-06 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9852 [ 0.01880677 0. 0.03845737 ... 0.35496363 -0.47466144 0.3235351 ] Sparsity at: 0.2700864012021037 Epoch 208/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1301e-06 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9847 [ 0.01880677 0. 0.03845737 ... 0.35510162 -0.47458428 0.32540953] Sparsity at: 0.2700864012021037 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6354e-07 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9851 [ 0.01880677 0. 0.03845737 ... 0.35627982 -0.4757639 0.32569066] Sparsity at: 0.2700864012021037 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1455e-07 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9852 [ 0.01880677 0. 0.03845737 ... 0.3577389 -0.4764981 0.32580936] Sparsity at: 0.2700864012021037 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5487e-07 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9850 [ 0.01880677 0. 0.03845737 ... 0.35756075 -0.47778642 0.32649672] Sparsity at: 0.2700864012021037 Epoch 212/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0484e-07 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9855 [ 0.01880677 0. 0.03845737 ... 0.35865164 -0.47870785 0.32698348] Sparsity at: 0.2700864012021037 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9977 - val_loss: 0.2105 - val_accuracy: 0.9693 [ 0.01880677 0. 0.03845737 ... 0.39237145 -0.49093488 0.3377547 ] Sparsity at: 0.2700864012021037 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0122 - accuracy: 0.9965 - val_loss: 0.1333 - val_accuracy: 0.9796 [ 0.01880677 0. 0.03845737 ... 0.42461854 -0.48783022 0.30389294] Sparsity at: 0.2700864012021037 Epoch 215/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1121 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.42036825 -0.4888907 0.30109268] Sparsity at: 0.2700864012021037 Epoch 216/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7490e-04 - accuracy: 0.9999 - val_loss: 0.1149 - val_accuracy: 0.9809 [ 0.01880677 0. 0.03845737 ... 0.41582876 -0.4907547 0.30110124] Sparsity at: 0.2700864012021037 Epoch 217/500 235/235 [==============================] - 3s 15ms/step - loss: 1.5328e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.41897428 -0.4913908 0.30070364] Sparsity at: 0.2700864012021037 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2944e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.4183183 -0.49323383 0.3007402 ] Sparsity at: 0.2700864012021037 Epoch 219/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3078e-05 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.4179737 -0.49358097 0.30209407] Sparsity at: 0.2700864012021037 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7978e-05 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.41765046 -0.4942667 0.30295324] Sparsity at: 0.2700864012021037 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9476e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.4176864 -0.49442405 0.3027076 ] Sparsity at: 0.2700864012021037 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8024e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.41773552 -0.49574912 0.3027521 ] Sparsity at: 0.2700864012021037 Epoch 223/500 235/235 [==============================] - 3s 15ms/step - loss: 3.0302e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.41809425 -0.49592334 0.30306578] Sparsity at: 0.2700864012021037 Epoch 224/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0294e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.41829067 -0.49560916 0.30217493] Sparsity at: 0.2700864012021037 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0096e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.4182166 -0.49559775 0.3022078 ] Sparsity at: 0.2700864012021037 Epoch 226/500 235/235 [==============================] - 3s 13ms/step - loss: 2.0924e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.41829705 -0.4960251 0.30145115] Sparsity at: 0.2700864012021037 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5397e-05 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.41814268 -0.4950594 0.30178007] Sparsity at: 0.2700864012021037 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8972e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.41764712 -0.49246183 0.3023807 ] Sparsity at: 0.2700864012021037 Epoch 229/500 235/235 [==============================] - 3s 13ms/step - loss: 3.0595e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.42408592 -0.4939985 0.29702964] Sparsity at: 0.2700864012021037 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.1797 - val_accuracy: 0.9733 [ 0.01880677 0. 0.03845737 ... 0.38841423 -0.48762226 0.3102435 ] Sparsity at: 0.2700864012021037 Epoch 231/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.1504 - val_accuracy: 0.9772 [ 0.01880677 0. 0.03845737 ... 0.3962979 -0.4863689 0.28819868] Sparsity at: 0.2700864012021037 Epoch 232/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1200 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.38960353 -0.5037552 0.3101871 ] Sparsity at: 0.2700864012021037 Epoch 233/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6499e-04 - accuracy: 0.9998 - val_loss: 0.1169 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.39122912 -0.50801945 0.3068121 ] Sparsity at: 0.2700864012021037 Epoch 234/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3443e-04 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.3966092 -0.5081724 0.30134556] Sparsity at: 0.2700864012021037 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0725e-04 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.39615136 -0.5080645 0.3009617 ] Sparsity at: 0.2700864012021037 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2857e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.39488232 -0.5084414 0.300379 ] Sparsity at: 0.2700864012021037 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0070e-05 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.3950429 -0.50430375 0.30109242] Sparsity at: 0.2700864012021037 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6719e-05 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.39512762 -0.5052368 0.30190766] Sparsity at: 0.2700864012021037 Epoch 239/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4429e-05 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.39563128 -0.5036845 0.302569 ] Sparsity at: 0.2700864012021037 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0874e-05 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.39612657 -0.5045081 0.3019057 ] Sparsity at: 0.2700864012021037 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7883e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.39527878 -0.50371796 0.30286515] Sparsity at: 0.2700864012021037 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4259e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.39500716 -0.50418353 0.30306926] Sparsity at: 0.2700864012021037 Epoch 243/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3056e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.39501286 -0.50402915 0.30357143] Sparsity at: 0.2700864012021037 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4530e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.39493927 -0.5045228 0.3038868 ] Sparsity at: 0.2700864012021037 Epoch 245/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.1372 - val_accuracy: 0.9799 [ 0.01880677 0. 0.03845737 ... 0.39564136 -0.5123502 0.30292124] Sparsity at: 0.2700864012021037 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.1471 - val_accuracy: 0.9793 [ 0.01880677 0. 0.03845737 ... 0.4302787 -0.51585627 0.33107707] Sparsity at: 0.2700864012021037 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.1289 - val_accuracy: 0.9799 [ 0.01880677 0. 0.03845737 ... 0.43270093 -0.5038761 0.32290173] Sparsity at: 0.2700864012021037 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1196 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.42104304 -0.5219948 0.3384797 ] Sparsity at: 0.2700864012021037 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9831e-04 - accuracy: 0.9999 - val_loss: 0.1184 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.415998 -0.52314967 0.329935 ] Sparsity at: 0.2700864012021037 Epoch 250/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2644e-04 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.4171712 -0.5249213 0.3339978 ] Sparsity at: 0.2700864012021037 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.3525012334928377 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.3701170785072101 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.6656737416860992 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 188s 11ms/step - loss: 9.8400e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.4153415 -0.5251948 0.33510813] Sparsity at: 0.2700864012021037 Epoch 252/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0117e-04 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.4205243 -0.52923036 0.33135483] Sparsity at: 0.2700864012021037 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6433e-04 - accuracy: 0.9999 - val_loss: 0.1190 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.41517037 -0.52724665 0.33376193] Sparsity at: 0.2700864012021037 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8912e-04 - accuracy: 0.9999 - val_loss: 0.1197 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.41745842 -0.52228445 0.3346743 ] Sparsity at: 0.2700864012021037 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3901e-04 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.41376457 -0.53081435 0.33818257] Sparsity at: 0.2700864012021037 Epoch 256/500 235/235 [==============================] - 3s 13ms/step - loss: 3.5619e-05 - accuracy: 1.0000 - val_loss: 0.1164 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.4151042 -0.5313632 0.3392456 ] Sparsity at: 0.2700864012021037 Epoch 257/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4986e-04 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.41385606 -0.53089726 0.3399099 ] Sparsity at: 0.2700864012021037 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7431e-05 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.40875438 -0.5307653 0.3448017 ] Sparsity at: 0.2700864012021037 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8695e-04 - accuracy: 0.9999 - val_loss: 0.1275 - val_accuracy: 0.9807 [ 0.01880677 0. 0.03845737 ... 0.4073385 -0.5323199 0.34833315] Sparsity at: 0.2700864012021037 Epoch 260/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5138e-04 - accuracy: 0.9999 - val_loss: 0.1256 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.40468368 -0.539095 0.3535958 ] Sparsity at: 0.2700864012021037 Epoch 261/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5688e-05 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.4081946 -0.53055644 0.3521436 ] Sparsity at: 0.2700864012021037 Epoch 262/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3778e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.40689647 -0.5327723 0.35564968] Sparsity at: 0.2700864012021037 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7068e-05 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.40389222 -0.53630584 0.35580838] Sparsity at: 0.2700864012021037 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2575e-05 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.4028894 -0.5357232 0.35670733] Sparsity at: 0.2700864012021037 Epoch 265/500 235/235 [==============================] - 3s 13ms/step - loss: 1.1395e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.40474254 -0.536253 0.35634285] Sparsity at: 0.2700864012021037 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9075e-05 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.40504634 -0.535391 0.35517022] Sparsity at: 0.2700864012021037 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.1489 - val_accuracy: 0.9775 [ 0.01880677 0. 0.03845737 ... 0.39812222 -0.545516 0.36634803] Sparsity at: 0.2700864012021037 Epoch 268/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 0.1299 - val_accuracy: 0.9803 [ 0.01880677 0. 0.03845737 ... 0.3965682 -0.55635047 0.3821793 ] Sparsity at: 0.2700864012021037 Epoch 269/500 235/235 [==============================] - 3s 13ms/step - loss: 6.9510e-04 - accuracy: 0.9998 - val_loss: 0.1191 - val_accuracy: 0.9809 [ 0.01880677 0. 0.03845737 ... 0.39817712 -0.55722547 0.37561053] Sparsity at: 0.2700864012021037 Epoch 270/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7852e-04 - accuracy: 0.9999 - val_loss: 0.1209 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.40058008 -0.551672 0.37366408] Sparsity at: 0.2700864012021037 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4779e-05 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.40039518 -0.55060464 0.37323916] Sparsity at: 0.2700864012021037 Epoch 272/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0072e-05 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.39827678 -0.552044 0.37506703] Sparsity at: 0.2700864012021037 Epoch 273/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5641e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.3988323 -0.5526311 0.37640378] Sparsity at: 0.2700864012021037 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8866e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.3990114 -0.55301535 0.37673283] Sparsity at: 0.2700864012021037 Epoch 275/500 235/235 [==============================] - 3s 13ms/step - loss: 1.0358e-04 - accuracy: 1.0000 - val_loss: 0.1143 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.39578936 -0.55523306 0.3687744 ] Sparsity at: 0.2700864012021037 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2937e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.39896113 -0.5541853 0.3726779 ] Sparsity at: 0.2700864012021037 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5709e-04 - accuracy: 0.9999 - val_loss: 0.1186 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.39995697 -0.5614169 0.3724748 ] Sparsity at: 0.2700864012021037 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1227e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.40198377 -0.56815046 0.37304357] Sparsity at: 0.2700864012021037 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8917e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.40365246 -0.5685243 0.37307006] Sparsity at: 0.2700864012021037 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4405e-05 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.40452942 -0.5679126 0.37245178] Sparsity at: 0.2700864012021037 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1274e-05 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.4048419 -0.5687718 0.37242457] Sparsity at: 0.2700864012021037 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7092e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.40500468 -0.56931275 0.3740916 ] Sparsity at: 0.2700864012021037 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0768e-05 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.4030841 -0.56994265 0.37501928] Sparsity at: 0.2700864012021037 Epoch 284/500 235/235 [==============================] - 3s 13ms/step - loss: 8.3432e-06 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.40471503 -0.57026035 0.37497312] Sparsity at: 0.2700864012021037 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8467e-06 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.4045088 -0.56993586 0.3774917 ] Sparsity at: 0.2700864012021037 Epoch 286/500 235/235 [==============================] - 3s 13ms/step - loss: 5.8827e-06 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.40437478 -0.569443 0.37802136] Sparsity at: 0.2700864012021037 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 7.8581e-06 - accuracy: 1.0000 - val_loss: 0.1195 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.40446097 -0.5660051 0.37882906] Sparsity at: 0.2700864012021037 Epoch 288/500 235/235 [==============================] - 3s 13ms/step - loss: 9.7530e-06 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.4051946 -0.5693887 0.38016033] Sparsity at: 0.2700864012021037 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2845e-06 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.40208623 -0.5697308 0.3808913 ] Sparsity at: 0.2700864012021037 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9980e-06 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.40573612 -0.5696897 0.38126838] Sparsity at: 0.2700864012021037 Epoch 291/500 235/235 [==============================] - 3s 13ms/step - loss: 7.2127e-06 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.40287754 -0.57210827 0.38060874] Sparsity at: 0.2700864012021037 Epoch 292/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0083 - accuracy: 0.9977 - val_loss: 0.1469 - val_accuracy: 0.9766 [ 0.01880677 0. 0.03845737 ... 0.4109524 -0.6072722 0.36886907] Sparsity at: 0.2700864012021037 Epoch 293/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.1129 - val_accuracy: 0.9808 [ 0.01880677 0. 0.03845737 ... 0.41276094 -0.61180115 0.3721094 ] Sparsity at: 0.2700864012021037 Epoch 294/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1165 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.41705182 -0.6110271 0.37227574] Sparsity at: 0.2700864012021037 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5655e-04 - accuracy: 0.9999 - val_loss: 0.1122 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.41582498 -0.6111558 0.374399 ] Sparsity at: 0.2700864012021037 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0012e-04 - accuracy: 0.9999 - val_loss: 0.1150 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.4160955 -0.6085833 0.37427104] Sparsity at: 0.2700864012021037 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2332e-04 - accuracy: 0.9999 - val_loss: 0.1134 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.41404036 -0.607109 0.3792852 ] Sparsity at: 0.2700864012021037 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4397e-05 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.41415608 -0.6082279 0.3801659 ] Sparsity at: 0.2700864012021037 Epoch 299/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7002e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.41369513 -0.6079416 0.380806 ] Sparsity at: 0.2700864012021037 Epoch 300/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7661e-05 - accuracy: 1.0000 - val_loss: 0.1119 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.4144415 -0.60806644 0.37954375] Sparsity at: 0.2700864012021037 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.455179675822027 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.46342552438913387 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.7265608601560913 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 196s 12ms/step - loss: 3.0829e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.41459385 -0.6087974 0.38024697] Sparsity at: 0.2700864012021037 Epoch 302/500 235/235 [==============================] - 3s 13ms/step - loss: 4.7216e-05 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.41463572 -0.6081316 0.37883145] Sparsity at: 0.2700864012021037 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2472e-04 - accuracy: 0.9998 - val_loss: 0.1191 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.4214491 -0.6075456 0.3654542 ] Sparsity at: 0.2700864012021037 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 9.0894e-04 - accuracy: 0.9997 - val_loss: 0.1268 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.41715908 -0.5988605 0.36602002] Sparsity at: 0.2700864012021037 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.1211 - val_accuracy: 0.9801 [ 0.01880677 0. 0.03845737 ... 0.42403612 -0.58957195 0.3784302 ] Sparsity at: 0.2700864012021037 Epoch 306/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1065 - val_accuracy: 0.9840 [ 0.01880677 0. 0.03845737 ... 0.43092728 -0.57015735 0.38037157] Sparsity at: 0.2700864012021037 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3765e-04 - accuracy: 0.9998 - val_loss: 0.1138 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.437109 -0.5724672 0.37934366] Sparsity at: 0.2700864012021037 Epoch 308/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7738e-04 - accuracy: 0.9999 - val_loss: 0.1153 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.4284016 -0.5722831 0.3850294 ] Sparsity at: 0.2700864012021037 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 9.6084e-05 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.42836928 -0.57336134 0.3846535 ] Sparsity at: 0.2700864012021037 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4341e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.42907724 -0.57186526 0.3895162 ] Sparsity at: 0.2700864012021037 Epoch 311/500 235/235 [==============================] - 3s 13ms/step - loss: 1.6215e-04 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.43583128 -0.57241714 0.38954934] Sparsity at: 0.2700864012021037 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1114e-04 - accuracy: 0.9999 - val_loss: 0.1264 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.42379013 -0.57757056 0.395545 ] Sparsity at: 0.2700864012021037 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9186e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.41842535 -0.5782206 0.39567617] Sparsity at: 0.2700864012021037 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9300e-04 - accuracy: 0.9999 - val_loss: 0.1223 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.41781586 -0.5808469 0.39791888] Sparsity at: 0.2700864012021037 Epoch 315/500 235/235 [==============================] - 3s 13ms/step - loss: 3.1029e-04 - accuracy: 0.9999 - val_loss: 0.1268 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.41379616 -0.57899874 0.39824682] Sparsity at: 0.2700864012021037 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0156e-04 - accuracy: 0.9999 - val_loss: 0.1175 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.4355057 -0.5857341 0.39691567] Sparsity at: 0.2700864012021037 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7518e-04 - accuracy: 0.9999 - val_loss: 0.1190 - val_accuracy: 0.9808 [ 0.01880677 0. 0.03845737 ... 0.42032993 -0.59410816 0.41350687] Sparsity at: 0.2700864012021037 Epoch 318/500 235/235 [==============================] - 3s 13ms/step - loss: 5.9927e-04 - accuracy: 0.9998 - val_loss: 0.1221 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.4316157 -0.5942907 0.4007133 ] Sparsity at: 0.2700864012021037 Epoch 319/500 235/235 [==============================] - 3s 13ms/step - loss: 6.2144e-04 - accuracy: 0.9999 - val_loss: 0.1265 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.42972535 -0.5975518 0.40797153] Sparsity at: 0.2700864012021037 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5714e-05 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.43510303 -0.5928668 0.39725143] Sparsity at: 0.2700864012021037 Epoch 321/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4296e-04 - accuracy: 0.9999 - val_loss: 0.1188 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.43313786 -0.58579254 0.38436073] Sparsity at: 0.2700864012021037 Epoch 322/500 235/235 [==============================] - 3s 13ms/step - loss: 4.9333e-04 - accuracy: 0.9999 - val_loss: 0.1233 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.42589295 -0.6013268 0.39300346] Sparsity at: 0.2700864012021037 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5335e-04 - accuracy: 0.9999 - val_loss: 0.1250 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.42929596 -0.5873119 0.41284811] Sparsity at: 0.2700864012021037 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5569e-04 - accuracy: 0.9999 - val_loss: 0.1256 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.41426408 -0.57459337 0.4235511 ] Sparsity at: 0.2700864012021037 Epoch 325/500 235/235 [==============================] - 3s 13ms/step - loss: 4.3872e-04 - accuracy: 0.9998 - val_loss: 0.1293 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.422124 -0.58939534 0.41267574] Sparsity at: 0.2700864012021037 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1553 - val_accuracy: 0.9787 [ 0.01880677 0. 0.03845737 ... 0.42896804 -0.5908288 0.4117715 ] Sparsity at: 0.2700864012021037 Epoch 327/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4508e-04 - accuracy: 0.9998 - val_loss: 0.1311 - val_accuracy: 0.9813 [ 0.01880677 0. 0.03845737 ... 0.42904016 -0.59808546 0.41232345] Sparsity at: 0.2700864012021037 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0160e-04 - accuracy: 1.0000 - val_loss: 0.1345 - val_accuracy: 0.9807 [ 0.01880677 0. 0.03845737 ... 0.42890304 -0.60567963 0.40989468] Sparsity at: 0.2700864012021037 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2469e-04 - accuracy: 1.0000 - val_loss: 0.1419 - val_accuracy: 0.9813 [ 0.01880677 0. 0.03845737 ... 0.4193758 -0.63648313 0.42645466] Sparsity at: 0.2700864012021037 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0493e-04 - accuracy: 0.9998 - val_loss: 0.1458 - val_accuracy: 0.9795 [ 0.01880677 0. 0.03845737 ... 0.42119902 -0.61875105 0.41385153] Sparsity at: 0.2700864012021037 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4694e-04 - accuracy: 0.9998 - val_loss: 0.1353 - val_accuracy: 0.9802 [ 0.01880677 0. 0.03845737 ... 0.42231616 -0.62632924 0.41865134] Sparsity at: 0.2700864012021037 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6802e-04 - accuracy: 0.9997 - val_loss: 0.1416 - val_accuracy: 0.9801 [ 0.01880677 0. 0.03845737 ... 0.4231663 -0.61787933 0.41199622] Sparsity at: 0.2700864012021037 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 2.9512e-04 - accuracy: 0.9999 - val_loss: 0.1363 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.42298537 -0.61879206 0.41606992] Sparsity at: 0.2700864012021037 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1669e-05 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.4234758 -0.61653227 0.4089424 ] Sparsity at: 0.2700864012021037 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2091e-05 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.4228547 -0.6168121 0.4101582 ] Sparsity at: 0.2700864012021037 Epoch 336/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3946e-05 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.4233637 -0.61846846 0.4107243 ] Sparsity at: 0.2700864012021037 Epoch 337/500 235/235 [==============================] - 3s 13ms/step - loss: 4.6527e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.42081243 -0.61313814 0.40760744] Sparsity at: 0.2700864012021037 Epoch 338/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1333 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.41643348 -0.6186158 0.4007658 ] Sparsity at: 0.2700864012021037 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.1574 - val_accuracy: 0.9806 [ 0.01880677 0. 0.03845737 ... 0.43208918 -0.6476024 0.4021684 ] Sparsity at: 0.2700864012021037 Epoch 340/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1398 - val_accuracy: 0.9801 [ 0.01880677 0. 0.03845737 ... 0.4421755 -0.64383507 0.40396574] Sparsity at: 0.2700864012021037 Epoch 341/500 235/235 [==============================] - 3s 13ms/step - loss: 5.4276e-04 - accuracy: 0.9998 - val_loss: 0.1397 - val_accuracy: 0.9810 [ 0.01880677 0. 0.03845737 ... 0.44869712 -0.63456476 0.40345833] Sparsity at: 0.2700864012021037 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9778e-04 - accuracy: 0.9999 - val_loss: 0.1340 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.4479818 -0.63540125 0.4027738 ] Sparsity at: 0.2700864012021037 Epoch 343/500 235/235 [==============================] - 3s 13ms/step - loss: 7.0454e-05 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.44842836 -0.63507396 0.4027824 ] Sparsity at: 0.2700864012021037 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2659e-05 - accuracy: 1.0000 - val_loss: 0.1332 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.44942498 -0.63560736 0.4025435 ] Sparsity at: 0.2700864012021037 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2014e-05 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.44750255 -0.63570803 0.4047935 ] Sparsity at: 0.2700864012021037 Epoch 346/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4643e-05 - accuracy: 1.0000 - val_loss: 0.1356 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.44930214 -0.63584536 0.4038901 ] Sparsity at: 0.2700864012021037 Epoch 347/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8809e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.44775516 -0.6362284 0.4068559 ] Sparsity at: 0.2700864012021037 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6870e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.44722083 -0.6346979 0.4073854 ] Sparsity at: 0.2700864012021037 Epoch 349/500 235/235 [==============================] - 3s 13ms/step - loss: 1.5981e-05 - accuracy: 1.0000 - val_loss: 0.1294 - val_accuracy: 0.9815 [ 0.01880677 0. 0.03845737 ... 0.44566643 -0.63472396 0.40778485] Sparsity at: 0.2700864012021037 Epoch 350/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9011e-05 - accuracy: 1.0000 - val_loss: 0.1284 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.4450856 -0.6330681 0.40925416] Sparsity at: 0.2700864012021037 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.5644655648506856 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.5600278243738117 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.8163510335207533 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 197s 11ms/step - loss: 1.0384e-05 - accuracy: 1.0000 - val_loss: 0.1291 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.4451666 -0.633469 0.40874115] Sparsity at: 0.2700864012021037 Epoch 352/500 235/235 [==============================] - 3s 13ms/step - loss: 8.6306e-06 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9813 [ 0.01880677 0. 0.03845737 ... 0.44577155 -0.63642395 0.40825918] Sparsity at: 0.2700864012021037 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2373e-06 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9812 [ 0.01880677 0. 0.03845737 ... 0.44691172 -0.6360387 0.4089757 ] Sparsity at: 0.2700864012021037 Epoch 354/500 235/235 [==============================] - 3s 13ms/step - loss: 6.0818e-06 - accuracy: 1.0000 - val_loss: 0.1290 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.44685388 -0.6367842 0.40975323] Sparsity at: 0.2700864012021037 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2330e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.4484082 -0.63781106 0.4159827 ] Sparsity at: 0.2700864012021037 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1555 - val_accuracy: 0.9786 [ 0.01880677 0. 0.03845737 ... 0.46490914 -0.61572325 0.4567466 ] Sparsity at: 0.2700864012021037 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.1555 - val_accuracy: 0.9782 [ 0.01880677 0. 0.03845737 ... 0.43958744 -0.60434747 0.43700954] Sparsity at: 0.2700864012021037 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1367 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.44159612 -0.6271154 0.43310794] Sparsity at: 0.2700864012021037 Epoch 359/500 235/235 [==============================] - 3s 13ms/step - loss: 6.1194e-04 - accuracy: 0.9999 - val_loss: 0.1316 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.44574755 -0.6251823 0.42895037] Sparsity at: 0.2700864012021037 Epoch 360/500 235/235 [==============================] - 3s 13ms/step - loss: 6.2409e-05 - accuracy: 1.0000 - val_loss: 0.1322 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.44672886 -0.62360114 0.428358 ] Sparsity at: 0.2700864012021037 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6384e-04 - accuracy: 0.9999 - val_loss: 0.1328 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.44695884 -0.6236495 0.42847192] Sparsity at: 0.2700864012021037 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2636e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.44764778 -0.62459743 0.42766157] Sparsity at: 0.2700864012021037 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0133e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.4477151 -0.6235162 0.4275271 ] Sparsity at: 0.2700864012021037 Epoch 364/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4958e-05 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.44712654 -0.6234714 0.42802516] Sparsity at: 0.2700864012021037 Epoch 365/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3007e-05 - accuracy: 1.0000 - val_loss: 0.1305 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.44765165 -0.6232419 0.4275212 ] Sparsity at: 0.2700864012021037 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6517e-05 - accuracy: 1.0000 - val_loss: 0.1293 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.44750014 -0.6245654 0.4277241 ] Sparsity at: 0.2700864012021037 Epoch 367/500 235/235 [==============================] - 3s 13ms/step - loss: 4.0779e-05 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.44877905 -0.6284608 0.42834488] Sparsity at: 0.2700864012021037 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5644e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.4506028 -0.62678146 0.42664927] Sparsity at: 0.2700864012021037 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4897e-05 - accuracy: 1.0000 - val_loss: 0.1309 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.45197487 -0.62553996 0.42520854] Sparsity at: 0.2700864012021037 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0477e-05 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.45133063 -0.62686414 0.42566523] Sparsity at: 0.2700864012021037 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8011e-04 - accuracy: 0.9999 - val_loss: 0.1409 - val_accuracy: 0.9801 [ 0.01880677 0. 0.03845737 ... 0.4507499 -0.6270846 0.418127 ] Sparsity at: 0.2700864012021037 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 8.3508e-04 - accuracy: 0.9998 - val_loss: 0.1442 - val_accuracy: 0.9802 [ 0.01880677 0. 0.03845737 ... 0.44715053 -0.6429228 0.43147108] Sparsity at: 0.2700864012021037 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.1411 - val_accuracy: 0.9800 [ 0.01880677 0. 0.03845737 ... 0.43295485 -0.6394391 0.41250584] Sparsity at: 0.2700864012021037 Epoch 374/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1417 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.44535258 -0.62315625 0.39839727] Sparsity at: 0.2700864012021037 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5793e-04 - accuracy: 0.9999 - val_loss: 0.1343 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.4467874 -0.6337322 0.3953345 ] Sparsity at: 0.2700864012021037 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9581e-04 - accuracy: 0.9999 - val_loss: 0.1335 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.44663742 -0.63570327 0.39670038] Sparsity at: 0.2700864012021037 Epoch 377/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7601e-04 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.44553074 -0.6353643 0.3965377 ] Sparsity at: 0.2700864012021037 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0936e-05 - accuracy: 1.0000 - val_loss: 0.1325 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.44526246 -0.6364899 0.39647025] Sparsity at: 0.2700864012021037 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5624e-05 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.44557664 -0.637094 0.39603493] Sparsity at: 0.2700864012021037 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6198e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.45825878 -0.63764393 0.39865378] Sparsity at: 0.2700864012021037 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7758e-04 - accuracy: 0.9999 - val_loss: 0.1307 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.4549328 -0.6383177 0.39185613] Sparsity at: 0.2700864012021037 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5026e-04 - accuracy: 0.9998 - val_loss: 0.1344 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.4535509 -0.63780546 0.39373192] Sparsity at: 0.2700864012021037 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6417e-04 - accuracy: 0.9999 - val_loss: 0.1314 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.44011182 -0.6319519 0.4048404 ] Sparsity at: 0.2700864012021037 Epoch 384/500 235/235 [==============================] - 3s 13ms/step - loss: 5.0730e-04 - accuracy: 0.9998 - val_loss: 0.1335 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.46084502 -0.64427954 0.38993615] Sparsity at: 0.2700864012021037 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8395e-04 - accuracy: 0.9999 - val_loss: 0.1381 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.45769942 -0.64657545 0.3885706 ] Sparsity at: 0.2700864012021037 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1189e-04 - accuracy: 0.9999 - val_loss: 0.1475 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.45612776 -0.64769167 0.39585263] Sparsity at: 0.2700864012021037 Epoch 387/500 235/235 [==============================] - 3s 13ms/step - loss: 6.9048e-04 - accuracy: 0.9998 - val_loss: 0.1431 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.4588586 -0.6370141 0.40369546] Sparsity at: 0.2700864012021037 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7104e-04 - accuracy: 0.9998 - val_loss: 0.1424 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.45682585 -0.6313816 0.40460625] Sparsity at: 0.2700864012021037 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6342e-04 - accuracy: 0.9999 - val_loss: 0.1369 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.4650648 -0.6638783 0.40846455] Sparsity at: 0.2700864012021037 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4489e-05 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.46627247 -0.65998715 0.40710875] Sparsity at: 0.2700864012021037 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8615e-05 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.46676907 -0.6613538 0.40647185] Sparsity at: 0.2700864012021037 Epoch 392/500 235/235 [==============================] - 3s 13ms/step - loss: 9.0562e-06 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.4667732 -0.66145897 0.40652075] Sparsity at: 0.2700864012021037 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5482e-06 - accuracy: 1.0000 - val_loss: 0.1346 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.46673197 -0.66031224 0.40658796] Sparsity at: 0.2700864012021037 Epoch 394/500 235/235 [==============================] - 4s 15ms/step - loss: 9.7914e-05 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.46717578 -0.66075 0.4064628 ] Sparsity at: 0.2700864012021037 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7885e-04 - accuracy: 0.9998 - val_loss: 0.1548 - val_accuracy: 0.9802 [ 0.01880677 0. 0.03845737 ... 0.46122706 -0.64909434 0.41022262] Sparsity at: 0.2700864012021037 Epoch 396/500 235/235 [==============================] - 3s 13ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1685 - val_accuracy: 0.9775 [ 0.01880677 0. 0.03845737 ... 0.4667297 -0.63820547 0.4229265 ] Sparsity at: 0.2700864012021037 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1427 - val_accuracy: 0.9804 [ 0.01880677 0. 0.03845737 ... 0.47011888 -0.6622189 0.40988645] Sparsity at: 0.2700864012021037 Epoch 398/500 235/235 [==============================] - 3s 13ms/step - loss: 8.1614e-04 - accuracy: 0.9998 - val_loss: 0.1446 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.46479094 -0.6591441 0.42159024] Sparsity at: 0.2700864012021037 Epoch 399/500 235/235 [==============================] - 3s 13ms/step - loss: 1.2917e-04 - accuracy: 1.0000 - val_loss: 0.1448 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.46502146 -0.6647157 0.4207599 ] Sparsity at: 0.2700864012021037 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2641e-05 - accuracy: 1.0000 - val_loss: 0.1457 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.46341294 -0.666411 0.42230028] Sparsity at: 0.2700864012021037 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.6572577147876544 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25390732 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [1. 1. 1. ... 1. 1. 1.] ... [0. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 0.] [0. 1. 0. ... 0. 1. 1.]], shape=(784, 300), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.6440891058018465 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.40593332 tf.Tensor( [[1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [1. 0. 1. ... 1. 1. 0.] ... [1. 1. 0. ... 1. 0. 1.] [0. 0. 1. ... 1. 1. 0.] [0. 0. 1. ... 1. 1. 0.]], shape=(300, 100), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.8803838511902526 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]], shape=(100, 10), dtype=float32) 235/235 [==============================] - 197s 11ms/step - loss: 2.0607e-05 - accuracy: 1.0000 - val_loss: 0.1446 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.46633345 -0.66804427 0.41892812] Sparsity at: 0.2700864012021037 Epoch 402/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9554e-05 - accuracy: 1.0000 - val_loss: 0.1437 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.46652657 -0.668023 0.41829896] Sparsity at: 0.2700864012021037 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0368e-05 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.46724027 -0.66870284 0.4183195 ] Sparsity at: 0.2700864012021037 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 9.5840e-06 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.4663525 -0.668626 0.4193142 ] Sparsity at: 0.2700864012021037 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6389e-06 - accuracy: 1.0000 - val_loss: 0.1422 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.4665143 -0.66899633 0.41944754] Sparsity at: 0.2700864012021037 Epoch 406/500 235/235 [==============================] - 3s 13ms/step - loss: 9.0062e-06 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.46860546 -0.6689246 0.4184126 ] Sparsity at: 0.2700864012021037 Epoch 407/500 235/235 [==============================] - 3s 13ms/step - loss: 8.1056e-06 - accuracy: 1.0000 - val_loss: 0.1426 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.46873325 -0.6685833 0.4183288 ] Sparsity at: 0.2700864012021037 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4066e-06 - accuracy: 1.0000 - val_loss: 0.1420 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.46880314 -0.6681805 0.41848987] Sparsity at: 0.2700864012021037 Epoch 409/500 235/235 [==============================] - 3s 13ms/step - loss: 3.7950e-04 - accuracy: 0.9999 - val_loss: 0.1561 - val_accuracy: 0.9810 [ 0.01880677 0. 0.03845737 ... 0.45084572 -0.6686814 0.43604264] Sparsity at: 0.2700864012021037 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9755e-04 - accuracy: 0.9998 - val_loss: 0.1550 - val_accuracy: 0.9816 [ 0.01880677 0. 0.03845737 ... 0.4656313 -0.6752855 0.40138668] Sparsity at: 0.2700864012021037 Epoch 411/500 235/235 [==============================] - 3s 13ms/step - loss: 6.2412e-04 - accuracy: 0.9998 - val_loss: 0.1466 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.4507388 -0.6743006 0.41448167] Sparsity at: 0.2700864012021037 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 8.8058e-04 - accuracy: 0.9997 - val_loss: 0.1463 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.45489672 -0.69769186 0.40985057] Sparsity at: 0.2700864012021037 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1358 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.45985702 -0.68309146 0.41062984] Sparsity at: 0.2700864012021037 Epoch 414/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9374e-04 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.45656508 -0.69363266 0.41802064] Sparsity at: 0.2700864012021037 Epoch 415/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3520e-04 - accuracy: 0.9999 - val_loss: 0.1323 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.47364724 -0.69193727 0.3999162 ] Sparsity at: 0.2700864012021037 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3129e-04 - accuracy: 0.9999 - val_loss: 0.1365 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.4739784 -0.68859583 0.3998263 ] Sparsity at: 0.2700864012021037 Epoch 417/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3219e-04 - accuracy: 0.9999 - val_loss: 0.1390 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.47405094 -0.6805354 0.40050626] Sparsity at: 0.2700864012021037 Epoch 418/500 235/235 [==============================] - 3s 13ms/step - loss: 1.7869e-04 - accuracy: 0.9999 - val_loss: 0.1406 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.46566617 -0.6805592 0.40232405] Sparsity at: 0.2700864012021037 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6626e-05 - accuracy: 1.0000 - val_loss: 0.1407 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.46778002 -0.68003017 0.40077406] Sparsity at: 0.2700864012021037 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4312e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.46637502 -0.6790302 0.4022436 ] Sparsity at: 0.2700864012021037 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6845e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.4661699 -0.68192846 0.40519503] Sparsity at: 0.2700864012021037 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3993e-04 - accuracy: 0.9999 - val_loss: 0.1360 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.46617717 -0.6790349 0.40467882] Sparsity at: 0.2700864012021037 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4790e-04 - accuracy: 0.9999 - val_loss: 0.1387 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.46748582 -0.67802894 0.40360326] Sparsity at: 0.2700864012021037 Epoch 424/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9054e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.46735564 -0.67733115 0.40376613] Sparsity at: 0.2700864012021037 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1974e-05 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.4680344 -0.67662156 0.40240416] Sparsity at: 0.2700864012021037 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7721e-06 - accuracy: 1.0000 - val_loss: 0.1373 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.4682882 -0.6771541 0.4021718 ] Sparsity at: 0.2700864012021037 Epoch 427/500 235/235 [==============================] - 3s 13ms/step - loss: 9.5601e-06 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.4677433 -0.6776685 0.40427023] Sparsity at: 0.2700864012021037 Epoch 428/500 235/235 [==============================] - 3s 13ms/step - loss: 5.7319e-06 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.46762228 -0.67821455 0.40410846] Sparsity at: 0.2700864012021037 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3334e-06 - accuracy: 1.0000 - val_loss: 0.1357 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.467608 -0.6782383 0.40366098] Sparsity at: 0.2700864012021037 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8042e-06 - accuracy: 1.0000 - val_loss: 0.1353 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.46754295 -0.67817485 0.40394324] Sparsity at: 0.2700864012021037 Epoch 431/500 235/235 [==============================] - 3s 13ms/step - loss: 2.7058e-06 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.46759093 -0.6779699 0.4040676 ] Sparsity at: 0.2700864012021037 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8740e-04 - accuracy: 0.9999 - val_loss: 0.1466 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.46744943 -0.66644967 0.40588087] Sparsity at: 0.2700864012021037 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1524 - val_accuracy: 0.9813 [ 0.01880677 0. 0.03845737 ... 0.46097827 -0.62711006 0.42067784] Sparsity at: 0.2700864012021037 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.1414 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.45657557 -0.6273581 0.4250402 ] Sparsity at: 0.2700864012021037 Epoch 435/500 235/235 [==============================] - 3s 13ms/step - loss: 3.4893e-04 - accuracy: 0.9999 - val_loss: 0.1398 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.45802897 -0.6266265 0.42477885] Sparsity at: 0.2700864012021037 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2441e-04 - accuracy: 0.9999 - val_loss: 0.1402 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.45604822 -0.62752724 0.43428692] Sparsity at: 0.2700864012021037 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 6.8691e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9840 [ 0.01880677 0. 0.03845737 ... 0.45472613 -0.6288736 0.4348841 ] Sparsity at: 0.2700864012021037 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8456e-05 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.45594355 -0.62880594 0.43710533] Sparsity at: 0.2700864012021037 Epoch 439/500 235/235 [==============================] - 3s 15ms/step - loss: 9.6716e-05 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.45650694 -0.62691766 0.43600905] Sparsity at: 0.2700864012021037 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4640e-04 - accuracy: 0.9999 - val_loss: 0.1408 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.45526773 -0.63491094 0.4365719 ] Sparsity at: 0.2700864012021037 Epoch 441/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4208e-04 - accuracy: 0.9999 - val_loss: 0.1438 - val_accuracy: 0.9838 [ 0.01880677 0. 0.03845737 ... 0.45614707 -0.6313941 0.4328075 ] Sparsity at: 0.2700864012021037 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7247e-05 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9842 [ 0.01880677 0. 0.03845737 ... 0.45721295 -0.6321107 0.43130693] Sparsity at: 0.2700864012021037 Epoch 443/500 235/235 [==============================] - 3s 13ms/step - loss: 1.3336e-05 - accuracy: 1.0000 - val_loss: 0.1438 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.45744625 -0.6324652 0.4301266 ] Sparsity at: 0.2700864012021037 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 9.2265e-06 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9839 [ 0.01880677 0. 0.03845737 ... 0.4575293 -0.63369817 0.43058583] Sparsity at: 0.2700864012021037 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4377e-04 - accuracy: 0.9999 - val_loss: 0.1509 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.4552686 -0.65081006 0.43637168] Sparsity at: 0.2700864012021037 Epoch 446/500 235/235 [==============================] - 3s 13ms/step - loss: 1.9970e-04 - accuracy: 0.9999 - val_loss: 0.1427 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.45342895 -0.6588442 0.43469203] Sparsity at: 0.2700864012021037 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7455e-04 - accuracy: 0.9999 - val_loss: 0.1465 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.45570192 -0.6619532 0.43477646] Sparsity at: 0.2700864012021037 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1448 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.47522023 -0.6305926 0.42582753] Sparsity at: 0.2700864012021037 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1343 - val_accuracy: 0.9835 [ 0.01880677 0. 0.03845737 ... 0.4954192 -0.6285819 0.4669497 ] Sparsity at: 0.2700864012021037 Epoch 450/500 235/235 [==============================] - 3s 13ms/step - loss: 8.5876e-04 - accuracy: 0.9997 - val_loss: 0.1420 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.50079274 -0.6303393 0.4709089 ] Sparsity at: 0.2700864012021037 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1441e-04 - accuracy: 0.9998 - val_loss: 0.1381 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.49741262 -0.62992424 0.47417143] Sparsity at: 0.2700864012021037 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3354e-04 - accuracy: 0.9999 - val_loss: 0.1448 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.49561664 -0.62809485 0.4776332 ] Sparsity at: 0.2700864012021037 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6338e-04 - accuracy: 0.9999 - val_loss: 0.1417 - val_accuracy: 0.9826 [ 0.01880677 0. 0.03845737 ... 0.49396494 -0.6300975 0.48053837] Sparsity at: 0.2700864012021037 Epoch 454/500 235/235 [==============================] - 3s 13ms/step - loss: 2.3473e-04 - accuracy: 0.9999 - val_loss: 0.1367 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.49057928 -0.6315608 0.4860551 ] Sparsity at: 0.2700864012021037 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6786e-05 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.49089608 -0.63261527 0.48420486] Sparsity at: 0.2700864012021037 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5470e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.4890302 -0.6310676 0.48348197] Sparsity at: 0.2700864012021037 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2836e-05 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9830 [ 0.01880677 0. 0.03845737 ... 0.4887562 -0.6263034 0.48337683] Sparsity at: 0.2700864012021037 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6432e-04 - accuracy: 0.9999 - val_loss: 0.1344 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.48778415 -0.6281333 0.4847315 ] Sparsity at: 0.2700864012021037 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6053e-05 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.48816496 -0.6256419 0.48476303] Sparsity at: 0.2700864012021037 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 8.3425e-06 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.4886472 -0.6258442 0.4841313 ] Sparsity at: 0.2700864012021037 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1966e-06 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.48854548 -0.62605673 0.48446417] Sparsity at: 0.2700864012021037 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0274e-06 - accuracy: 1.0000 - val_loss: 0.1315 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.48861292 -0.6263668 0.48482335] Sparsity at: 0.2700864012021037 Epoch 463/500 235/235 [==============================] - 3s 14ms/step - loss: 6.7343e-06 - accuracy: 1.0000 - val_loss: 0.1314 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.48862886 -0.6252509 0.48466864] Sparsity at: 0.2700864012021037 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4802e-06 - accuracy: 1.0000 - val_loss: 0.1306 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.48871908 -0.62547314 0.48476958] Sparsity at: 0.2700864012021037 Epoch 465/500 235/235 [==============================] - 3s 13ms/step - loss: 3.8455e-06 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.48870808 -0.6255478 0.4848354 ] Sparsity at: 0.2700864012021037 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 3.3227e-06 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9833 [ 0.01880677 0. 0.03845737 ... 0.48873988 -0.6258117 0.48489904] Sparsity at: 0.2700864012021037 Epoch 467/500 235/235 [==============================] - 3s 13ms/step - loss: 3.3459e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9834 [ 0.01880677 0. 0.03845737 ... 0.4889535 -0.6255376 0.4848146 ] Sparsity at: 0.2700864012021037 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2425e-06 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.4885305 -0.62568027 0.4854094 ] Sparsity at: 0.2700864012021037 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7768e-06 - accuracy: 1.0000 - val_loss: 0.1301 - val_accuracy: 0.9832 [ 0.01880677 0. 0.03845737 ... 0.4881956 -0.62559664 0.4857285 ] Sparsity at: 0.2700864012021037 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3299e-06 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9837 [ 0.01880677 0. 0.03845737 ... 0.48870555 -0.6255467 0.4857981 ] Sparsity at: 0.2700864012021037 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3181e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.4888284 -0.6260802 0.48551163] Sparsity at: 0.2700864012021037 Epoch 472/500 235/235 [==============================] - 4s 16ms/step - loss: 3.5649e-06 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9841 [ 0.01880677 0. 0.03845737 ... 0.48941073 -0.6271921 0.48552984] Sparsity at: 0.2700864012021037 Epoch 473/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6748e-06 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9836 [ 0.01880677 0. 0.03845737 ... 0.48932803 -0.6267637 0.48558357] Sparsity at: 0.2700864012021037 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3095e-04 - accuracy: 0.9998 - val_loss: 0.1749 - val_accuracy: 0.9794 [ 0.01880677 0. 0.03845737 ... 0.4655318 -0.628347 0.46190992] Sparsity at: 0.2700864012021037 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0050 - accuracy: 0.9985 - val_loss: 0.1691 - val_accuracy: 0.9798 [ 0.01880677 0. 0.03845737 ... 0.44827434 -0.62281805 0.5273579 ] Sparsity at: 0.2700864012021037 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1462 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.44287345 -0.61887336 0.52848494] Sparsity at: 0.2700864012021037 Epoch 477/500 235/235 [==============================] - 3s 13ms/step - loss: 2.5482e-04 - accuracy: 0.9999 - val_loss: 0.1387 - val_accuracy: 0.9828 [ 0.01880677 0. 0.03845737 ... 0.44191244 -0.6289943 0.52140516] Sparsity at: 0.2700864012021037 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 6.6499e-05 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.44295457 -0.62999314 0.52239364] Sparsity at: 0.2700864012021037 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4158e-05 - accuracy: 1.0000 - val_loss: 0.1402 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.44192117 -0.6276317 0.5260622 ] Sparsity at: 0.2700864012021037 Epoch 480/500 235/235 [==============================] - 3s 13ms/step - loss: 2.1252e-05 - accuracy: 1.0000 - val_loss: 0.1412 - val_accuracy: 0.9823 [ 0.01880677 0. 0.03845737 ... 0.44181022 -0.62668484 0.52317286] Sparsity at: 0.2700864012021037 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3044e-05 - accuracy: 1.0000 - val_loss: 0.1410 - val_accuracy: 0.9827 [ 0.01880677 0. 0.03845737 ... 0.44262424 -0.6269376 0.52284724] Sparsity at: 0.2700864012021037 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3125e-05 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.44242978 -0.627432 0.5216963 ] Sparsity at: 0.2700864012021037 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4894e-05 - accuracy: 1.0000 - val_loss: 0.1416 - val_accuracy: 0.9825 [ 0.01880677 0. 0.03845737 ... 0.4431408 -0.6280628 0.52082515] Sparsity at: 0.2700864012021037 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 4.4950e-05 - accuracy: 1.0000 - val_loss: 0.1415 - val_accuracy: 0.9821 [ 0.01880677 0. 0.03845737 ... 0.43784878 -0.6195985 0.52076095] Sparsity at: 0.2700864012021037 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8787e-05 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.43878213 -0.6349239 0.5280176 ] Sparsity at: 0.2700864012021037 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7190e-05 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9829 [ 0.01880677 0. 0.03845737 ... 0.43720216 -0.6323217 0.5261143 ] Sparsity at: 0.2700864012021037 Epoch 487/500 235/235 [==============================] - 3s 13ms/step - loss: 8.4527e-06 - accuracy: 1.0000 - val_loss: 0.1404 - val_accuracy: 0.9831 [ 0.01880677 0. 0.03845737 ... 0.4384575 -0.63301456 0.5239528 ] Sparsity at: 0.2700864012021037 Epoch 488/500 235/235 [==============================] - 3s 13ms/step - loss: 4.4184e-04 - accuracy: 0.9999 - val_loss: 0.1496 - val_accuracy: 0.9819 [ 0.01880677 0. 0.03845737 ... 0.44965163 -0.63156843 0.50300837] Sparsity at: 0.2700864012021037 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.1647 - val_accuracy: 0.9796 [ 0.01880677 0. 0.03845737 ... 0.43609804 -0.65148854 0.50748736] Sparsity at: 0.2700864012021037 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.1490 - val_accuracy: 0.9808 [ 0.01880677 0. 0.03845737 ... 0.44622424 -0.65381765 0.5002455 ] Sparsity at: 0.2700864012021037 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3510e-04 - accuracy: 0.9999 - val_loss: 0.1443 - val_accuracy: 0.9806 [ 0.01880677 0. 0.03845737 ... 0.44121972 -0.6252421 0.49803033] Sparsity at: 0.2700864012021037 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7316e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.44149444 -0.62498707 0.49824798] Sparsity at: 0.2700864012021037 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3295e-05 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9817 [ 0.01880677 0. 0.03845737 ... 0.4419989 -0.6275225 0.49887785] Sparsity at: 0.2700864012021037 Epoch 494/500 235/235 [==============================] - 3s 13ms/step - loss: 1.4205e-04 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9814 [ 0.01880677 0. 0.03845737 ... 0.44184676 -0.6266536 0.50754625] Sparsity at: 0.2700864012021037 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5516e-05 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.44270357 -0.6250824 0.5069428 ] Sparsity at: 0.2700864012021037 Epoch 496/500 235/235 [==============================] - 3s 13ms/step - loss: 9.3779e-06 - accuracy: 1.0000 - val_loss: 0.1433 - val_accuracy: 0.9818 [ 0.01880677 0. 0.03845737 ... 0.4431729 -0.62458086 0.50672305] Sparsity at: 0.2700864012021037 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6809e-06 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.44329783 -0.6244296 0.50652224] Sparsity at: 0.2700864012021037 Epoch 498/500 235/235 [==============================] - 3s 13ms/step - loss: 4.5708e-06 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9820 [ 0.01880677 0. 0.03845737 ... 0.44338268 -0.62531406 0.5064558 ] Sparsity at: 0.2700864012021037 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6651e-06 - accuracy: 1.0000 - val_loss: 0.1421 - val_accuracy: 0.9822 [ 0.01880677 0. 0.03845737 ... 0.44348463 -0.62528926 0.5063269 ] Sparsity at: 0.2700864012021037 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1423e-06 - accuracy: 1.0000 - val_loss: 0.1427 - val_accuracy: 0.9824 [ 0.01880677 0. 0.03845737 ... 0.44315782 -0.62628734 0.5062329 ] Sparsity at: 0.2700864012021037 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.0419277586042881 Thresholhold 0.02126331627368927 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08924054354429245 Thresholhold 0.07131436467170715 Using suggest threshold. Applying new mask Percentage zeros 0.40124512 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 0. 0.] ... [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10948323458433151 Thresholhold -0.12435988336801529 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:45 - loss: 4.5111 - accuracy: 0.1133WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0054s vs `on_train_batch_begin` time: 2.4816s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 1.6686 - accuracy: 0.8455 - val_loss: 1.0195 - val_accuracy: 0.8994 [-8.9834053e-08 0.0000000e+00 2.7637500e-07 ... 9.2959017e-02 -2.2156824e-01 1.0482026e-01] Sparsity at: 0.2684582886266094 Epoch 2/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9634 - accuracy: 0.8940 - val_loss: 0.9065 - val_accuracy: 0.8988 [-3.0853472e-13 0.0000000e+00 2.2770566e-13 ... 8.2338721e-02 -2.3112498e-01 4.4672038e-02] Sparsity at: 0.2684582886266094 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9122 - accuracy: 0.8944 - val_loss: 0.8852 - val_accuracy: 0.8966 [-1.7760507e-18 0.0000000e+00 8.2230149e-19 ... 7.8650504e-02 -2.2880547e-01 7.9721259e-03] Sparsity at: 0.2684582886266094 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8972 - accuracy: 0.8944 - val_loss: 0.8747 - val_accuracy: 0.8967 [-9.8042710e-24 0.0000000e+00 -1.8378601e-23 ... 7.7959441e-02 -2.2215100e-01 -1.5425724e-02] Sparsity at: 0.2684582886266094 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8888 - accuracy: 0.8942 - val_loss: 0.8683 - val_accuracy: 0.8967 [-3.9132513e-29 0.0000000e+00 1.3306845e-28 ... 7.8732900e-02 -2.1589056e-01 -3.1906340e-02] Sparsity at: 0.2684582886266094 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8837 - accuracy: 0.8942 - val_loss: 0.8639 - val_accuracy: 0.8967 [-5.4838758e-34 0.0000000e+00 7.2596075e-34 ... 7.9592131e-02 -2.1179301e-01 -4.4010080e-02] Sparsity at: 0.2684582886266094 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8798 - accuracy: 0.8944 - val_loss: 0.8598 - val_accuracy: 0.8969 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.0336444e-02 -2.0890974e-01 -5.2624092e-02] Sparsity at: 0.2684582886266094 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8768 - accuracy: 0.8945 - val_loss: 0.8564 - val_accuracy: 0.8984 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.0848403e-02 -2.0676634e-01 -5.8968142e-02] Sparsity at: 0.2684582886266094 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8743 - accuracy: 0.8950 - val_loss: 0.8543 - val_accuracy: 0.8990 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.1109725e-02 -2.0475410e-01 -6.3569792e-02] Sparsity at: 0.2684582886266094 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8724 - accuracy: 0.8952 - val_loss: 0.8520 - val_accuracy: 0.8991 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.1877403e-02 -2.0312461e-01 -6.7121670e-02] Sparsity at: 0.2684582886266094 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8707 - accuracy: 0.8955 - val_loss: 0.8504 - val_accuracy: 0.8998 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.2827128e-02 -2.0135188e-01 -7.0030734e-02] Sparsity at: 0.2684582886266094 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8696 - accuracy: 0.8955 - val_loss: 0.8496 - val_accuracy: 0.8998 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.3823629e-02 -1.9927554e-01 -7.2568446e-02] Sparsity at: 0.2684582886266094 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8685 - accuracy: 0.8957 - val_loss: 0.8482 - val_accuracy: 0.8992 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.5966997e-02 -1.9705325e-01 -7.4851796e-02] Sparsity at: 0.2684582886266094 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8676 - accuracy: 0.8962 - val_loss: 0.8474 - val_accuracy: 0.8999 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 8.7693244e-02 -1.9499266e-01 -7.6929368e-02] Sparsity at: 0.2684582886266094 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8667 - accuracy: 0.8964 - val_loss: 0.8465 - val_accuracy: 0.9001 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 9.0563200e-02 -1.9279966e-01 -7.9493463e-02] Sparsity at: 0.2684582886266094 Epoch 16/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8661 - accuracy: 0.8961 - val_loss: 0.8459 - val_accuracy: 0.9006 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 9.3673728e-02 -1.9049768e-01 -8.0761567e-02] Sparsity at: 0.2684582886266094 Epoch 17/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8652 - accuracy: 0.8964 - val_loss: 0.8455 - val_accuracy: 0.9001 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 9.7277142e-02 -1.8852895e-01 -8.2394883e-02] Sparsity at: 0.2684582886266094 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8653 - accuracy: 0.8963 - val_loss: 0.8449 - val_accuracy: 0.9008 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.0137876e-01 -1.8637373e-01 -8.3329752e-02] Sparsity at: 0.2684582886266094 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8645 - accuracy: 0.8965 - val_loss: 0.8449 - val_accuracy: 0.9009 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.0583045e-01 -1.8402047e-01 -8.3982341e-02] Sparsity at: 0.2684582886266094 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8643 - accuracy: 0.8963 - val_loss: 0.8445 - val_accuracy: 0.9006 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.1039498e-01 -1.8128894e-01 -8.4096767e-02] Sparsity at: 0.2684582886266094 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8641 - accuracy: 0.8964 - val_loss: 0.8443 - val_accuracy: 0.9006 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.1552964e-01 -1.7875883e-01 -8.4102675e-02] Sparsity at: 0.2684582886266094 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8637 - accuracy: 0.8967 - val_loss: 0.8440 - val_accuracy: 0.9006 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2074105e-01 -1.7593881e-01 -8.3336130e-02] Sparsity at: 0.2684582886266094 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8632 - accuracy: 0.8967 - val_loss: 0.8438 - val_accuracy: 0.9008 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2579715e-01 -1.7305377e-01 -8.2584061e-02] Sparsity at: 0.2684582886266094 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8632 - accuracy: 0.8968 - val_loss: 0.8433 - val_accuracy: 0.9009 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3128863e-01 -1.7029977e-01 -8.1688486e-02] Sparsity at: 0.2684582886266094 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8630 - accuracy: 0.8970 - val_loss: 0.8431 - val_accuracy: 0.9010 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3630304e-01 -1.6688475e-01 -8.0261007e-02] Sparsity at: 0.2684582886266094 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8627 - accuracy: 0.8967 - val_loss: 0.8433 - val_accuracy: 0.9011 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.4104909e-01 -1.6343622e-01 -7.8545250e-02] Sparsity at: 0.2684582886266094 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8625 - accuracy: 0.8969 - val_loss: 0.8430 - val_accuracy: 0.9013 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.4602126e-01 -1.6011302e-01 -7.6961733e-02] Sparsity at: 0.2684582886266094 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8625 - accuracy: 0.8970 - val_loss: 0.8426 - val_accuracy: 0.9011 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5008494e-01 -1.5658461e-01 -7.5381815e-02] Sparsity at: 0.2684582886266094 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8621 - accuracy: 0.8968 - val_loss: 0.8422 - val_accuracy: 0.9012 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5344375e-01 -1.5296414e-01 -7.3594399e-02] Sparsity at: 0.2684582886266094 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8974 - val_loss: 0.8421 - val_accuracy: 0.9009 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5623417e-01 -1.4959928e-01 -7.2117716e-02] Sparsity at: 0.2684582886266094 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8971 - val_loss: 0.8416 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5851764e-01 -1.4622952e-01 -7.0940711e-02] Sparsity at: 0.2684582886266094 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8972 - val_loss: 0.8413 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5999149e-01 -1.4289366e-01 -6.9045879e-02] Sparsity at: 0.2684582886266094 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8612 - accuracy: 0.8972 - val_loss: 0.8414 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.6096839e-01 -1.3972487e-01 -6.7543022e-02] Sparsity at: 0.2684582886266094 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8612 - accuracy: 0.8970 - val_loss: 0.8416 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.6125178e-01 -1.3688794e-01 -6.6141121e-02] Sparsity at: 0.2684582886266094 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8612 - accuracy: 0.8971 - val_loss: 0.8410 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.6116372e-01 -1.3457474e-01 -6.4589985e-02] Sparsity at: 0.2684582886266094 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8608 - accuracy: 0.8974 - val_loss: 0.8415 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.6038267e-01 -1.3253972e-01 -6.2733725e-02] Sparsity at: 0.2684582886266094 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8607 - accuracy: 0.8973 - val_loss: 0.8411 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5946534e-01 -1.3117670e-01 -6.0914136e-02] Sparsity at: 0.2684582886266094 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8606 - accuracy: 0.8972 - val_loss: 0.8412 - val_accuracy: 0.9012 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5804388e-01 -1.3005283e-01 -5.8641482e-02] Sparsity at: 0.2684582886266094 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8605 - accuracy: 0.8972 - val_loss: 0.8409 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5634952e-01 -1.2936707e-01 -5.6192875e-02] Sparsity at: 0.2684582886266094 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8604 - accuracy: 0.8974 - val_loss: 0.8409 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5422808e-01 -1.2921877e-01 -5.3835955e-02] Sparsity at: 0.2684582886266094 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8603 - accuracy: 0.8976 - val_loss: 0.8406 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.5216966e-01 -1.3010690e-01 -5.1113464e-02] Sparsity at: 0.2684582886266094 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8600 - accuracy: 0.8974 - val_loss: 0.8406 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.4935975e-01 -1.3109179e-01 -4.8321776e-02] Sparsity at: 0.2684582886266094 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8601 - accuracy: 0.8972 - val_loss: 0.8406 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.4664097e-01 -1.3282675e-01 -4.5363359e-02] Sparsity at: 0.2684582886266094 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8598 - accuracy: 0.8974 - val_loss: 0.8403 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.4355481e-01 -1.3472095e-01 -4.2140409e-02] Sparsity at: 0.2684582886266094 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8600 - accuracy: 0.8975 - val_loss: 0.8397 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.4051218e-01 -1.3746266e-01 -3.9190140e-02] Sparsity at: 0.2684582886266094 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8596 - accuracy: 0.8979 - val_loss: 0.8401 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3740715e-01 -1.4030695e-01 -3.6161326e-02] Sparsity at: 0.2684582886266094 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8979 - val_loss: 0.8403 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3381296e-01 -1.4316659e-01 -3.3144969e-02] Sparsity at: 0.2684582886266094 Epoch 48/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8595 - accuracy: 0.8976 - val_loss: 0.8398 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3106842e-01 -1.4616594e-01 -3.0530335e-02] Sparsity at: 0.2684582886266094 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8594 - accuracy: 0.8975 - val_loss: 0.8398 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2804312e-01 -1.4871861e-01 -2.7732907e-02] Sparsity at: 0.2684582886266094 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8595 - accuracy: 0.8974 - val_loss: 0.8394 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2547843e-01 -1.5147868e-01 -2.5067046e-02] Sparsity at: 0.2684582886266094 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.0036432837180088717 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.019940239056950748 Thresholhold 0.10994073003530502 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [1. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [1. 1. 0. ... 0. 0. 0.] [1. 1. 0. ... 1. 0. 1.] [1. 0. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.11557942384274611 Thresholhold -0.10227083414793015 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 48s 7ms/step - loss: 0.8600 - accuracy: 0.8976 - val_loss: 0.8400 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2234274e-01 -1.5507537e-01 -2.4513166e-02] Sparsity at: 0.3020553916309013 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8596 - accuracy: 0.8978 - val_loss: 0.8398 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2294033e-01 -1.5856490e-01 -2.5542196e-02] Sparsity at: 0.3020553916309013 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8594 - accuracy: 0.8979 - val_loss: 0.8398 - val_accuracy: 0.9029 [-5.48387584e-34 0.00000000e+00 5.09155001e-34 ... 1.24105774e-01 -1.61454782e-01 -2.71752495e-02] Sparsity at: 0.3020553916309013 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8595 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2485599e-01 -1.6371562e-01 -2.8881321e-02] Sparsity at: 0.3020553916309013 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8595 - accuracy: 0.8976 - val_loss: 0.8397 - val_accuracy: 0.9030 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2569897e-01 -1.6562264e-01 -3.0724384e-02] Sparsity at: 0.3020553916309013 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8595 - accuracy: 0.8978 - val_loss: 0.8393 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2636068e-01 -1.6714354e-01 -3.2255009e-02] Sparsity at: 0.3020553916309013 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8396 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2696125e-01 -1.6858852e-01 -3.3573054e-02] Sparsity at: 0.3020553916309013 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8976 - val_loss: 0.8399 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2746310e-01 -1.6980223e-01 -3.4823522e-02] Sparsity at: 0.3020553916309013 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8594 - accuracy: 0.8977 - val_loss: 0.8396 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2792858e-01 -1.7073731e-01 -3.5746925e-02] Sparsity at: 0.3020553916309013 Epoch 60/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8978 - val_loss: 0.8397 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2836723e-01 -1.7152169e-01 -3.6618531e-02] Sparsity at: 0.3020553916309013 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8393 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2874204e-01 -1.7206110e-01 -3.7384976e-02] Sparsity at: 0.3020553916309013 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8397 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2877426e-01 -1.7272711e-01 -3.7892692e-02] Sparsity at: 0.3020553916309013 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8397 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2884371e-01 -1.7304236e-01 -3.8338814e-02] Sparsity at: 0.3020553916309013 Epoch 64/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8593 - accuracy: 0.8975 - val_loss: 0.8396 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2930056e-01 -1.7367229e-01 -3.8727719e-02] Sparsity at: 0.3020553916309013 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2944511e-01 -1.7363392e-01 -3.9231349e-02] Sparsity at: 0.3020553916309013 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8977 - val_loss: 0.8394 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2966843e-01 -1.7406814e-01 -3.9438769e-02] Sparsity at: 0.3020553916309013 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8976 - val_loss: 0.8394 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2981075e-01 -1.7430477e-01 -3.9743174e-02] Sparsity at: 0.3020553916309013 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3019060e-01 -1.7477098e-01 -4.0095720e-02] Sparsity at: 0.3020553916309013 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8395 - val_accuracy: 0.9033 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3001566e-01 -1.7477211e-01 -4.0001515e-02] Sparsity at: 0.3020553916309013 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8979 - val_loss: 0.8394 - val_accuracy: 0.9031 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3010490e-01 -1.7500022e-01 -4.0189072e-02] Sparsity at: 0.3020553916309013 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3003357e-01 -1.7517424e-01 -4.0266704e-02] Sparsity at: 0.3020553916309013 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8975 - val_loss: 0.8395 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3006732e-01 -1.7537594e-01 -4.0378712e-02] Sparsity at: 0.3020553916309013 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3020296e-01 -1.7545526e-01 -4.0520452e-02] Sparsity at: 0.3020553916309013 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8978 - val_loss: 0.8393 - val_accuracy: 0.9034 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3042489e-01 -1.7538187e-01 -4.0536780e-02] Sparsity at: 0.3020553916309013 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8396 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3049713e-01 -1.7596348e-01 -4.0737361e-02] Sparsity at: 0.3020553916309013 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8977 - val_loss: 0.8394 - val_accuracy: 0.9034 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3060771e-01 -1.7591920e-01 -4.0577546e-02] Sparsity at: 0.3020553916309013 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8593 - accuracy: 0.8978 - val_loss: 0.8393 - val_accuracy: 0.9030 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3076569e-01 -1.7591909e-01 -4.0690858e-02] Sparsity at: 0.3020553916309013 Epoch 78/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8395 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3076486e-01 -1.7612094e-01 -4.0782146e-02] Sparsity at: 0.3020553916309013 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8977 - val_loss: 0.8393 - val_accuracy: 0.9031 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3081729e-01 -1.7613031e-01 -4.0732473e-02] Sparsity at: 0.3020553916309013 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8390 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3082041e-01 -1.7621996e-01 -4.0636394e-02] Sparsity at: 0.3020553916309013 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8394 - val_accuracy: 0.9033 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3098948e-01 -1.7644405e-01 -4.0798191e-02] Sparsity at: 0.3020553916309013 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8978 - val_loss: 0.8392 - val_accuracy: 0.9031 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3130747e-01 -1.7655924e-01 -4.0756479e-02] Sparsity at: 0.3020553916309013 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8976 - val_loss: 0.8393 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3120663e-01 -1.7677036e-01 -4.0497895e-02] Sparsity at: 0.3020553916309013 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8979 - val_loss: 0.8395 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3121243e-01 -1.7697068e-01 -4.0852841e-02] Sparsity at: 0.3020553916309013 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8981 - val_loss: 0.8394 - val_accuracy: 0.9033 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3165174e-01 -1.7680240e-01 -4.0843982e-02] Sparsity at: 0.3020553916309013 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8980 - val_loss: 0.8398 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3138409e-01 -1.7695697e-01 -4.0686388e-02] Sparsity at: 0.3020553916309013 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8979 - val_loss: 0.8394 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3159584e-01 -1.7705370e-01 -4.0659670e-02] Sparsity at: 0.3020553916309013 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8975 - val_loss: 0.8394 - val_accuracy: 0.9030 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3142163e-01 -1.7730904e-01 -4.0503614e-02] Sparsity at: 0.3020553916309013 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3187335e-01 -1.7745581e-01 -4.0574327e-02] Sparsity at: 0.3020553916309013 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8592 - accuracy: 0.8974 - val_loss: 0.8399 - val_accuracy: 0.9031 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3147616e-01 -1.7752194e-01 -4.0517319e-02] Sparsity at: 0.3020553916309013 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8979 - val_loss: 0.8393 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3184576e-01 -1.7747264e-01 -4.0603787e-02] Sparsity at: 0.3020553916309013 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8975 - val_loss: 0.8396 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3190538e-01 -1.7765263e-01 -4.0478241e-02] Sparsity at: 0.3020553916309013 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8589 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9031 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3190360e-01 -1.7768240e-01 -4.0426746e-02] Sparsity at: 0.3020553916309013 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8977 - val_loss: 0.8393 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3179721e-01 -1.7752849e-01 -4.0478081e-02] Sparsity at: 0.3020553916309013 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8396 - val_accuracy: 0.9033 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3203646e-01 -1.7793046e-01 -4.0302433e-02] Sparsity at: 0.3020553916309013 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8593 - accuracy: 0.8974 - val_loss: 0.8397 - val_accuracy: 0.9032 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3222447e-01 -1.7781563e-01 -4.0426910e-02] Sparsity at: 0.3020553916309013 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8592 - accuracy: 0.8977 - val_loss: 0.8392 - val_accuracy: 0.9031 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3222960e-01 -1.7816213e-01 -4.0201418e-02] Sparsity at: 0.3020553916309013 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8590 - accuracy: 0.8978 - val_loss: 0.8395 - val_accuracy: 0.9038 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3217404e-01 -1.7805555e-01 -4.0291063e-02] Sparsity at: 0.3020553916309013 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8591 - accuracy: 0.8976 - val_loss: 0.8395 - val_accuracy: 0.9033 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3215074e-01 -1.7794009e-01 -4.0262833e-02] Sparsity at: 0.3020553916309013 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8591 - accuracy: 0.8977 - val_loss: 0.8395 - val_accuracy: 0.9036 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3230026e-01 -1.7811580e-01 -4.0080838e-02] Sparsity at: 0.3020553916309013 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.009493211909383348 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.03640017389897343 Thresholhold 0.10112528502941132 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.13820159512233054 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 46s 7ms/step - loss: 0.8635 - accuracy: 0.8981 - val_loss: 0.8433 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2821461e-01 -1.8314569e-01 -3.8852900e-02] Sparsity at: 0.31764686158798283 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8625 - accuracy: 0.8977 - val_loss: 0.8432 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2682478e-01 -1.8493056e-01 -4.0374514e-02] Sparsity at: 0.31764686158798283 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8623 - accuracy: 0.8976 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2700060e-01 -1.8594225e-01 -4.1738406e-02] Sparsity at: 0.31764686158798283 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8623 - accuracy: 0.8974 - val_loss: 0.8429 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2713227e-01 -1.8635452e-01 -4.2957414e-02] Sparsity at: 0.31764686158798283 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8622 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2701347e-01 -1.8701567e-01 -4.3908153e-02] Sparsity at: 0.31764686158798283 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8622 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2704466e-01 -1.8764000e-01 -4.4646252e-02] Sparsity at: 0.31764686158798283 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2732610e-01 -1.8808383e-01 -4.5259889e-02] Sparsity at: 0.31764686158798283 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2724626e-01 -1.8865944e-01 -4.5633864e-02] Sparsity at: 0.31764686158798283 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8621 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2751086e-01 -1.8911141e-01 -4.6109665e-02] Sparsity at: 0.31764686158798283 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2767273e-01 -1.8939529e-01 -4.6456531e-02] Sparsity at: 0.31764686158798283 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8620 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2795898e-01 -1.8991999e-01 -4.6820179e-02] Sparsity at: 0.31764686158798283 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2815933e-01 -1.9008371e-01 -4.7333226e-02] Sparsity at: 0.31764686158798283 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2815264e-01 -1.9031869e-01 -4.7475237e-02] Sparsity at: 0.31764686158798283 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2849016e-01 -1.9082949e-01 -4.7659192e-02] Sparsity at: 0.31764686158798283 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2845236e-01 -1.9101778e-01 -4.7793601e-02] Sparsity at: 0.31764686158798283 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2865999e-01 -1.9145867e-01 -4.8006583e-02] Sparsity at: 0.31764686158798283 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2885745e-01 -1.9148713e-01 -4.8114952e-02] Sparsity at: 0.31764686158798283 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2908526e-01 -1.9163844e-01 -4.8208833e-02] Sparsity at: 0.31764686158798283 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8619 - accuracy: 0.8976 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2897655e-01 -1.9180900e-01 -4.8430927e-02] Sparsity at: 0.31764686158798283 Epoch 120/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2923451e-01 -1.9227915e-01 -4.8585393e-02] Sparsity at: 0.31764686158798283 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2933771e-01 -1.9254167e-01 -4.8641730e-02] Sparsity at: 0.31764686158798283 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2950709e-01 -1.9256245e-01 -4.8787158e-02] Sparsity at: 0.31764686158798283 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2952569e-01 -1.9285975e-01 -4.8726380e-02] Sparsity at: 0.31764686158798283 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8619 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2968600e-01 -1.9273987e-01 -4.8867244e-02] Sparsity at: 0.31764686158798283 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2951250e-01 -1.9285864e-01 -4.8959654e-02] Sparsity at: 0.31764686158798283 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2962729e-01 -1.9309379e-01 -4.9072322e-02] Sparsity at: 0.31764686158798283 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.2979282e-01 -1.9322781e-01 -4.8913687e-02] Sparsity at: 0.31764686158798283 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3003753e-01 -1.9343826e-01 -4.8896972e-02] Sparsity at: 0.31764686158798283 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3030161e-01 -1.9369686e-01 -4.9045946e-02] Sparsity at: 0.31764686158798283 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3017318e-01 -1.9368851e-01 -4.8820440e-02] Sparsity at: 0.31764686158798283 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3038777e-01 -1.9370711e-01 -4.8804794e-02] Sparsity at: 0.31764686158798283 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3031024e-01 -1.9387725e-01 -4.8685513e-02] Sparsity at: 0.31764686158798283 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3041861e-01 -1.9399741e-01 -4.8672013e-02] Sparsity at: 0.31764686158798283 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8619 - accuracy: 0.8977 - val_loss: 0.8429 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3065943e-01 -1.9398069e-01 -4.8678558e-02] Sparsity at: 0.31764686158798283 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3075496e-01 -1.9422056e-01 -4.8466679e-02] Sparsity at: 0.31764686158798283 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3086253e-01 -1.9430256e-01 -4.8664290e-02] Sparsity at: 0.31764686158798283 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3089217e-01 -1.9445390e-01 -4.8425332e-02] Sparsity at: 0.31764686158798283 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3113028e-01 -1.9454832e-01 -4.8509661e-02] Sparsity at: 0.31764686158798283 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3124588e-01 -1.9458926e-01 -4.8391465e-02] Sparsity at: 0.31764686158798283 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3110988e-01 -1.9467093e-01 -4.8345648e-02] Sparsity at: 0.31764686158798283 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3116015e-01 -1.9473970e-01 -4.8383031e-02] Sparsity at: 0.31764686158798283 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8975 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3114993e-01 -1.9499183e-01 -4.8224635e-02] Sparsity at: 0.31764686158798283 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8429 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3117220e-01 -1.9513275e-01 -4.8479598e-02] Sparsity at: 0.31764686158798283 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8978 - val_loss: 0.8429 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3135523e-01 -1.9529510e-01 -4.8400950e-02] Sparsity at: 0.31764686158798283 Epoch 145/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3129324e-01 -1.9500449e-01 -4.8331078e-02] Sparsity at: 0.31764686158798283 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3116494e-01 -1.9516598e-01 -4.8521806e-02] Sparsity at: 0.31764686158798283 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3150419e-01 -1.9525765e-01 -4.8552930e-02] Sparsity at: 0.31764686158798283 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3143615e-01 -1.9552180e-01 -4.8496269e-02] Sparsity at: 0.31764686158798283 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8982 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3129306e-01 -1.9555634e-01 -4.8402920e-02] Sparsity at: 0.31764686158798283 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8976 - val_loss: 0.8427 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3145301e-01 -1.9553630e-01 -4.8637949e-02] Sparsity at: 0.31764686158798283 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.016370235658226484 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.05085836911255104 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.16201439394987815 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3134664e-01 -1.9551423e-01 -4.8543576e-02] Sparsity at: 0.31764686158798283 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3151520e-01 -1.9551814e-01 -4.8416600e-02] Sparsity at: 0.31764686158798283 Epoch 153/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3142766e-01 -1.9558457e-01 -4.8482835e-02] Sparsity at: 0.31764686158798283 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3142197e-01 -1.9570747e-01 -4.8660621e-02] Sparsity at: 0.31764686158798283 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3162774e-01 -1.9576450e-01 -4.8666112e-02] Sparsity at: 0.31764686158798283 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3142668e-01 -1.9564982e-01 -4.8768625e-02] Sparsity at: 0.31764686158798283 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3154729e-01 -1.9586788e-01 -4.8639391e-02] Sparsity at: 0.31764686158798283 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8982 - val_loss: 0.8428 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3161272e-01 -1.9610412e-01 -4.8663873e-02] Sparsity at: 0.31764686158798283 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3153143e-01 -1.9595259e-01 -4.8596386e-02] Sparsity at: 0.31764686158798283 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8620 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3161562e-01 -1.9610885e-01 -4.8720330e-02] Sparsity at: 0.31764686158798283 Epoch 161/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3173233e-01 -1.9615996e-01 -4.8737977e-02] Sparsity at: 0.31764686158798283 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3177030e-01 -1.9607089e-01 -4.8769373e-02] Sparsity at: 0.31764686158798283 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3174957e-01 -1.9621418e-01 -4.8929773e-02] Sparsity at: 0.31764686158798283 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8429 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3174346e-01 -1.9631459e-01 -4.8848152e-02] Sparsity at: 0.31764686158798283 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3175415e-01 -1.9614850e-01 -4.8962120e-02] Sparsity at: 0.31764686158798283 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3163190e-01 -1.9631489e-01 -4.8884977e-02] Sparsity at: 0.31764686158798283 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3167328e-01 -1.9637337e-01 -4.8946764e-02] Sparsity at: 0.31764686158798283 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3179286e-01 -1.9639114e-01 -4.8989117e-02] Sparsity at: 0.31764686158798283 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3153571e-01 -1.9633736e-01 -4.8856497e-02] Sparsity at: 0.31764686158798283 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3183625e-01 -1.9655170e-01 -4.8994616e-02] Sparsity at: 0.31764686158798283 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3153484e-01 -1.9667366e-01 -4.8872393e-02] Sparsity at: 0.31764686158798283 Epoch 172/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9009 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3174793e-01 -1.9650687e-01 -4.8859589e-02] Sparsity at: 0.31764686158798283 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3166814e-01 -1.9660209e-01 -4.9039505e-02] Sparsity at: 0.31764686158798283 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3182704e-01 -1.9655381e-01 -4.9036797e-02] Sparsity at: 0.31764686158798283 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3161987e-01 -1.9652063e-01 -4.8913248e-02] Sparsity at: 0.31764686158798283 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8423 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3184595e-01 -1.9665773e-01 -4.8893366e-02] Sparsity at: 0.31764686158798283 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3185468e-01 -1.9673279e-01 -4.9085606e-02] Sparsity at: 0.31764686158798283 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3192044e-01 -1.9671328e-01 -4.9127519e-02] Sparsity at: 0.31764686158798283 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3185282e-01 -1.9672896e-01 -4.8897829e-02] Sparsity at: 0.31764686158798283 Epoch 180/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8429 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3186932e-01 -1.9679476e-01 -4.9031869e-02] Sparsity at: 0.31764686158798283 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3190877e-01 -1.9668463e-01 -4.9033135e-02] Sparsity at: 0.31764686158798283 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3218339e-01 -1.9688039e-01 -4.9073666e-02] Sparsity at: 0.31764686158798283 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3201900e-01 -1.9676551e-01 -4.9048178e-02] Sparsity at: 0.31764686158798283 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3201238e-01 -1.9678928e-01 -4.9263485e-02] Sparsity at: 0.31764686158798283 Epoch 185/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8429 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3194270e-01 -1.9678959e-01 -4.9410645e-02] Sparsity at: 0.31764686158798283 Epoch 186/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3205931e-01 -1.9683908e-01 -4.9072742e-02] Sparsity at: 0.31764686158798283 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3212754e-01 -1.9685809e-01 -4.9272075e-02] Sparsity at: 0.31764686158798283 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3204212e-01 -1.9705476e-01 -4.9155395e-02] Sparsity at: 0.31764686158798283 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3212602e-01 -1.9687542e-01 -4.9248464e-02] Sparsity at: 0.31764686158798283 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3209842e-01 -1.9694383e-01 -4.9141664e-02] Sparsity at: 0.31764686158798283 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3217551e-01 -1.9706658e-01 -4.9254362e-02] Sparsity at: 0.31764686158798283 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3203961e-01 -1.9724111e-01 -4.9024723e-02] Sparsity at: 0.31764686158798283 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3202311e-01 -1.9700061e-01 -4.9173471e-02] Sparsity at: 0.31764686158798283 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3214070e-01 -1.9711721e-01 -4.9211733e-02] Sparsity at: 0.31764686158798283 Epoch 195/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3231492e-01 -1.9711117e-01 -4.9273893e-02] Sparsity at: 0.31764686158798283 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.483876e-34 0.000000e+00 5.091550e-34 ... 1.323446e-01 -1.971135e-01 -4.927989e-02] Sparsity at: 0.31764686158798283 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9014 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3214421e-01 -1.9714426e-01 -4.9130607e-02] Sparsity at: 0.31764686158798283 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8982 - val_loss: 0.8424 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3242665e-01 -1.9718294e-01 -4.9162071e-02] Sparsity at: 0.31764686158798283 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3222337e-01 -1.9710758e-01 -4.8979387e-02] Sparsity at: 0.31764686158798283 Epoch 200/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3225886e-01 -1.9716600e-01 -4.8887614e-02] Sparsity at: 0.31764686158798283 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.02556332363108682 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.08065675018447038 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.187723324082814 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 64s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3235617e-01 -1.9716236e-01 -4.8922386e-02] Sparsity at: 0.31764686158798283 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3233872e-01 -1.9742027e-01 -4.8925292e-02] Sparsity at: 0.31764686158798283 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249913e-01 -1.9743334e-01 -4.8945550e-02] Sparsity at: 0.31764686158798283 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247161e-01 -1.9742592e-01 -4.8983023e-02] Sparsity at: 0.31764686158798283 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247986e-01 -1.9758783e-01 -4.9187984e-02] Sparsity at: 0.31764686158798283 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9020 [-5.483876e-34 0.000000e+00 5.091550e-34 ... 1.325217e-01 -1.974552e-01 -4.928371e-02] Sparsity at: 0.31764686158798283 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3253118e-01 -1.9760072e-01 -4.9105503e-02] Sparsity at: 0.31764686158798283 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3233891e-01 -1.9747142e-01 -4.9214002e-02] Sparsity at: 0.31764686158798283 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3243431e-01 -1.9754511e-01 -4.8991591e-02] Sparsity at: 0.31764686158798283 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251102e-01 -1.9762695e-01 -4.9119323e-02] Sparsity at: 0.31764686158798283 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247992e-01 -1.9771177e-01 -4.9114339e-02] Sparsity at: 0.31764686158798283 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9026 [-5.483876e-34 0.000000e+00 5.091550e-34 ... 1.324988e-01 -1.973929e-01 -4.904706e-02] Sparsity at: 0.31764686158798283 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3240026e-01 -1.9747585e-01 -4.9096629e-02] Sparsity at: 0.31764686158798283 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254574e-01 -1.9746101e-01 -4.9201634e-02] Sparsity at: 0.31764686158798283 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268796e-01 -1.9764492e-01 -4.9154725e-02] Sparsity at: 0.31764686158798283 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3244972e-01 -1.9766811e-01 -4.9133420e-02] Sparsity at: 0.31764686158798283 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8429 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3246946e-01 -1.9776979e-01 -4.9316298e-02] Sparsity at: 0.31764686158798283 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250221e-01 -1.9760148e-01 -4.9325477e-02] Sparsity at: 0.31764686158798283 Epoch 219/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251768e-01 -1.9784541e-01 -4.9305931e-02] Sparsity at: 0.31764686158798283 Epoch 220/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3245976e-01 -1.9786319e-01 -4.9312707e-02] Sparsity at: 0.31764686158798283 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8983 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255097e-01 -1.9776452e-01 -4.9196705e-02] Sparsity at: 0.31764686158798283 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3253079e-01 -1.9779831e-01 -4.9270019e-02] Sparsity at: 0.31764686158798283 Epoch 223/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258941e-01 -1.9777648e-01 -4.9184009e-02] Sparsity at: 0.31764686158798283 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3260470e-01 -1.9770518e-01 -4.9405504e-02] Sparsity at: 0.31764686158798283 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252379e-01 -1.9772010e-01 -4.9401361e-02] Sparsity at: 0.31764686158798283 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8975 - val_loss: 0.8423 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3241419e-01 -1.9771178e-01 -4.9378376e-02] Sparsity at: 0.31764686158798283 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269220e-01 -1.9773848e-01 -4.9295675e-02] Sparsity at: 0.31764686158798283 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3248172e-01 -1.9782944e-01 -4.9281776e-02] Sparsity at: 0.31764686158798283 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265231e-01 -1.9786410e-01 -4.9408652e-02] Sparsity at: 0.31764686158798283 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254000e-01 -1.9780730e-01 -4.9428076e-02] Sparsity at: 0.31764686158798283 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254124e-01 -1.9786313e-01 -4.9348202e-02] Sparsity at: 0.31764686158798283 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8982 - val_loss: 0.8427 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268690e-01 -1.9782664e-01 -4.9521375e-02] Sparsity at: 0.31764686158798283 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3239712e-01 -1.9786432e-01 -4.9626920e-02] Sparsity at: 0.31764686158798283 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258721e-01 -1.9775945e-01 -4.9586155e-02] Sparsity at: 0.31764686158798283 Epoch 235/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3236269e-01 -1.9794369e-01 -4.9515229e-02] Sparsity at: 0.31764686158798283 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256113e-01 -1.9780158e-01 -4.9612332e-02] Sparsity at: 0.31764686158798283 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8428 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3253745e-01 -1.9798046e-01 -4.9473926e-02] Sparsity at: 0.31764686158798283 Epoch 238/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254429e-01 -1.9770826e-01 -4.9647328e-02] Sparsity at: 0.31764686158798283 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257693e-01 -1.9768004e-01 -4.9613215e-02] Sparsity at: 0.31764686158798283 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259612e-01 -1.9777218e-01 -4.9532041e-02] Sparsity at: 0.31764686158798283 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3238288e-01 -1.9778067e-01 -4.9674653e-02] Sparsity at: 0.31764686158798283 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249508e-01 -1.9765589e-01 -4.9639314e-02] Sparsity at: 0.31764686158798283 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265821e-01 -1.9788790e-01 -4.9675032e-02] Sparsity at: 0.31764686158798283 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3270748e-01 -1.9804683e-01 -4.9469050e-02] Sparsity at: 0.31764686158798283 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251804e-01 -1.9787075e-01 -4.9816035e-02] Sparsity at: 0.31764686158798283 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8423 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3264535e-01 -1.9795381e-01 -4.9637474e-02] Sparsity at: 0.31764686158798283 Epoch 247/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8614 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261926e-01 -1.9790334e-01 -4.9785260e-02] Sparsity at: 0.31764686158798283 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3273104e-01 -1.9783448e-01 -4.9817681e-02] Sparsity at: 0.31764686158798283 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259253e-01 -1.9799730e-01 -4.9647469e-02] Sparsity at: 0.31764686158798283 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251796e-01 -1.9777572e-01 -4.9746413e-02] Sparsity at: 0.31764686158798283 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.03626223626598213 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.11141459069796689 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.21360910653589116 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 54s 7ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257369e-01 -1.9780327e-01 -4.9655702e-02] Sparsity at: 0.31764686158798283 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256332e-01 -1.9803447e-01 -4.9606662e-02] Sparsity at: 0.31764686158798283 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247232e-01 -1.9796558e-01 -4.9524635e-02] Sparsity at: 0.31764686158798283 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3240048e-01 -1.9791499e-01 -4.9514562e-02] Sparsity at: 0.31764686158798283 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261023e-01 -1.9786955e-01 -4.9532466e-02] Sparsity at: 0.31764686158798283 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261232e-01 -1.9786939e-01 -4.9699165e-02] Sparsity at: 0.31764686158798283 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258256e-01 -1.9794552e-01 -4.9770962e-02] Sparsity at: 0.31764686158798283 Epoch 258/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265368e-01 -1.9798878e-01 -4.9749810e-02] Sparsity at: 0.31764686158798283 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9013 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250917e-01 -1.9787462e-01 -4.9729265e-02] Sparsity at: 0.31764686158798283 Epoch 260/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250373e-01 -1.9791879e-01 -4.9724240e-02] Sparsity at: 0.31764686158798283 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258451e-01 -1.9802029e-01 -4.9740311e-02] Sparsity at: 0.31764686158798283 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8983 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269940e-01 -1.9808505e-01 -4.9911484e-02] Sparsity at: 0.31764686158798283 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274360e-01 -1.9800594e-01 -4.9683839e-02] Sparsity at: 0.31764686158798283 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256845e-01 -1.9799311e-01 -4.9821161e-02] Sparsity at: 0.31764686158798283 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268915e-01 -1.9769433e-01 -4.9656745e-02] Sparsity at: 0.31764686158798283 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247445e-01 -1.9794540e-01 -4.9711458e-02] Sparsity at: 0.31764686158798283 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3273451e-01 -1.9806153e-01 -4.9621224e-02] Sparsity at: 0.31764686158798283 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255194e-01 -1.9782656e-01 -4.9603172e-02] Sparsity at: 0.31764686158798283 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250780e-01 -1.9794939e-01 -4.9568966e-02] Sparsity at: 0.31764686158798283 Epoch 270/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256982e-01 -1.9803466e-01 -4.9794700e-02] Sparsity at: 0.31764686158798283 Epoch 271/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9014 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269734e-01 -1.9795600e-01 -4.9889129e-02] Sparsity at: 0.31764686158798283 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269490e-01 -1.9803166e-01 -4.9882852e-02] Sparsity at: 0.31764686158798283 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254073e-01 -1.9798416e-01 -4.9768340e-02] Sparsity at: 0.31764686158798283 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258573e-01 -1.9799423e-01 -4.9929056e-02] Sparsity at: 0.31764686158798283 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3238344e-01 -1.9812205e-01 -4.9770564e-02] Sparsity at: 0.31764686158798283 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249350e-01 -1.9816700e-01 -4.9689785e-02] Sparsity at: 0.31764686158798283 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3230267e-01 -1.9795969e-01 -4.9668603e-02] Sparsity at: 0.31764686158798283 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259347e-01 -1.9789292e-01 -4.9734324e-02] Sparsity at: 0.31764686158798283 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261445e-01 -1.9810584e-01 -4.9681913e-02] Sparsity at: 0.31764686158798283 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256408e-01 -1.9816026e-01 -4.9610984e-02] Sparsity at: 0.31764686158798283 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8982 - val_loss: 0.8425 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262101e-01 -1.9791436e-01 -4.9821641e-02] Sparsity at: 0.31764686158798283 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254182e-01 -1.9801253e-01 -4.9684793e-02] Sparsity at: 0.31764686158798283 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3279141e-01 -1.9812328e-01 -4.9740724e-02] Sparsity at: 0.31764686158798283 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265823e-01 -1.9802296e-01 -4.9998369e-02] Sparsity at: 0.31764686158798283 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3283135e-01 -1.9795346e-01 -5.0070040e-02] Sparsity at: 0.31764686158798283 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3281339e-01 -1.9809550e-01 -4.9847193e-02] Sparsity at: 0.31764686158798283 Epoch 287/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269632e-01 -1.9811673e-01 -4.9940422e-02] Sparsity at: 0.31764686158798283 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267826e-01 -1.9803974e-01 -4.9987052e-02] Sparsity at: 0.31764686158798283 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8619 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265364e-01 -1.9791408e-01 -4.9788188e-02] Sparsity at: 0.31764686158798283 Epoch 290/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251366e-01 -1.9786063e-01 -4.9811009e-02] Sparsity at: 0.31764686158798283 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262077e-01 -1.9799949e-01 -4.9862377e-02] Sparsity at: 0.31764686158798283 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259126e-01 -1.9800738e-01 -4.9696483e-02] Sparsity at: 0.31764686158798283 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252182e-01 -1.9808300e-01 -4.9704488e-02] Sparsity at: 0.31764686158798283 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3264091e-01 -1.9794048e-01 -4.9898226e-02] Sparsity at: 0.31764686158798283 Epoch 295/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3273513e-01 -1.9788145e-01 -4.9966913e-02] Sparsity at: 0.31764686158798283 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252199e-01 -1.9790380e-01 -4.9957853e-02] Sparsity at: 0.31764686158798283 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3242649e-01 -1.9798808e-01 -4.9913958e-02] Sparsity at: 0.31764686158798283 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267371e-01 -1.9784570e-01 -4.9937833e-02] Sparsity at: 0.31764686158798283 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268983e-01 -1.9785890e-01 -4.9913019e-02] Sparsity at: 0.31764686158798283 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3266930e-01 -1.9813108e-01 -5.0035678e-02] Sparsity at: 0.31764686158798283 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.04788359900957806 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.13539425739688404 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.2351152310006963 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 50s 7ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3278276e-01 -1.9795179e-01 -4.9800307e-02] Sparsity at: 0.31764686158798283 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250822e-01 -1.9795927e-01 -4.9746864e-02] Sparsity at: 0.31764686158798283 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250214e-01 -1.9803071e-01 -4.9839821e-02] Sparsity at: 0.31764686158798283 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258010e-01 -1.9806913e-01 -4.9885873e-02] Sparsity at: 0.31764686158798283 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3266473e-01 -1.9804579e-01 -4.9949896e-02] Sparsity at: 0.31764686158798283 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8429 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3275422e-01 -1.9803868e-01 -5.0110999e-02] Sparsity at: 0.31764686158798283 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3272098e-01 -1.9797283e-01 -5.0021850e-02] Sparsity at: 0.31764686158798283 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3275847e-01 -1.9795921e-01 -4.9938034e-02] Sparsity at: 0.31764686158798283 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269009e-01 -1.9785550e-01 -4.9979270e-02] Sparsity at: 0.31764686158798283 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3270719e-01 -1.9799496e-01 -4.9963541e-02] Sparsity at: 0.31764686158798283 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274992e-01 -1.9787772e-01 -5.0076935e-02] Sparsity at: 0.31764686158798283 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3275257e-01 -1.9794948e-01 -4.9907103e-02] Sparsity at: 0.31764686158798283 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8428 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259248e-01 -1.9797005e-01 -5.0076805e-02] Sparsity at: 0.31764686158798283 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3266964e-01 -1.9787095e-01 -4.9846541e-02] Sparsity at: 0.31764686158798283 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8421 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262784e-01 -1.9790511e-01 -4.9979873e-02] Sparsity at: 0.31764686158798283 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256787e-01 -1.9802929e-01 -4.9989499e-02] Sparsity at: 0.31764686158798283 Epoch 317/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3248563e-01 -1.9809888e-01 -4.9851183e-02] Sparsity at: 0.31764686158798283 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257951e-01 -1.9792271e-01 -5.0012995e-02] Sparsity at: 0.31764686158798283 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8423 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3253632e-01 -1.9804838e-01 -5.0026283e-02] Sparsity at: 0.31764686158798283 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3241871e-01 -1.9820940e-01 -4.9835708e-02] Sparsity at: 0.31764686158798283 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268398e-01 -1.9788592e-01 -4.9785346e-02] Sparsity at: 0.31764686158798283 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256554e-01 -1.9773345e-01 -4.9814887e-02] Sparsity at: 0.31764686158798283 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3260059e-01 -1.9801638e-01 -4.9924187e-02] Sparsity at: 0.31764686158798283 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250667e-01 -1.9792512e-01 -4.9900260e-02] Sparsity at: 0.31764686158798283 Epoch 325/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8428 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247307e-01 -1.9793144e-01 -4.9950205e-02] Sparsity at: 0.31764686158798283 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257244e-01 -1.9798738e-01 -4.9880147e-02] Sparsity at: 0.31764686158798283 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3266009e-01 -1.9806498e-01 -4.9752679e-02] Sparsity at: 0.31764686158798283 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3246007e-01 -1.9803892e-01 -4.9954880e-02] Sparsity at: 0.31764686158798283 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262451e-01 -1.9799948e-01 -4.9768053e-02] Sparsity at: 0.31764686158798283 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3242239e-01 -1.9808955e-01 -4.9986999e-02] Sparsity at: 0.31764686158798283 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3270764e-01 -1.9804269e-01 -5.0109550e-02] Sparsity at: 0.31764686158798283 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254161e-01 -1.9808698e-01 -4.9990784e-02] Sparsity at: 0.31764686158798283 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254024e-01 -1.9804102e-01 -5.0128985e-02] Sparsity at: 0.31764686158798283 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257775e-01 -1.9807452e-01 -4.9898531e-02] Sparsity at: 0.31764686158798283 Epoch 335/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3253900e-01 -1.9809070e-01 -4.9916886e-02] Sparsity at: 0.31764686158798283 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256250e-01 -1.9801477e-01 -5.0011158e-02] Sparsity at: 0.31764686158798283 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3237356e-01 -1.9791655e-01 -4.9787626e-02] Sparsity at: 0.31764686158798283 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3270876e-01 -1.9800150e-01 -4.9976308e-02] Sparsity at: 0.31764686158798283 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263313e-01 -1.9797869e-01 -4.9882732e-02] Sparsity at: 0.31764686158798283 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8614 - accuracy: 0.8981 - val_loss: 0.8423 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3278721e-01 -1.9805530e-01 -4.9979366e-02] Sparsity at: 0.31764686158798283 Epoch 341/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263096e-01 -1.9798875e-01 -4.9881052e-02] Sparsity at: 0.31764686158798283 Epoch 342/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3270178e-01 -1.9800828e-01 -4.9856909e-02] Sparsity at: 0.31764686158798283 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267998e-01 -1.9799435e-01 -5.0002381e-02] Sparsity at: 0.31764686158798283 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3271005e-01 -1.9807129e-01 -5.0144494e-02] Sparsity at: 0.31764686158798283 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262835e-01 -1.9798715e-01 -5.0095156e-02] Sparsity at: 0.31764686158798283 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274355e-01 -1.9808359e-01 -5.0110567e-02] Sparsity at: 0.31764686158798283 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274935e-01 -1.9809730e-01 -5.0057184e-02] Sparsity at: 0.31764686158798283 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8422 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250378e-01 -1.9798309e-01 -4.9946167e-02] Sparsity at: 0.31764686158798283 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3238361e-01 -1.9800623e-01 -4.9975187e-02] Sparsity at: 0.31764686158798283 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3264570e-01 -1.9787477e-01 -5.0022580e-02] Sparsity at: 0.31764686158798283 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.05873832177777283 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.15064661322034212 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.2532106425008891 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 51s 7ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265680e-01 -1.9808239e-01 -4.9933340e-02] Sparsity at: 0.31764686158798283 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265838e-01 -1.9794565e-01 -5.0004542e-02] Sparsity at: 0.31764686158798283 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3272992e-01 -1.9801296e-01 -5.0048936e-02] Sparsity at: 0.31764686158798283 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8428 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256115e-01 -1.9823101e-01 -4.9993545e-02] Sparsity at: 0.31764686158798283 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3272852e-01 -1.9798085e-01 -5.0036993e-02] Sparsity at: 0.31764686158798283 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8976 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3260914e-01 -1.9804540e-01 -4.9952053e-02] Sparsity at: 0.31764686158798283 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268028e-01 -1.9809811e-01 -4.9953248e-02] Sparsity at: 0.31764686158798283 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267979e-01 -1.9790269e-01 -5.0109535e-02] Sparsity at: 0.31764686158798283 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269076e-01 -1.9800615e-01 -4.9854103e-02] Sparsity at: 0.31764686158798283 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268243e-01 -1.9816689e-01 -5.0042354e-02] Sparsity at: 0.31764686158798283 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274419e-01 -1.9789301e-01 -4.9959507e-02] Sparsity at: 0.31764686158798283 Epoch 362/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3277349e-01 -1.9807650e-01 -4.9992546e-02] Sparsity at: 0.31764686158798283 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8423 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262415e-01 -1.9814546e-01 -4.9951889e-02] Sparsity at: 0.31764686158798283 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259934e-01 -1.9793811e-01 -4.9808122e-02] Sparsity at: 0.31764686158798283 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3244286e-01 -1.9788468e-01 -4.9903709e-02] Sparsity at: 0.31764686158798283 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261224e-01 -1.9790162e-01 -4.9907438e-02] Sparsity at: 0.31764686158798283 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247515e-01 -1.9799717e-01 -4.9878392e-02] Sparsity at: 0.31764686158798283 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3260734e-01 -1.9792810e-01 -4.9924057e-02] Sparsity at: 0.31764686158798283 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247943e-01 -1.9792208e-01 -4.9863584e-02] Sparsity at: 0.31764686158798283 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3237037e-01 -1.9799241e-01 -4.9948093e-02] Sparsity at: 0.31764686158798283 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267656e-01 -1.9793609e-01 -4.9962562e-02] Sparsity at: 0.31764686158798283 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3288635e-01 -1.9793965e-01 -4.9978610e-02] Sparsity at: 0.31764686158798283 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255368e-01 -1.9814402e-01 -5.0068762e-02] Sparsity at: 0.31764686158798283 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249813e-01 -1.9801405e-01 -5.0055359e-02] Sparsity at: 0.31764686158798283 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257667e-01 -1.9787610e-01 -5.0115645e-02] Sparsity at: 0.31764686158798283 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3243504e-01 -1.9782761e-01 -5.0059263e-02] Sparsity at: 0.31764686158798283 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9029 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269022e-01 -1.9822682e-01 -5.0120346e-02] Sparsity at: 0.31764686158798283 Epoch 378/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252436e-01 -1.9793418e-01 -5.0225124e-02] Sparsity at: 0.31764686158798283 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3232271e-01 -1.9817276e-01 -4.9929984e-02] Sparsity at: 0.31764686158798283 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257635e-01 -1.9795622e-01 -5.0071165e-02] Sparsity at: 0.31764686158798283 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250557e-01 -1.9795847e-01 -4.9893592e-02] Sparsity at: 0.31764686158798283 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263968e-01 -1.9806768e-01 -4.9911957e-02] Sparsity at: 0.31764686158798283 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269655e-01 -1.9787218e-01 -4.9997874e-02] Sparsity at: 0.31764686158798283 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267446e-01 -1.9811675e-01 -4.9882427e-02] Sparsity at: 0.31764686158798283 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9013 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259782e-01 -1.9797219e-01 -5.0059944e-02] Sparsity at: 0.31764686158798283 Epoch 386/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3260244e-01 -1.9785139e-01 -5.0202347e-02] Sparsity at: 0.31764686158798283 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3281606e-01 -1.9803700e-01 -5.0133862e-02] Sparsity at: 0.31764686158798283 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8428 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251641e-01 -1.9800407e-01 -5.0273761e-02] Sparsity at: 0.31764686158798283 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3280791e-01 -1.9799767e-01 -5.0290324e-02] Sparsity at: 0.31764686158798283 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274169e-01 -1.9797666e-01 -5.0147194e-02] Sparsity at: 0.31764686158798283 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267086e-01 -1.9788232e-01 -4.9989849e-02] Sparsity at: 0.31764686158798283 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269809e-01 -1.9788675e-01 -5.0011151e-02] Sparsity at: 0.31764686158798283 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8975 - val_loss: 0.8427 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3286439e-01 -1.9792023e-01 -5.0096732e-02] Sparsity at: 0.31764686158798283 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3266510e-01 -1.9793274e-01 -5.0019603e-02] Sparsity at: 0.31764686158798283 Epoch 395/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262866e-01 -1.9784863e-01 -5.0098855e-02] Sparsity at: 0.31764686158798283 Epoch 396/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255030e-01 -1.9783841e-01 -5.0131239e-02] Sparsity at: 0.31764686158798283 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251403e-01 -1.9784488e-01 -5.0109874e-02] Sparsity at: 0.31764686158798283 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8420 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258386e-01 -1.9808303e-01 -5.0051775e-02] Sparsity at: 0.31764686158798283 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255984e-01 -1.9824937e-01 -4.9821362e-02] Sparsity at: 0.31764686158798283 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256598e-01 -1.9794929e-01 -5.0095707e-02] Sparsity at: 0.31764686158798283 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.06570947417196216 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.16158135941639884 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.7593994 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [0. 1. 1. ... 0. 0. 0.] [0. 1. 1. ... 1. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 1. 0. ... 0. 0. 1.] [0. 0. 0. ... 0. 0. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.2637035259593681 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 49s 7ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263148e-01 -1.9777408e-01 -5.0077610e-02] Sparsity at: 0.31764686158798283 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263366e-01 -1.9798130e-01 -5.0034296e-02] Sparsity at: 0.31764686158798283 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3239899e-01 -1.9788615e-01 -5.0109621e-02] Sparsity at: 0.31764686158798283 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3274319e-01 -1.9788106e-01 -5.0019369e-02] Sparsity at: 0.31764686158798283 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249852e-01 -1.9787355e-01 -4.9989782e-02] Sparsity at: 0.31764686158798283 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8427 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250411e-01 -1.9818258e-01 -4.9999382e-02] Sparsity at: 0.31764686158798283 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9014 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250957e-01 -1.9798924e-01 -5.0187260e-02] Sparsity at: 0.31764686158798283 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3243374e-01 -1.9789532e-01 -5.0093584e-02] Sparsity at: 0.31764686158798283 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249572e-01 -1.9804832e-01 -5.0010800e-02] Sparsity at: 0.31764686158798283 Epoch 410/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257360e-01 -1.9800691e-01 -4.9868125e-02] Sparsity at: 0.31764686158798283 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3264650e-01 -1.9784173e-01 -4.9922738e-02] Sparsity at: 0.31764686158798283 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3246875e-01 -1.9800596e-01 -4.9887218e-02] Sparsity at: 0.31764686158798283 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267536e-01 -1.9808479e-01 -5.0008949e-02] Sparsity at: 0.31764686158798283 Epoch 414/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262331e-01 -1.9801077e-01 -5.0030064e-02] Sparsity at: 0.31764686158798283 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265188e-01 -1.9792736e-01 -5.0074395e-02] Sparsity at: 0.31764686158798283 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8427 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3257772e-01 -1.9794686e-01 -5.0035834e-02] Sparsity at: 0.31764686158798283 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8421 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256471e-01 -1.9807048e-01 -5.0043069e-02] Sparsity at: 0.31764686158798283 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265765e-01 -1.9790578e-01 -5.0051350e-02] Sparsity at: 0.31764686158798283 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3269290e-01 -1.9794534e-01 -5.0027918e-02] Sparsity at: 0.31764686158798283 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259532e-01 -1.9809301e-01 -5.0023921e-02] Sparsity at: 0.31764686158798283 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3246635e-01 -1.9785452e-01 -4.9828585e-02] Sparsity at: 0.31764686158798283 Epoch 422/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8976 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3268240e-01 -1.9794391e-01 -4.9942352e-02] Sparsity at: 0.31764686158798283 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3260315e-01 -1.9805324e-01 -5.0026156e-02] Sparsity at: 0.31764686158798283 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258880e-01 -1.9810832e-01 -4.9839612e-02] Sparsity at: 0.31764686158798283 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256963e-01 -1.9802730e-01 -4.9957942e-02] Sparsity at: 0.31764686158798283 Epoch 426/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3240978e-01 -1.9793838e-01 -4.9901646e-02] Sparsity at: 0.31764686158798283 Epoch 427/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3248630e-01 -1.9803531e-01 -4.9987637e-02] Sparsity at: 0.31764686158798283 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8976 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261342e-01 -1.9800305e-01 -5.0041020e-02] Sparsity at: 0.31764686158798283 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256860e-01 -1.9800164e-01 -5.0026007e-02] Sparsity at: 0.31764686158798283 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3278963e-01 -1.9814353e-01 -5.0101567e-02] Sparsity at: 0.31764686158798283 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259658e-01 -1.9800606e-01 -5.0203025e-02] Sparsity at: 0.31764686158798283 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8981 - val_loss: 0.8427 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3245519e-01 -1.9795997e-01 -5.0144415e-02] Sparsity at: 0.31764686158798283 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3266058e-01 -1.9792086e-01 -5.0271228e-02] Sparsity at: 0.31764686158798283 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3259588e-01 -1.9829655e-01 -5.0304312e-02] Sparsity at: 0.31764686158798283 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3238792e-01 -1.9814217e-01 -4.9957145e-02] Sparsity at: 0.31764686158798283 Epoch 436/500 235/235 [==============================] - 2s 10ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252203e-01 -1.9794235e-01 -4.9957469e-02] Sparsity at: 0.31764686158798283 Epoch 437/500 235/235 [==============================] - 3s 11ms/step - loss: 0.8615 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3249087e-01 -1.9809633e-01 -4.9902093e-02] Sparsity at: 0.31764686158798283 Epoch 438/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9027 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256255e-01 -1.9802758e-01 -5.0011899e-02] Sparsity at: 0.31764686158798283 Epoch 439/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8980 - val_loss: 0.8424 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3235496e-01 -1.9812007e-01 -5.0001614e-02] Sparsity at: 0.31764686158798283 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3242367e-01 -1.9802591e-01 -5.0083179e-02] Sparsity at: 0.31764686158798283 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3243179e-01 -1.9807337e-01 -5.0052747e-02] Sparsity at: 0.31764686158798283 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8424 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267496e-01 -1.9803952e-01 -5.0078813e-02] Sparsity at: 0.31764686158798283 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263175e-01 -1.9824378e-01 -4.9981348e-02] Sparsity at: 0.31764686158798283 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255067e-01 -1.9793999e-01 -5.0043646e-02] Sparsity at: 0.31764686158798283 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8421 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3271338e-01 -1.9795136e-01 -4.9843110e-02] Sparsity at: 0.31764686158798283 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8614 - accuracy: 0.8981 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3258998e-01 -1.9804506e-01 -5.0114356e-02] Sparsity at: 0.31764686158798283 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3271517e-01 -1.9807704e-01 -4.9867090e-02] Sparsity at: 0.31764686158798283 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3273109e-01 -1.9803922e-01 -4.9987983e-02] Sparsity at: 0.31764686158798283 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3279690e-01 -1.9805783e-01 -4.9898762e-02] Sparsity at: 0.31764686158798283 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262923e-01 -1.9803488e-01 -4.9989060e-02] Sparsity at: 0.31764686158798283 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254808e-01 -1.9794846e-01 -5.0017815e-02] Sparsity at: 0.31764686158798283 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3271305e-01 -1.9798163e-01 -5.0043624e-02] Sparsity at: 0.31764686158798283 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3278776e-01 -1.9794081e-01 -5.0188821e-02] Sparsity at: 0.31764686158798283 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8428 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256088e-01 -1.9793466e-01 -5.0146092e-02] Sparsity at: 0.31764686158798283 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8975 - val_loss: 0.8426 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3248058e-01 -1.9798517e-01 -5.0227847e-02] Sparsity at: 0.31764686158798283 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255036e-01 -1.9812705e-01 -5.0001271e-02] Sparsity at: 0.31764686158798283 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261460e-01 -1.9794351e-01 -4.9969021e-02] Sparsity at: 0.31764686158798283 Epoch 458/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3273987e-01 -1.9806549e-01 -4.9887273e-02] Sparsity at: 0.31764686158798283 Epoch 459/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267429e-01 -1.9801582e-01 -5.0127238e-02] Sparsity at: 0.31764686158798283 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267228e-01 -1.9791621e-01 -5.0011240e-02] Sparsity at: 0.31764686158798283 Epoch 461/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8422 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267070e-01 -1.9809151e-01 -4.9882427e-02] Sparsity at: 0.31764686158798283 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252528e-01 -1.9797547e-01 -4.9919579e-02] Sparsity at: 0.31764686158798283 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3240734e-01 -1.9811274e-01 -5.0145332e-02] Sparsity at: 0.31764686158798283 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3236594e-01 -1.9795379e-01 -4.9955167e-02] Sparsity at: 0.31764686158798283 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3246596e-01 -1.9804743e-01 -4.9982406e-02] Sparsity at: 0.31764686158798283 Epoch 466/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8422 - val_accuracy: 0.9012 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3272028e-01 -1.9798447e-01 -4.9977090e-02] Sparsity at: 0.31764686158798283 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252762e-01 -1.9797373e-01 -5.0039735e-02] Sparsity at: 0.31764686158798283 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252729e-01 -1.9786377e-01 -4.9897715e-02] Sparsity at: 0.31764686158798283 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8426 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3262740e-01 -1.9799607e-01 -5.0096452e-02] Sparsity at: 0.31764686158798283 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9026 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263333e-01 -1.9809455e-01 -5.0054040e-02] Sparsity at: 0.31764686158798283 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8981 - val_loss: 0.8421 - val_accuracy: 0.9023 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263416e-01 -1.9815184e-01 -4.9906433e-02] Sparsity at: 0.31764686158798283 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8423 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3272633e-01 -1.9805856e-01 -5.0000936e-02] Sparsity at: 0.31764686158798283 Epoch 473/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8422 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3273297e-01 -1.9802850e-01 -5.0038446e-02] Sparsity at: 0.31764686158798283 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8983 - val_loss: 0.8425 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255207e-01 -1.9813329e-01 -4.9889475e-02] Sparsity at: 0.31764686158798283 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8422 - val_accuracy: 0.9022 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255222e-01 -1.9798040e-01 -4.9811702e-02] Sparsity at: 0.31764686158798283 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3256466e-01 -1.9802372e-01 -4.9898021e-02] Sparsity at: 0.31764686158798283 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3247627e-01 -1.9791786e-01 -4.9946710e-02] Sparsity at: 0.31764686158798283 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8426 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3254672e-01 -1.9797236e-01 -4.9786828e-02] Sparsity at: 0.31764686158798283 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255042e-01 -1.9817907e-01 -4.9881876e-02] Sparsity at: 0.31764686158798283 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8975 - val_loss: 0.8426 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3250558e-01 -1.9806054e-01 -4.9778279e-02] Sparsity at: 0.31764686158798283 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8980 - val_loss: 0.8423 - val_accuracy: 0.9017 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3255520e-01 -1.9807257e-01 -4.9937755e-02] Sparsity at: 0.31764686158798283 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3241418e-01 -1.9794680e-01 -4.9906436e-02] Sparsity at: 0.31764686158798283 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8977 - val_loss: 0.8424 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3235725e-01 -1.9814113e-01 -4.9884971e-02] Sparsity at: 0.31764686158798283 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8984 - val_loss: 0.8426 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3229862e-01 -1.9800222e-01 -4.9869563e-02] Sparsity at: 0.31764686158798283 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3237546e-01 -1.9806202e-01 -4.9970575e-02] Sparsity at: 0.31764686158798283 Epoch 486/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8615 - accuracy: 0.8979 - val_loss: 0.8424 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3263072e-01 -1.9810417e-01 -4.9941722e-02] Sparsity at: 0.31764686158798283 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8428 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3271482e-01 -1.9816847e-01 -4.9906813e-02] Sparsity at: 0.31764686158798283 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8425 - val_accuracy: 0.9018 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3243754e-01 -1.9806570e-01 -4.9752977e-02] Sparsity at: 0.31764686158798283 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9022 [-5.483876e-34 0.000000e+00 5.091550e-34 ... 1.326886e-01 -1.981011e-01 -4.989126e-02] Sparsity at: 0.31764686158798283 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8981 - val_loss: 0.8427 - val_accuracy: 0.9020 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3267493e-01 -1.9803464e-01 -4.9972672e-02] Sparsity at: 0.31764686158798283 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8979 - val_loss: 0.8427 - val_accuracy: 0.9014 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3261253e-01 -1.9809864e-01 -4.9887691e-02] Sparsity at: 0.31764686158798283 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8426 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3278884e-01 -1.9795603e-01 -4.9933054e-02] Sparsity at: 0.31764686158798283 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8617 - accuracy: 0.8978 - val_loss: 0.8427 - val_accuracy: 0.9019 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3239925e-01 -1.9804376e-01 -4.9918123e-02] Sparsity at: 0.31764686158798283 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8977 - val_loss: 0.8426 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3253441e-01 -1.9795430e-01 -4.9909659e-02] Sparsity at: 0.31764686158798283 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8615 - accuracy: 0.8977 - val_loss: 0.8425 - val_accuracy: 0.9021 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3252130e-01 -1.9831890e-01 -4.9981963e-02] Sparsity at: 0.31764686158798283 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8616 - accuracy: 0.8978 - val_loss: 0.8423 - val_accuracy: 0.9016 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3251908e-01 -1.9803734e-01 -4.9723741e-02] Sparsity at: 0.31764686158798283 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8616 - accuracy: 0.8980 - val_loss: 0.8425 - val_accuracy: 0.9028 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3239101e-01 -1.9809103e-01 -4.9818065e-02] Sparsity at: 0.31764686158798283 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8977 - val_loss: 0.8427 - val_accuracy: 0.9015 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3238424e-01 -1.9803026e-01 -4.9927667e-02] Sparsity at: 0.31764686158798283 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8618 - accuracy: 0.8979 - val_loss: 0.8423 - val_accuracy: 0.9025 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3264406e-01 -1.9787149e-01 -4.9839906e-02] Sparsity at: 0.31764686158798283 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8617 - accuracy: 0.8979 - val_loss: 0.8425 - val_accuracy: 0.9024 [-5.4838758e-34 0.0000000e+00 5.0915500e-34 ... 1.3265264e-01 -1.9797458e-01 -4.9811125e-02] Sparsity at: 0.31764686158798283 Epoch 1/500 Wanted sparsity 0.5 Upper percentile 0.0419277586042881 Thresholhold 0.02126331627368927 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.08924054354429245 Thresholhold 0.07131436467170715 Using suggest threshold. Applying new mask Percentage zeros 0.40124512 tf.Tensor( [[1. 1. 0. ... 0. 1. 0.] [1. 1. 1. ... 0. 1. 1.] [0. 1. 1. ... 1. 0. 0.] ... [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 1. 0. 1.] [1. 1. 1. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.5 Upper percentile 0.10948323458433151 Thresholhold -0.12435988336801529 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 1/235 [..............................] - ETA: 59:11 - loss: 2.3452 - accuracy: 0.1172WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0059s vs `on_train_batch_begin` time: 2.4707s). Check your callbacks. 235/235 [==============================] - 17s 8ms/step - loss: 0.5215 - accuracy: 0.8597 - val_loss: 0.2712 - val_accuracy: 0.9193 [ 0.02126332 0. 0.04348067 ... 0.16702083 -0.26408657 0.1956686 ] Sparsity at: 0.2684582886266094 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2477 - accuracy: 0.9294 - val_loss: 0.2109 - val_accuracy: 0.9377 [ 0.02126332 0. 0.04348067 ... 0.18104391 -0.3083002 0.21626326] Sparsity at: 0.2684582886266094 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1942 - accuracy: 0.9441 - val_loss: 0.1738 - val_accuracy: 0.9487 [ 0.02126332 0. 0.04348067 ... 0.19138181 -0.34489295 0.23536518] Sparsity at: 0.2684582886266094 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1589 - accuracy: 0.9538 - val_loss: 0.1500 - val_accuracy: 0.9551 [ 0.02126332 0. 0.04348067 ... 0.19762674 -0.372677 0.25169244] Sparsity at: 0.2684582886266094 Epoch 5/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1333 - accuracy: 0.9608 - val_loss: 0.1332 - val_accuracy: 0.9597 [ 0.02126332 0. 0.04348067 ... 0.20223808 -0.3937274 0.26742727] Sparsity at: 0.2684582886266094 Epoch 6/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1138 - accuracy: 0.9669 - val_loss: 0.1213 - val_accuracy: 0.9629 [ 0.02126332 0. 0.04348067 ... 0.20531437 -0.4105581 0.28378928] Sparsity at: 0.2684582886266094 Epoch 7/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0984 - accuracy: 0.9714 - val_loss: 0.1128 - val_accuracy: 0.9644 [ 0.02126332 0. 0.04348067 ... 0.20786038 -0.42422217 0.29993114] Sparsity at: 0.2684582886266094 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0859 - accuracy: 0.9753 - val_loss: 0.1069 - val_accuracy: 0.9662 [ 0.02126332 0. 0.04348067 ... 0.21039784 -0.43551382 0.31516293] Sparsity at: 0.2684582886266094 Epoch 9/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0760 - accuracy: 0.9784 - val_loss: 0.1028 - val_accuracy: 0.9678 [ 0.02126332 0. 0.04348067 ... 0.21285456 -0.4457577 0.3291802 ] Sparsity at: 0.2684582886266094 Epoch 10/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0674 - accuracy: 0.9812 - val_loss: 0.0997 - val_accuracy: 0.9684 [ 0.02126332 0. 0.04348067 ... 0.21493122 -0.45520988 0.34343296] Sparsity at: 0.2684582886266094 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0600 - accuracy: 0.9833 - val_loss: 0.0980 - val_accuracy: 0.9696 [ 0.02126332 0. 0.04348067 ... 0.2168569 -0.4639403 0.3569665 ] Sparsity at: 0.2684582886266094 Epoch 12/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0537 - accuracy: 0.9851 - val_loss: 0.0963 - val_accuracy: 0.9700 [ 0.02126332 0. 0.04348067 ... 0.2192016 -0.47206596 0.37023714] Sparsity at: 0.2684582886266094 Epoch 13/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0479 - accuracy: 0.9873 - val_loss: 0.0957 - val_accuracy: 0.9706 [ 0.02126332 0. 0.04348067 ... 0.22107998 -0.47938725 0.38289675] Sparsity at: 0.2684582886266094 Epoch 14/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0429 - accuracy: 0.9890 - val_loss: 0.0957 - val_accuracy: 0.9707 [ 0.02126332 0. 0.04348067 ... 0.22361805 -0.48631063 0.3949754 ] Sparsity at: 0.2684582886266094 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0383 - accuracy: 0.9906 - val_loss: 0.0952 - val_accuracy: 0.9713 [ 0.02126332 0. 0.04348067 ... 0.22632354 -0.49305093 0.4065653 ] Sparsity at: 0.2684582886266094 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0341 - accuracy: 0.9917 - val_loss: 0.0956 - val_accuracy: 0.9712 [ 0.02126332 0. 0.04348067 ... 0.2295316 -0.49925712 0.41724834] Sparsity at: 0.2684582886266094 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0303 - accuracy: 0.9929 - val_loss: 0.0958 - val_accuracy: 0.9715 [ 0.02126332 0. 0.04348067 ... 0.23396556 -0.5060252 0.42774615] Sparsity at: 0.2684582886266094 Epoch 18/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0268 - accuracy: 0.9940 - val_loss: 0.0968 - val_accuracy: 0.9718 [ 0.02126332 0. 0.04348067 ... 0.23803149 -0.51327354 0.43757227] Sparsity at: 0.2684582886266094 Epoch 19/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0237 - accuracy: 0.9949 - val_loss: 0.0970 - val_accuracy: 0.9723 [ 0.02126332 0. 0.04348067 ... 0.24232528 -0.5205872 0.44733778] Sparsity at: 0.2684582886266094 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0208 - accuracy: 0.9958 - val_loss: 0.0980 - val_accuracy: 0.9724 [ 0.02126332 0. 0.04348067 ... 0.24621749 -0.52817464 0.45674244] Sparsity at: 0.2684582886266094 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0182 - accuracy: 0.9967 - val_loss: 0.1000 - val_accuracy: 0.9723 [ 0.02126332 0. 0.04348067 ... 0.25079218 -0.53614646 0.465871 ] Sparsity at: 0.2684582886266094 Epoch 22/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0159 - accuracy: 0.9974 - val_loss: 0.1006 - val_accuracy: 0.9738 [ 0.02126332 0. 0.04348067 ... 0.2557964 -0.54538226 0.47451037] Sparsity at: 0.2684582886266094 Epoch 23/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0139 - accuracy: 0.9979 - val_loss: 0.1023 - val_accuracy: 0.9733 [ 0.02126332 0. 0.04348067 ... 0.25865233 -0.55384856 0.48364258] Sparsity at: 0.2684582886266094 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0122 - accuracy: 0.9984 - val_loss: 0.1034 - val_accuracy: 0.9729 [ 0.02126332 0. 0.04348067 ... 0.26205382 -0.5627565 0.49245447] Sparsity at: 0.2684582886266094 Epoch 25/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0107 - accuracy: 0.9988 - val_loss: 0.1040 - val_accuracy: 0.9740 [ 0.02126332 0. 0.04348067 ... 0.26463956 -0.5717029 0.500163 ] Sparsity at: 0.2684582886266094 Epoch 26/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0093 - accuracy: 0.9992 - val_loss: 0.1056 - val_accuracy: 0.9739 [ 0.02126332 0. 0.04348067 ... 0.26524624 -0.57905805 0.5071833 ] Sparsity at: 0.2684582886266094 Epoch 27/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0081 - accuracy: 0.9993 - val_loss: 0.1063 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.2677221 -0.5871909 0.5135752 ] Sparsity at: 0.2684582886266094 Epoch 28/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0072 - accuracy: 0.9994 - val_loss: 0.1099 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.2692408 -0.5945608 0.5200721 ] Sparsity at: 0.2684582886266094 Epoch 29/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0065 - accuracy: 0.9994 - val_loss: 0.1164 - val_accuracy: 0.9731 [ 0.02126332 0. 0.04348067 ... 0.27089462 -0.60207236 0.5238904 ] Sparsity at: 0.2684582886266094 Epoch 30/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0060 - accuracy: 0.9995 - val_loss: 0.1170 - val_accuracy: 0.9734 [ 0.02126332 0. 0.04348067 ... 0.27399483 -0.60899323 0.5284181 ] Sparsity at: 0.2684582886266094 Epoch 31/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0053 - accuracy: 0.9995 - val_loss: 0.1208 - val_accuracy: 0.9728 [ 0.02126332 0. 0.04348067 ... 0.27982938 -0.617689 0.5311662 ] Sparsity at: 0.2684582886266094 Epoch 32/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0048 - accuracy: 0.9997 - val_loss: 0.1174 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.281081 -0.62403387 0.5339233 ] Sparsity at: 0.2684582886266094 Epoch 33/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0050 - accuracy: 0.9993 - val_loss: 0.1284 - val_accuracy: 0.9715 [ 0.02126332 0. 0.04348067 ... 0.28026482 -0.6339376 0.5380926 ] Sparsity at: 0.2684582886266094 Epoch 34/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9991 - val_loss: 0.1309 - val_accuracy: 0.9723 [ 0.02126332 0. 0.04348067 ... 0.27961272 -0.6434787 0.5465148 ] Sparsity at: 0.2684582886266094 Epoch 35/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.1433 - val_accuracy: 0.9706 [ 0.02126332 0. 0.04348067 ... 0.27895343 -0.63876915 0.55570805] Sparsity at: 0.2684582886266094 Epoch 36/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.1257 - val_accuracy: 0.9739 [ 0.02126332 0. 0.04348067 ... 0.28327608 -0.63464594 0.5659999 ] Sparsity at: 0.2684582886266094 Epoch 37/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0055 - accuracy: 0.9986 - val_loss: 0.1289 - val_accuracy: 0.9738 [ 0.02126332 0. 0.04348067 ... 0.29228336 -0.6496202 0.5689408 ] Sparsity at: 0.2684582886266094 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.1467 - val_accuracy: 0.9696 [ 0.02126332 0. 0.04348067 ... 0.287273 -0.6581386 0.56477755] Sparsity at: 0.2684582886266094 Epoch 39/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.1396 - val_accuracy: 0.9732 [ 0.02126332 0. 0.04348067 ... 0.28866014 -0.66592216 0.5668326 ] Sparsity at: 0.2684582886266094 Epoch 40/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1400 - val_accuracy: 0.9726 [ 0.02126332 0. 0.04348067 ... 0.28370982 -0.6668031 0.5654722 ] Sparsity at: 0.2684582886266094 Epoch 41/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 0.1353 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.28491423 -0.67179185 0.5675952 ] Sparsity at: 0.2684582886266094 Epoch 42/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.1443 - val_accuracy: 0.9739 [ 0.02126332 0. 0.04348067 ... 0.28270745 -0.6839155 0.5858273 ] Sparsity at: 0.2684582886266094 Epoch 43/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.1443 - val_accuracy: 0.9729 [ 0.02126332 0. 0.04348067 ... 0.2908145 -0.6832616 0.57646084] Sparsity at: 0.2684582886266094 Epoch 44/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1334 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.28910634 -0.67992157 0.57445294] Sparsity at: 0.2684582886266094 Epoch 45/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.1392 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.28810105 -0.69014704 0.5703469 ] Sparsity at: 0.2684582886266094 Epoch 46/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1424 - val_accuracy: 0.9740 [ 0.02126332 0. 0.04348067 ... 0.2841043 -0.69731104 0.5754799 ] Sparsity at: 0.2684582886266094 Epoch 47/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1333 - val_accuracy: 0.9760 [ 0.02126332 0. 0.04348067 ... 0.282685 -0.69918203 0.57337946] Sparsity at: 0.2684582886266094 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3822e-04 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.28225207 -0.7063152 0.57344896] Sparsity at: 0.2684582886266094 Epoch 49/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3327e-04 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9762 [ 0.02126332 0. 0.04348067 ... 0.2876788 -0.7117837 0.58140206] Sparsity at: 0.2684582886266094 Epoch 50/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5199e-04 - accuracy: 0.9999 - val_loss: 0.1372 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.28921196 -0.720552 0.5840367 ] Sparsity at: 0.2684582886266094 Epoch 51/500 Wanted sparsity 0.6458585 Upper percentile 0.10526492157142009 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.1655148892114795 Thresholhold 0.28851205110549927 Threshold over percentile. Lowering. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.6458585 Upper percentile 0.4916216782141518 Thresholhold -0.06144412234425545 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 50s 8ms/step - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.1612 - val_accuracy: 0.9711 [ 0.02126332 0. 0.04348067 ... 0.29993507 -0.73898196 0.56850666] Sparsity at: 0.3020553916309013 Epoch 52/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0133 - accuracy: 0.9957 - val_loss: 0.1444 - val_accuracy: 0.9741 [ 0.02126332 0. 0.04348067 ... 0.30290055 -0.73904043 0.56436616] Sparsity at: 0.3020553916309013 Epoch 53/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.1515 - val_accuracy: 0.9726 [ 0.02126332 0. 0.04348067 ... 0.30786392 -0.7549374 0.56952095] Sparsity at: 0.3020553916309013 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 0.9998 - val_loss: 0.1384 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.30705678 -0.7540603 0.5600867 ] Sparsity at: 0.3020553916309013 Epoch 55/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7309e-04 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9760 [ 0.02126332 0. 0.04348067 ... 0.31005606 -0.7543497 0.55939704] Sparsity at: 0.3020553916309013 Epoch 56/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1628e-04 - accuracy: 1.0000 - val_loss: 0.1375 - val_accuracy: 0.9763 [ 0.02126332 0. 0.04348067 ... 0.31195587 -0.75820017 0.5619109 ] Sparsity at: 0.3020553916309013 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0808e-04 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9762 [ 0.02126332 0. 0.04348067 ... 0.31275234 -0.7608173 0.5640171 ] Sparsity at: 0.3020553916309013 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5035e-04 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.31359777 -0.7634921 0.5655775 ] Sparsity at: 0.3020553916309013 Epoch 59/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1010e-04 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.31444967 -0.76614916 0.5670739 ] Sparsity at: 0.3020553916309013 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7964e-04 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.315171 -0.76880765 0.56849414] Sparsity at: 0.3020553916309013 Epoch 61/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5418e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.31588772 -0.77158695 0.5699023 ] Sparsity at: 0.3020553916309013 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3220e-04 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9763 [ 0.02126332 0. 0.04348067 ... 0.31662792 -0.77442056 0.5713052 ] Sparsity at: 0.3020553916309013 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1239e-04 - accuracy: 1.0000 - val_loss: 0.1432 - val_accuracy: 0.9763 [ 0.02126332 0. 0.04348067 ... 0.31750235 -0.77733916 0.5727592 ] Sparsity at: 0.3020553916309013 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9539e-04 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9763 [ 0.02126332 0. 0.04348067 ... 0.31825516 -0.7803762 0.57421046] Sparsity at: 0.3020553916309013 Epoch 65/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7950e-04 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9763 [ 0.02126332 0. 0.04348067 ... 0.31920326 -0.783513 0.57574946] Sparsity at: 0.3020553916309013 Epoch 66/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6509e-04 - accuracy: 1.0000 - val_loss: 0.1463 - val_accuracy: 0.9763 [ 0.02126332 0. 0.04348067 ... 0.32006913 -0.7867509 0.57729465] Sparsity at: 0.3020553916309013 Epoch 67/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5180e-04 - accuracy: 1.0000 - val_loss: 0.1475 - val_accuracy: 0.9764 [ 0.02126332 0. 0.04348067 ... 0.32105178 -0.79012835 0.57890093] Sparsity at: 0.3020553916309013 Epoch 68/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3962e-04 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9764 [ 0.02126332 0. 0.04348067 ... 0.32199794 -0.7936606 0.58049375] Sparsity at: 0.3020553916309013 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2841e-04 - accuracy: 1.0000 - val_loss: 0.1498 - val_accuracy: 0.9767 [ 0.02126332 0. 0.04348067 ... 0.32292423 -0.7972948 0.58221066] Sparsity at: 0.3020553916309013 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1758e-04 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9769 [ 0.02126332 0. 0.04348067 ... 0.3238646 -0.8010634 0.5840214 ] Sparsity at: 0.3020553916309013 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0773e-04 - accuracy: 1.0000 - val_loss: 0.1524 - val_accuracy: 0.9768 [ 0.02126332 0. 0.04348067 ... 0.3248686 -0.80492693 0.58584595] Sparsity at: 0.3020553916309013 Epoch 72/500 235/235 [==============================] - 2s 8ms/step - loss: 9.8423e-05 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9769 [ 0.02126332 0. 0.04348067 ... 0.32573307 -0.80894053 0.58765984] Sparsity at: 0.3020553916309013 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 8.9830e-05 - accuracy: 1.0000 - val_loss: 0.1553 - val_accuracy: 0.9766 [ 0.02126332 0. 0.04348067 ... 0.326851 -0.81310886 0.5895688 ] Sparsity at: 0.3020553916309013 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2079e-05 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9767 [ 0.02126332 0. 0.04348067 ... 0.3277809 -0.8173641 0.5915443 ] Sparsity at: 0.3020553916309013 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4837e-05 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9766 [ 0.02126332 0. 0.04348067 ... 0.32886505 -0.8217835 0.59352183] Sparsity at: 0.3020553916309013 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7851e-05 - accuracy: 1.0000 - val_loss: 0.1596 - val_accuracy: 0.9767 [ 0.02126332 0. 0.04348067 ... 0.33001345 -0.82630676 0.5955734 ] Sparsity at: 0.3020553916309013 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1525e-05 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9767 [ 0.02126332 0. 0.04348067 ... 0.33107185 -0.83086187 0.5976295 ] Sparsity at: 0.3020553916309013 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5719e-05 - accuracy: 1.0000 - val_loss: 0.1627 - val_accuracy: 0.9766 [ 0.02126332 0. 0.04348067 ... 0.33204454 -0.83557504 0.59977317] Sparsity at: 0.3020553916309013 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0418e-05 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9765 [ 0.02126332 0. 0.04348067 ... 0.33310527 -0.8404195 0.60200197] Sparsity at: 0.3020553916309013 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5482e-05 - accuracy: 1.0000 - val_loss: 0.1659 - val_accuracy: 0.9765 [ 0.02126332 0. 0.04348067 ... 0.3342645 -0.8453272 0.60416013] Sparsity at: 0.3020553916309013 Epoch 81/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1107e-05 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9764 [ 0.02126332 0. 0.04348067 ... 0.3353951 -0.8502975 0.6063052 ] Sparsity at: 0.3020553916309013 Epoch 82/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7013e-05 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9762 [ 0.02126332 0. 0.04348067 ... 0.33659205 -0.85541177 0.6086061 ] Sparsity at: 0.3020553916309013 Epoch 83/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3242e-05 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.337826 -0.86060476 0.6109776 ] Sparsity at: 0.3020553916309013 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9851e-05 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.3390802 -0.8658501 0.61338425] Sparsity at: 0.3020553916309013 Epoch 85/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6767e-05 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9762 [ 0.02126332 0. 0.04348067 ... 0.34021023 -0.8711478 0.61562383] Sparsity at: 0.3020553916309013 Epoch 86/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3972e-05 - accuracy: 1.0000 - val_loss: 0.1760 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.3413985 -0.876512 0.6178698 ] Sparsity at: 0.3020553916309013 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1448e-05 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9762 [ 0.02126332 0. 0.04348067 ... 0.3425436 -0.8819239 0.62035364] Sparsity at: 0.3020553916309013 Epoch 88/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9155e-05 - accuracy: 1.0000 - val_loss: 0.1795 - val_accuracy: 0.9760 [ 0.02126332 0. 0.04348067 ... 0.34374452 -0.88730526 0.6225602 ] Sparsity at: 0.3020553916309013 Epoch 89/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7146e-05 - accuracy: 1.0000 - val_loss: 0.1813 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.34495136 -0.8927965 0.6249054 ] Sparsity at: 0.3020553916309013 Epoch 90/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5317e-05 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.3461938 -0.89823115 0.6273789 ] Sparsity at: 0.3020553916309013 Epoch 91/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3632e-05 - accuracy: 1.0000 - val_loss: 0.1848 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.34751108 -0.9036638 0.62979305] Sparsity at: 0.3020553916309013 Epoch 92/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2109e-05 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.34891424 -0.9093175 0.63210946] Sparsity at: 0.3020553916309013 Epoch 93/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0789e-05 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.35007548 -0.91476864 0.63449436] Sparsity at: 0.3020553916309013 Epoch 94/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5815e-06 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9759 [ 0.02126332 0. 0.04348067 ... 0.3511449 -0.92030597 0.63701326] Sparsity at: 0.3020553916309013 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5074e-06 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9760 [ 0.02126332 0. 0.04348067 ... 0.35229912 -0.9258466 0.6392699 ] Sparsity at: 0.3020553916309013 Epoch 96/500 235/235 [==============================] - 2s 8ms/step - loss: 7.5968e-06 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9759 [ 0.02126332 0. 0.04348067 ... 0.35351416 -0.93129647 0.64141214] Sparsity at: 0.3020553916309013 Epoch 97/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7495e-06 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.3545256 -0.93674505 0.6439172 ] Sparsity at: 0.3020553916309013 Epoch 98/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9862e-06 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.3559423 -0.94216096 0.64634335] Sparsity at: 0.3020553916309013 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3127e-06 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.35695285 -0.94764656 0.64855397] Sparsity at: 0.3020553916309013 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 4.7206e-06 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.35831973 -0.95307165 0.6506135 ] Sparsity at: 0.3020553916309013 Epoch 101/500 Wanted sparsity 0.7594532 Upper percentile 0.17641599869092062 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.28262760829185396 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.7594532 Upper percentile 0.7852845313879158 Thresholhold -0.05276104062795639 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 4.1893e-06 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.35902867 -0.9584466 0.65309083] Sparsity at: 0.3020553916309013 Epoch 102/500 235/235 [==============================] - 2s 7ms/step - loss: 3.7123e-06 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.36011884 -0.96368843 0.655142 ] Sparsity at: 0.3020553916309013 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3057e-06 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.36122075 -0.96900207 0.657227 ] Sparsity at: 0.3020553916309013 Epoch 104/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9384e-06 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9762 [ 0.02126332 0. 0.04348067 ... 0.3622366 -0.97432584 0.6595657 ] Sparsity at: 0.3020553916309013 Epoch 105/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6099e-06 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.9760 [ 0.02126332 0. 0.04348067 ... 0.3635117 -0.9797349 0.6614887 ] Sparsity at: 0.3020553916309013 Epoch 106/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3120e-06 - accuracy: 1.0000 - val_loss: 0.2127 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.3645397 -0.9849432 0.663842 ] Sparsity at: 0.3020553916309013 Epoch 107/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0517e-06 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.3656114 -0.9901247 0.6659095 ] Sparsity at: 0.3020553916309013 Epoch 108/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8221e-06 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9761 [ 0.02126332 0. 0.04348067 ... 0.36670732 -0.995268 0.6679974 ] Sparsity at: 0.3020553916309013 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6132e-06 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9759 [ 0.02126332 0. 0.04348067 ... 0.3677123 -1.0003829 0.6701143 ] Sparsity at: 0.3020553916309013 Epoch 110/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4354e-06 - accuracy: 1.0000 - val_loss: 0.2200 - val_accuracy: 0.9759 [ 0.02126332 0. 0.04348067 ... 0.36882368 -1.0053213 0.67198604] Sparsity at: 0.3020553916309013 Epoch 111/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2790e-06 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.9759 [ 0.02126332 0. 0.04348067 ... 0.36966538 -1.0103289 0.6740459 ] Sparsity at: 0.3020553916309013 Epoch 112/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1421e-06 - accuracy: 1.0000 - val_loss: 0.2236 - val_accuracy: 0.9759 [ 0.02126332 0. 0.04348067 ... 0.37047738 -1.0152307 0.67621154] Sparsity at: 0.3020553916309013 Epoch 113/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0163e-06 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9758 [ 0.02126332 0. 0.04348067 ... 0.3714164 -1.0201155 0.6778345 ] Sparsity at: 0.3020553916309013 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0812e-07 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.37247437 -1.0249437 0.6798683 ] Sparsity at: 0.3020553916309013 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0989e-07 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.37346327 -1.029692 0.68162274] Sparsity at: 0.3020553916309013 Epoch 116/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2154e-07 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.37434512 -1.0344833 0.68347067] Sparsity at: 0.3020553916309013 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4616e-07 - accuracy: 1.0000 - val_loss: 0.2326 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.37512416 -1.0390948 0.68535656] Sparsity at: 0.3020553916309013 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7762e-07 - accuracy: 1.0000 - val_loss: 0.2341 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.37620085 -1.0436782 0.6869813 ] Sparsity at: 0.3020553916309013 Epoch 119/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1740e-07 - accuracy: 1.0000 - val_loss: 0.2358 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.37706652 -1.0483215 0.68876547] Sparsity at: 0.3020553916309013 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6413e-07 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.37786058 -1.0527827 0.69044477] Sparsity at: 0.3020553916309013 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1716e-07 - accuracy: 1.0000 - val_loss: 0.2392 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.37871218 -1.0572727 0.6920911 ] Sparsity at: 0.3020553916309013 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7494e-07 - accuracy: 1.0000 - val_loss: 0.2405 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.3795897 -1.0616144 0.69364095] Sparsity at: 0.3020553916309013 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3754e-07 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.3803709 -1.0658586 0.6953236 ] Sparsity at: 0.3020553916309013 Epoch 124/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0509e-07 - accuracy: 1.0000 - val_loss: 0.2436 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.38124338 -1.070061 0.6966802 ] Sparsity at: 0.3020553916309013 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7564e-07 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.38216603 -1.0741729 0.69798815] Sparsity at: 0.3020553916309013 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4975e-07 - accuracy: 1.0000 - val_loss: 0.2468 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.38285068 -1.0781872 0.69955486] Sparsity at: 0.3020553916309013 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2636e-07 - accuracy: 1.0000 - val_loss: 0.2479 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.38357112 -1.0821426 0.70088595] Sparsity at: 0.3020553916309013 Epoch 128/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0604e-07 - accuracy: 1.0000 - val_loss: 0.2494 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.38438904 -1.0860124 0.70215183] Sparsity at: 0.3020553916309013 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8822e-07 - accuracy: 1.0000 - val_loss: 0.2509 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.38513124 -1.089845 0.70351726] Sparsity at: 0.3020553916309013 Epoch 130/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7149e-07 - accuracy: 1.0000 - val_loss: 0.2524 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.38590807 -1.0934672 0.70468336] Sparsity at: 0.3020553916309013 Epoch 131/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5698e-07 - accuracy: 1.0000 - val_loss: 0.2531 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.38650522 -1.0970206 0.70582664] Sparsity at: 0.3020553916309013 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4371e-07 - accuracy: 1.0000 - val_loss: 0.2545 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.38720056 -1.1004738 0.70704263] Sparsity at: 0.3020553916309013 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3193e-07 - accuracy: 1.0000 - val_loss: 0.2562 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.38784903 -1.1038669 0.70813674] Sparsity at: 0.3020553916309013 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2162e-07 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.38855568 -1.1071227 0.7092101 ] Sparsity at: 0.3020553916309013 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1204e-07 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.38921872 -1.1103287 0.71025485] Sparsity at: 0.3020553916309013 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0355e-07 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.38985908 -1.1133968 0.71128625] Sparsity at: 0.3020553916309013 Epoch 137/500 235/235 [==============================] - 2s 8ms/step - loss: 9.6252e-08 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.39054394 -1.1163673 0.71217316] Sparsity at: 0.3020553916309013 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 8.9405e-08 - accuracy: 1.0000 - val_loss: 0.2607 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.39112422 -1.1192385 0.713121 ] Sparsity at: 0.3020553916309013 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 8.2993e-08 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.39175558 -1.1220284 0.71397656] Sparsity at: 0.3020553916309013 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 7.7571e-08 - accuracy: 1.0000 - val_loss: 0.2630 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.3922613 -1.1247317 0.71482986] Sparsity at: 0.3020553916309013 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2612e-08 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.39287773 -1.12731 0.7156162 ] Sparsity at: 0.3020553916309013 Epoch 142/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7987e-08 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.3934136 -1.1297511 0.7163169 ] Sparsity at: 0.3020553916309013 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4065e-08 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.39389396 -1.1321375 0.71714973] Sparsity at: 0.3020553916309013 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0185e-08 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.39440697 -1.1344634 0.7179217 ] Sparsity at: 0.3020553916309013 Epoch 145/500 235/235 [==============================] - 2s 8ms/step - loss: 5.6734e-08 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.39489898 -1.1367143 0.7186596 ] Sparsity at: 0.3020553916309013 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3511e-08 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.3953859 -1.1388718 0.71936715] Sparsity at: 0.3020553916309013 Epoch 147/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0735e-08 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.39587575 -1.1409422 0.7201189 ] Sparsity at: 0.3020553916309013 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8065e-08 - accuracy: 1.0000 - val_loss: 0.2694 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.39630726 -1.1429724 0.7207858 ] Sparsity at: 0.3020553916309013 Epoch 149/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5667e-08 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.39672115 -1.1449288 0.7214309 ] Sparsity at: 0.3020553916309013 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3609e-08 - accuracy: 1.0000 - val_loss: 0.2708 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.39718214 -1.1468433 0.72199345] Sparsity at: 0.3020553916309013 Epoch 151/500 Wanted sparsity 0.84481686 Upper percentile 0.25270230488357726 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 0.41199740619523517 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.84481686 Upper percentile 1.114794101674569 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 4.1533e-08 - accuracy: 1.0000 - val_loss: 0.2711 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.39754912 -1.1486295 0.7226232 ] Sparsity at: 0.3020553916309013 Epoch 152/500 235/235 [==============================] - 2s 7ms/step - loss: 3.9657e-08 - accuracy: 1.0000 - val_loss: 0.2717 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.39796552 -1.150314 0.7231762 ] Sparsity at: 0.3020553916309013 Epoch 153/500 235/235 [==============================] - 2s 7ms/step - loss: 3.7913e-08 - accuracy: 1.0000 - val_loss: 0.2724 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.39832076 -1.1520225 0.7236695 ] Sparsity at: 0.3020553916309013 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6331e-08 - accuracy: 1.0000 - val_loss: 0.2730 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.3987034 -1.1536518 0.72415704] Sparsity at: 0.3020553916309013 Epoch 155/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4650e-08 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.39907932 -1.1551743 0.72460157] Sparsity at: 0.3020553916309013 Epoch 156/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3337e-08 - accuracy: 1.0000 - val_loss: 0.2744 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.3994371 -1.1566299 0.72514033] Sparsity at: 0.3020553916309013 Epoch 157/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2123e-08 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.39980236 -1.1580284 0.7256011 ] Sparsity at: 0.3020553916309013 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0939e-08 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9754 [ 0.02126332 0. 0.04348067 ... 0.4001185 -1.1594058 0.7260858 ] Sparsity at: 0.3020553916309013 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9878e-08 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.40044922 -1.1607648 0.72651744] Sparsity at: 0.3020553916309013 Epoch 160/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8723e-08 - accuracy: 1.0000 - val_loss: 0.2763 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.4007979 -1.1620554 0.72695684] Sparsity at: 0.3020553916309013 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7802e-08 - accuracy: 1.0000 - val_loss: 0.2769 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.40113485 -1.1632669 0.72736925] Sparsity at: 0.3020553916309013 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6824e-08 - accuracy: 1.0000 - val_loss: 0.2776 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.40147457 -1.1644619 0.7277867 ] Sparsity at: 0.3020553916309013 Epoch 163/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5978e-08 - accuracy: 1.0000 - val_loss: 0.2782 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.40179595 -1.1656052 0.7281475 ] Sparsity at: 0.3020553916309013 Epoch 164/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5207e-08 - accuracy: 1.0000 - val_loss: 0.2787 - val_accuracy: 0.9755 [ 0.02126332 0. 0.04348067 ... 0.40215182 -1.1667194 0.7285153 ] Sparsity at: 0.3020553916309013 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4523e-08 - accuracy: 1.0000 - val_loss: 0.2790 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.40246183 -1.1678003 0.72886705] Sparsity at: 0.3020553916309013 Epoch 166/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3774e-08 - accuracy: 1.0000 - val_loss: 0.2796 - val_accuracy: 0.9757 [ 0.02126332 0. 0.04348067 ... 0.40277153 -1.1688373 0.7291956 ] Sparsity at: 0.3020553916309013 Epoch 167/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3137e-08 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.4030929 -1.1698108 0.7295016 ] Sparsity at: 0.3020553916309013 Epoch 168/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2471e-08 - accuracy: 1.0000 - val_loss: 0.2807 - val_accuracy: 0.9756 [ 0.02126332 0. 0.04348067 ... 0.40340135 -1.1707615 0.72982633] Sparsity at: 0.3020553916309013 Epoch 169/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1911e-08 - accuracy: 1.0000 - val_loss: 0.2813 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.40372908 -1.1717043 0.7301472 ] Sparsity at: 0.3020553916309013 Epoch 170/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1271e-08 - accuracy: 1.0000 - val_loss: 0.2818 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.40403262 -1.172613 0.73045737] Sparsity at: 0.3020553916309013 Epoch 171/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0782e-08 - accuracy: 1.0000 - val_loss: 0.2823 - val_accuracy: 0.9753 [ 0.02126332 0. 0.04348067 ... 0.40432006 -1.1735181 0.7308144 ] Sparsity at: 0.3020553916309013 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0226e-08 - accuracy: 1.0000 - val_loss: 0.2826 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.4046306 -1.1743859 0.73115295] Sparsity at: 0.3020553916309013 Epoch 173/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9652e-08 - accuracy: 1.0000 - val_loss: 0.2832 - val_accuracy: 0.9752 [ 0.02126332 0. 0.04348067 ... 0.4049353 -1.1752218 0.7314857 ] Sparsity at: 0.3020553916309013 Epoch 174/500 235/235 [==============================] - 2s 8ms/step - loss: 1.9163e-08 - accuracy: 1.0000 - val_loss: 0.2834 - val_accuracy: 0.9752 [ 0.02126332 0. 0.04348067 ... 0.40523517 -1.1760334 0.7317776 ] Sparsity at: 0.3020553916309013 Epoch 175/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8732e-08 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.405517 -1.1768292 0.73207796] Sparsity at: 0.3020553916309013 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 1.8219e-08 - accuracy: 1.0000 - val_loss: 0.2845 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.40579262 -1.1775489 0.7324003 ] Sparsity at: 0.3020553916309013 Epoch 177/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7794e-08 - accuracy: 1.0000 - val_loss: 0.2847 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.40606073 -1.1782804 0.7327133 ] Sparsity at: 0.3020553916309013 Epoch 178/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7353e-08 - accuracy: 1.0000 - val_loss: 0.2852 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.40634128 -1.1789798 0.7330158 ] Sparsity at: 0.3020553916309013 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7029e-08 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.40660667 -1.1796848 0.7333078 ] Sparsity at: 0.3020553916309013 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6648e-08 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.4068455 -1.1803658 0.73362 ] Sparsity at: 0.3020553916309013 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6304e-08 - accuracy: 1.0000 - val_loss: 0.2864 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.40710312 -1.1810097 0.7339123 ] Sparsity at: 0.3020553916309013 Epoch 182/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5875e-08 - accuracy: 1.0000 - val_loss: 0.2866 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.40735534 -1.1816602 0.7341666 ] Sparsity at: 0.3020553916309013 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5628e-08 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.40760913 -1.1822644 0.7344539 ] Sparsity at: 0.3020553916309013 Epoch 184/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5267e-08 - accuracy: 1.0000 - val_loss: 0.2874 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.4078724 -1.1828725 0.7347268 ] Sparsity at: 0.3020553916309013 Epoch 185/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4947e-08 - accuracy: 1.0000 - val_loss: 0.2877 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.40814027 -1.1834705 0.73498434] Sparsity at: 0.3020553916309013 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4679e-08 - accuracy: 1.0000 - val_loss: 0.2879 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.4084104 -1.1840194 0.7352781 ] Sparsity at: 0.3020553916309013 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4317e-08 - accuracy: 1.0000 - val_loss: 0.2883 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.40868747 -1.184552 0.7355466 ] Sparsity at: 0.3020553916309013 Epoch 188/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4057e-08 - accuracy: 1.0000 - val_loss: 0.2885 - val_accuracy: 0.9752 [ 0.02126332 0. 0.04348067 ... 0.40896714 -1.1851096 0.73580366] Sparsity at: 0.3020553916309013 Epoch 189/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3765e-08 - accuracy: 1.0000 - val_loss: 0.2888 - val_accuracy: 0.9752 [ 0.02126332 0. 0.04348067 ... 0.40923104 -1.1856184 0.7360505 ] Sparsity at: 0.3020553916309013 Epoch 190/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3514e-08 - accuracy: 1.0000 - val_loss: 0.2889 - val_accuracy: 0.9752 [ 0.02126332 0. 0.04348067 ... 0.40945876 -1.1861299 0.7362822 ] Sparsity at: 0.3020553916309013 Epoch 191/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3244e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9752 [ 0.02126332 0. 0.04348067 ... 0.4096933 -1.1866218 0.7365049 ] Sparsity at: 0.3020553916309013 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2978e-08 - accuracy: 1.0000 - val_loss: 0.2896 - val_accuracy: 0.9751 [ 0.02126332 0. 0.04348067 ... 0.40992075 -1.1871115 0.73672694] Sparsity at: 0.3020553916309013 Epoch 193/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2726e-08 - accuracy: 1.0000 - val_loss: 0.2897 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41014296 -1.1876031 0.7369361 ] Sparsity at: 0.3020553916309013 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2491e-08 - accuracy: 1.0000 - val_loss: 0.2900 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.4103491 -1.188099 0.73713005] Sparsity at: 0.3020553916309013 Epoch 195/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2253e-08 - accuracy: 1.0000 - val_loss: 0.2901 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41056684 -1.1885579 0.7373313 ] Sparsity at: 0.3020553916309013 Epoch 196/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2066e-08 - accuracy: 1.0000 - val_loss: 0.2903 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.4107773 -1.1890384 0.73757356] Sparsity at: 0.3020553916309013 Epoch 197/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1841e-08 - accuracy: 1.0000 - val_loss: 0.2904 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41100466 -1.1894847 0.73778695] Sparsity at: 0.3020553916309013 Epoch 198/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1617e-08 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41123056 -1.1899287 0.7379922 ] Sparsity at: 0.3020553916309013 Epoch 199/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1440e-08 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41144383 -1.1903454 0.73823285] Sparsity at: 0.3020553916309013 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1230e-08 - accuracy: 1.0000 - val_loss: 0.2909 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41168195 -1.1907562 0.7384579 ] Sparsity at: 0.3020553916309013 Epoch 201/500 Wanted sparsity 0.90598273 Upper percentile 0.32897635305230466 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 0.5186788086043634 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.90598273 Upper percentile 1.3412633376150325 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 42s 7ms/step - loss: 1.0997e-08 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41189814 -1.1911304 0.7386835 ] Sparsity at: 0.3020553916309013 Epoch 202/500 235/235 [==============================] - 2s 7ms/step - loss: 1.0810e-08 - accuracy: 1.0000 - val_loss: 0.2913 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.4121093 -1.1915112 0.73892367] Sparsity at: 0.3020553916309013 Epoch 203/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0655e-08 - accuracy: 1.0000 - val_loss: 0.2914 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41230246 -1.1919066 0.7391482 ] Sparsity at: 0.3020553916309013 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0480e-08 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41250992 -1.1922907 0.73936963] Sparsity at: 0.3020553916309013 Epoch 205/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0312e-08 - accuracy: 1.0000 - val_loss: 0.2917 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41270247 -1.1926603 0.7396002 ] Sparsity at: 0.3020553916309013 Epoch 206/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0145e-08 - accuracy: 1.0000 - val_loss: 0.2920 - val_accuracy: 0.9750 [ 0.02126332 0. 0.04348067 ... 0.41290188 -1.1930207 0.73980415] Sparsity at: 0.3020553916309013 Epoch 207/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0018e-08 - accuracy: 1.0000 - val_loss: 0.2921 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41311085 -1.1933827 0.74001896] Sparsity at: 0.3020553916309013 Epoch 208/500 235/235 [==============================] - 2s 8ms/step - loss: 9.8586e-09 - accuracy: 1.0000 - val_loss: 0.2922 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.4133249 -1.193743 0.7401866 ] Sparsity at: 0.3020553916309013 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7156e-09 - accuracy: 1.0000 - val_loss: 0.2924 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41352913 -1.1940681 0.7403836 ] Sparsity at: 0.3020553916309013 Epoch 210/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5646e-09 - accuracy: 1.0000 - val_loss: 0.2925 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41374967 -1.1943852 0.7405639 ] Sparsity at: 0.3020553916309013 Epoch 211/500 235/235 [==============================] - 2s 8ms/step - loss: 9.4632e-09 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41394046 -1.1947309 0.7407478 ] Sparsity at: 0.3020553916309013 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 9.2804e-09 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41413978 -1.1950192 0.74092835] Sparsity at: 0.3020553916309013 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 9.2129e-09 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.4143285 -1.1953473 0.74111116] Sparsity at: 0.3020553916309013 Epoch 214/500 235/235 [==============================] - 2s 8ms/step - loss: 9.0778e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.41453606 -1.1956526 0.7412978 ] Sparsity at: 0.3020553916309013 Epoch 215/500 235/235 [==============================] - 2s 8ms/step - loss: 8.9387e-09 - accuracy: 1.0000 - val_loss: 0.2931 - val_accuracy: 0.9748 [ 0.02126332 0. 0.04348067 ... 0.41472086 -1.1959534 0.7414813 ] Sparsity at: 0.3020553916309013 Epoch 216/500 235/235 [==============================] - 2s 8ms/step - loss: 8.7937e-09 - accuracy: 1.0000 - val_loss: 0.2933 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.41488424 -1.1962414 0.7416832 ] Sparsity at: 0.3020553916309013 Epoch 217/500 235/235 [==============================] - 2s 8ms/step - loss: 8.6745e-09 - accuracy: 1.0000 - val_loss: 0.2934 - val_accuracy: 0.9748 [ 0.02126332 0. 0.04348067 ... 0.41507727 -1.1965115 0.7418821 ] Sparsity at: 0.3020553916309013 Epoch 218/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5235e-09 - accuracy: 1.0000 - val_loss: 0.2936 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.41527244 -1.1967857 0.74203086] Sparsity at: 0.3020553916309013 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 8.4698e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.41544256 -1.1970468 0.7422091 ] Sparsity at: 0.3020553916309013 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3486e-09 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41562253 -1.1973232 0.7423906 ] Sparsity at: 0.3020553916309013 Epoch 221/500 235/235 [==============================] - 2s 8ms/step - loss: 8.2215e-09 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41581276 -1.1975768 0.7425695 ] Sparsity at: 0.3020553916309013 Epoch 222/500 235/235 [==============================] - 2s 8ms/step - loss: 8.1062e-09 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4159862 -1.197835 0.74273264] Sparsity at: 0.3020553916309013 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 8.0268e-09 - accuracy: 1.0000 - val_loss: 0.2941 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41616452 -1.1980891 0.74292314] Sparsity at: 0.3020553916309013 Epoch 224/500 235/235 [==============================] - 2s 8ms/step - loss: 7.9572e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41632533 -1.1983374 0.74309444] Sparsity at: 0.3020553916309013 Epoch 225/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8599e-09 - accuracy: 1.0000 - val_loss: 0.2943 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41648182 -1.1985697 0.7432732 ] Sparsity at: 0.3020553916309013 Epoch 226/500 235/235 [==============================] - 2s 8ms/step - loss: 7.7685e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4166369 -1.1987923 0.7434664 ] Sparsity at: 0.3020553916309013 Epoch 227/500 235/235 [==============================] - 2s 8ms/step - loss: 7.6791e-09 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41679347 -1.1990248 0.7436146 ] Sparsity at: 0.3020553916309013 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5261e-09 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.41694933 -1.1992478 0.7438167 ] Sparsity at: 0.3020553916309013 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 7.4506e-09 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.41711432 -1.1994696 0.7439899 ] Sparsity at: 0.3020553916309013 Epoch 230/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3949e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.41729105 -1.1996717 0.7441569 ] Sparsity at: 0.3020553916309013 Epoch 231/500 235/235 [==============================] - 2s 8ms/step - loss: 7.3175e-09 - accuracy: 1.0000 - val_loss: 0.2949 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.41745865 -1.1999032 0.74432015] Sparsity at: 0.3020553916309013 Epoch 232/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1943e-09 - accuracy: 1.0000 - val_loss: 0.2951 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.41764727 -1.2000784 0.74449575] Sparsity at: 0.3020553916309013 Epoch 233/500 235/235 [==============================] - 2s 8ms/step - loss: 7.1406e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4178118 -1.2002548 0.7446694 ] Sparsity at: 0.3020553916309013 Epoch 234/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0095e-09 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.41799542 -1.2004259 0.74486655] Sparsity at: 0.3020553916309013 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 6.9042e-09 - accuracy: 1.0000 - val_loss: 0.2953 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4181846 -1.2005795 0.7450589 ] Sparsity at: 0.3020553916309013 Epoch 236/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8863e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.41836572 -1.2007366 0.74526924] Sparsity at: 0.3020553916309013 Epoch 237/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8128e-09 - accuracy: 1.0000 - val_loss: 0.2954 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.41854388 -1.2008815 0.74544704] Sparsity at: 0.3020553916309013 Epoch 238/500 235/235 [==============================] - 2s 8ms/step - loss: 6.7353e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.41874114 -1.2010219 0.7456279 ] Sparsity at: 0.3020553916309013 Epoch 239/500 235/235 [==============================] - 2s 8ms/step - loss: 6.6340e-09 - accuracy: 1.0000 - val_loss: 0.2956 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4189321 -1.2011335 0.74580586] Sparsity at: 0.3020553916309013 Epoch 240/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5962e-09 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.41911608 -1.2012739 0.74599755] Sparsity at: 0.3020553916309013 Epoch 241/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5088e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.41929668 -1.2013761 0.7461803 ] Sparsity at: 0.3020553916309013 Epoch 242/500 235/235 [==============================] - 2s 8ms/step - loss: 6.4095e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4194813 -1.2014809 0.7463962 ] Sparsity at: 0.3020553916309013 Epoch 243/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3658e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4196692 -1.2015927 0.746583 ] Sparsity at: 0.3020553916309013 Epoch 244/500 235/235 [==============================] - 2s 8ms/step - loss: 6.3399e-09 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4198622 -1.2017004 0.74678266] Sparsity at: 0.3020553916309013 Epoch 245/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2803e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.42005274 -1.2017969 0.74694496] Sparsity at: 0.3020553916309013 Epoch 246/500 235/235 [==============================] - 2s 8ms/step - loss: 6.2168e-09 - accuracy: 1.0000 - val_loss: 0.2960 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.42024437 -1.201914 0.7471218 ] Sparsity at: 0.3020553916309013 Epoch 247/500 235/235 [==============================] - 2s 8ms/step - loss: 6.1452e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4204311 -1.2019912 0.74729294] Sparsity at: 0.3020553916309013 Epoch 248/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0638e-09 - accuracy: 1.0000 - val_loss: 0.2961 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4206216 -1.2020781 0.7474642 ] Sparsity at: 0.3020553916309013 Epoch 249/500 235/235 [==============================] - 2s 8ms/step - loss: 6.0201e-09 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.42082593 -1.2021646 0.74762857] Sparsity at: 0.3020553916309013 Epoch 250/500 235/235 [==============================] - 2s 8ms/step - loss: 5.9346e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4210017 -1.202281 0.747803 ] Sparsity at: 0.3020553916309013 Epoch 251/500 Wanted sparsity 0.9469837 Upper percentile 0.41203891892625677 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 0.6075214533092677 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9469837 Upper percentile 1.5514764097761145 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 44s 7ms/step - loss: 5.9048e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4211976 -1.2023685 0.7479691 ] Sparsity at: 0.3020553916309013 Epoch 252/500 235/235 [==============================] - 2s 7ms/step - loss: 5.8929e-09 - accuracy: 1.0000 - val_loss: 0.2964 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4213776 -1.2024628 0.74814194] Sparsity at: 0.3020553916309013 Epoch 253/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7836e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4215615 -1.2025505 0.74830467] Sparsity at: 0.3020553916309013 Epoch 254/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7419e-09 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42171392 -1.2026299 0.74848413] Sparsity at: 0.3020553916309013 Epoch 255/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7101e-09 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42191166 -1.2027004 0.74866796] Sparsity at: 0.3020553916309013 Epoch 256/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5909e-09 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42208704 -1.2027755 0.7488535 ] Sparsity at: 0.3020553916309013 Epoch 257/500 235/235 [==============================] - 2s 8ms/step - loss: 5.5333e-09 - accuracy: 1.0000 - val_loss: 0.2968 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42225656 -1.202837 0.74905425] Sparsity at: 0.3020553916309013 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5214e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42242593 -1.2029052 0.7492231 ] Sparsity at: 0.3020553916309013 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4797e-09 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42260206 -1.2029502 0.7494042 ] Sparsity at: 0.3020553916309013 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 5.4677e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42277065 -1.2030219 0.7495975 ] Sparsity at: 0.3020553916309013 Epoch 261/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3783e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42294705 -1.2030613 0.7497704 ] Sparsity at: 0.3020553916309013 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 5.3386e-09 - accuracy: 1.0000 - val_loss: 0.2970 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42310962 -1.2031165 0.7499439 ] Sparsity at: 0.3020553916309013 Epoch 263/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2949e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4232688 -1.2031521 0.7501253 ] Sparsity at: 0.3020553916309013 Epoch 264/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2472e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42344227 -1.2031747 0.750311 ] Sparsity at: 0.3020553916309013 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2373e-09 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42360845 -1.2032051 0.7504784 ] Sparsity at: 0.3020553916309013 Epoch 266/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1518e-09 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42377922 -1.2032326 0.7506681 ] Sparsity at: 0.3020553916309013 Epoch 267/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1737e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4239427 -1.2032685 0.7508458 ] Sparsity at: 0.3020553916309013 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0545e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42411447 -1.2032768 0.7510166 ] Sparsity at: 0.3020553916309013 Epoch 269/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0803e-09 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42429924 -1.2032886 0.7512091 ] Sparsity at: 0.3020553916309013 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 5.0207e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42444593 -1.2032864 0.7513876 ] Sparsity at: 0.3020553916309013 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9969e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42461672 -1.2032986 0.75157887] Sparsity at: 0.3020553916309013 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9452e-09 - accuracy: 1.0000 - val_loss: 0.2975 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42478597 -1.203322 0.7517516 ] Sparsity at: 0.3020553916309013 Epoch 273/500 235/235 [==============================] - 2s 8ms/step - loss: 4.9194e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42497346 -1.2033162 0.75192946] Sparsity at: 0.3020553916309013 Epoch 274/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8379e-09 - accuracy: 1.0000 - val_loss: 0.2976 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42515004 -1.2033033 0.7521158 ] Sparsity at: 0.3020553916309013 Epoch 275/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7942e-09 - accuracy: 1.0000 - val_loss: 0.2977 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.425301 -1.2032802 0.75227886] Sparsity at: 0.3020553916309013 Epoch 276/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7922e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42546347 -1.2032843 0.75244486] Sparsity at: 0.3020553916309013 Epoch 277/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7664e-09 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42562312 -1.2032888 0.7526034 ] Sparsity at: 0.3020553916309013 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6968e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42579433 -1.2032748 0.75278455] Sparsity at: 0.3020553916309013 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 4.7366e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42594197 -1.2032696 0.75295717] Sparsity at: 0.3020553916309013 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6035e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42612684 -1.2032402 0.7531086 ] Sparsity at: 0.3020553916309013 Epoch 281/500 235/235 [==============================] - 2s 8ms/step - loss: 4.6213e-09 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42630962 -1.2032082 0.7532856 ] Sparsity at: 0.3020553916309013 Epoch 282/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5995e-09 - accuracy: 1.0000 - val_loss: 0.2981 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4264684 -1.2032002 0.7534619 ] Sparsity at: 0.3020553916309013 Epoch 283/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5637e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42664525 -1.2031788 0.7536176 ] Sparsity at: 0.3020553916309013 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5121e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42681187 -1.2031475 0.75380975] Sparsity at: 0.3020553916309013 Epoch 285/500 235/235 [==============================] - 2s 8ms/step - loss: 4.5439e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42696738 -1.2031196 0.75398743] Sparsity at: 0.3020553916309013 Epoch 286/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4227e-09 - accuracy: 1.0000 - val_loss: 0.2982 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42713678 -1.2030857 0.7541824 ] Sparsity at: 0.3020553916309013 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4227e-09 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42729527 -1.2030532 0.75435305] Sparsity at: 0.3020553916309013 Epoch 288/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4068e-09 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4274472 -1.2030358 0.754525 ] Sparsity at: 0.3020553916309013 Epoch 289/500 235/235 [==============================] - 2s 8ms/step - loss: 4.4207e-09 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42759472 -1.2030027 0.75467825] Sparsity at: 0.3020553916309013 Epoch 290/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3511e-09 - accuracy: 1.0000 - val_loss: 0.2984 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42777023 -1.2029535 0.7548717 ] Sparsity at: 0.3020553916309013 Epoch 291/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3015e-09 - accuracy: 1.0000 - val_loss: 0.2984 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42794365 -1.2029169 0.75503725] Sparsity at: 0.3020553916309013 Epoch 292/500 235/235 [==============================] - 2s 8ms/step - loss: 4.3333e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4281233 -1.2028645 0.75522673] Sparsity at: 0.3020553916309013 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2975e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42827424 -1.2028269 0.75540996] Sparsity at: 0.3020553916309013 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2379e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42845312 -1.2027583 0.75557715] Sparsity at: 0.3020553916309013 Epoch 295/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2538e-09 - accuracy: 1.0000 - val_loss: 0.2985 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.42860255 -1.2027283 0.75573504] Sparsity at: 0.3020553916309013 Epoch 296/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1823e-09 - accuracy: 1.0000 - val_loss: 0.2986 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.4287646 -1.2026851 0.7559277 ] Sparsity at: 0.3020553916309013 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1982e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.4289348 -1.2026632 0.7561077 ] Sparsity at: 0.3020553916309013 Epoch 298/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1564e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.4290832 -1.2025799 0.7562703 ] Sparsity at: 0.3020553916309013 Epoch 299/500 235/235 [==============================] - 2s 8ms/step - loss: 4.1803e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42926866 -1.2025566 0.75647074] Sparsity at: 0.3020553916309013 Epoch 300/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0551e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42943448 -1.2025026 0.75665396] Sparsity at: 0.3020553916309013 Epoch 301/500 Wanted sparsity 0.9718529 Upper percentile 0.5041835091003932 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 0.6901974902365353 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9718529 Upper percentile 1.7779325935055965 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 44s 8ms/step - loss: 4.0710e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42962724 -1.2024461 0.75683504] Sparsity at: 0.3020553916309013 Epoch 302/500 235/235 [==============================] - 2s 7ms/step - loss: 4.0730e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.42979398 -1.2023813 0.75702316] Sparsity at: 0.3020553916309013 Epoch 303/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0670e-09 - accuracy: 1.0000 - val_loss: 0.2988 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.4299462 -1.2023201 0.7571892 ] Sparsity at: 0.3020553916309013 Epoch 304/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9955e-09 - accuracy: 1.0000 - val_loss: 0.2987 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43011278 -1.2022681 0.7573853 ] Sparsity at: 0.3020553916309013 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0392e-09 - accuracy: 1.0000 - val_loss: 0.2988 - val_accuracy: 0.9748 [ 0.02126332 0. 0.04348067 ... 0.43027496 -1.2022394 0.75756574] Sparsity at: 0.3020553916309013 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9399e-09 - accuracy: 1.0000 - val_loss: 0.2988 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43044317 -1.2021407 0.7577472 ] Sparsity at: 0.3020553916309013 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0074e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43062112 -1.2021099 0.75792235] Sparsity at: 0.3020553916309013 Epoch 308/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9538e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43080148 -1.202039 0.75811857] Sparsity at: 0.3020553916309013 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9220e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43096304 -1.2019972 0.7582874 ] Sparsity at: 0.3020553916309013 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 3.9160e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43112087 -1.2019482 0.7584692 ] Sparsity at: 0.3020553916309013 Epoch 311/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8226e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43127662 -1.2018526 0.75867367] Sparsity at: 0.3020553916309013 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8485e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.4314353 -1.2017882 0.7588634 ] Sparsity at: 0.3020553916309013 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8445e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.4316052 -1.201714 0.7590403 ] Sparsity at: 0.3020553916309013 Epoch 314/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8485e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43175533 -1.2016523 0.75923043] Sparsity at: 0.3020553916309013 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8107e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43192315 -1.201576 0.75943536] Sparsity at: 0.3020553916309013 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8048e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9749 [ 0.02126332 0. 0.04348067 ... 0.4320782 -1.2015322 0.7596271 ] Sparsity at: 0.3020553916309013 Epoch 317/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7988e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43222234 -1.201458 0.7598009 ] Sparsity at: 0.3020553916309013 Epoch 318/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7849e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43237486 -1.2014086 0.75997895] Sparsity at: 0.3020553916309013 Epoch 319/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7412e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43253955 -1.2013321 0.7601703 ] Sparsity at: 0.3020553916309013 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7611e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9748 [ 0.02126332 0. 0.04348067 ... 0.43270895 -1.201246 0.76035815] Sparsity at: 0.3020553916309013 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6637e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43286926 -1.201169 0.76052773] Sparsity at: 0.3020553916309013 Epoch 322/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7034e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9747 [ 0.02126332 0. 0.04348067 ... 0.43304655 -1.201109 0.76072556] Sparsity at: 0.3020553916309013 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6816e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43320101 -1.2010474 0.7609369 ] Sparsity at: 0.3020553916309013 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6677e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43337584 -1.2009869 0.7611411 ] Sparsity at: 0.3020553916309013 Epoch 325/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6617e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43353876 -1.2009194 0.7613162 ] Sparsity at: 0.3020553916309013 Epoch 326/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6418e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4336833 -1.2008481 0.7615021 ] Sparsity at: 0.3020553916309013 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6220e-09 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43385506 -1.2007889 0.76169956] Sparsity at: 0.3020553916309013 Epoch 328/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6259e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43402994 -1.2007078 0.76188004] Sparsity at: 0.3020553916309013 Epoch 329/500 235/235 [==============================] - 2s 8ms/step - loss: 3.6200e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43420014 -1.2006179 0.7620791 ] Sparsity at: 0.3020553916309013 Epoch 330/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5604e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43435845 -1.2005686 0.7622587 ] Sparsity at: 0.3020553916309013 Epoch 331/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5723e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43451008 -1.2004873 0.7624602 ] Sparsity at: 0.3020553916309013 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5385e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43466958 -1.2004151 0.7626764 ] Sparsity at: 0.3020553916309013 Epoch 333/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5544e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43484166 -1.2003417 0.7628837 ] Sparsity at: 0.3020553916309013 Epoch 334/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4451e-09 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43500692 -1.2002516 0.76308435] Sparsity at: 0.3020553916309013 Epoch 335/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4948e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4351614 -1.2001725 0.7632671 ] Sparsity at: 0.3020553916309013 Epoch 336/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5127e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4353184 -1.2001033 0.763446 ] Sparsity at: 0.3020553916309013 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4908e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43546447 -1.2000476 0.7636349 ] Sparsity at: 0.3020553916309013 Epoch 338/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4670e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43562397 -1.1999848 0.76381 ] Sparsity at: 0.3020553916309013 Epoch 339/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4571e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43578553 -1.1999032 0.76400614] Sparsity at: 0.3020553916309013 Epoch 340/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4332e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4359646 -1.199801 0.7642023 ] Sparsity at: 0.3020553916309013 Epoch 341/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4253e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4361326 -1.1997018 0.7644077 ] Sparsity at: 0.3020553916309013 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3895e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4362772 -1.1996224 0.7645682 ] Sparsity at: 0.3020553916309013 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4054e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43643862 -1.1995416 0.7647525 ] Sparsity at: 0.3020553916309013 Epoch 344/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4193e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4366154 -1.1994528 0.76491815] Sparsity at: 0.3020553916309013 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 3.4114e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43677664 -1.1993619 0.76511544] Sparsity at: 0.3020553916309013 Epoch 346/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3776e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4369398 -1.1992807 0.7653003 ] Sparsity at: 0.3020553916309013 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3259e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43709382 -1.1992131 0.76547414] Sparsity at: 0.3020553916309013 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 3.3577e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43722713 -1.1991279 0.76565117] Sparsity at: 0.3020553916309013 Epoch 349/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3319e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43739134 -1.1990519 0.7658348 ] Sparsity at: 0.3020553916309013 Epoch 350/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3677e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43754336 -1.1989881 0.76600736] Sparsity at: 0.3020553916309013 Epoch 351/500 Wanted sparsity 0.9846233 Upper percentile 0.5842941218939899 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 0.7564832252152556 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.9846233 Upper percentile 1.9474849072882847 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 44s 7ms/step - loss: 3.2524e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.43771684 -1.1988655 0.76617086] Sparsity at: 0.3020553916309013 Epoch 352/500 235/235 [==============================] - 2s 7ms/step - loss: 3.3597e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43789226 -1.1987897 0.7663675 ] Sparsity at: 0.3020553916309013 Epoch 353/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2802e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4380473 -1.1986862 0.76655674] Sparsity at: 0.3020553916309013 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2564e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4381995 -1.1985962 0.7667392 ] Sparsity at: 0.3020553916309013 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2802e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43839976 -1.1985067 0.766919 ] Sparsity at: 0.3020553916309013 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2345e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.43855318 -1.198407 0.76711196] Sparsity at: 0.3020553916309013 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2743e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43871936 -1.1983039 0.76730174] Sparsity at: 0.3020553916309013 Epoch 358/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2504e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9746 [ 0.02126332 0. 0.04348067 ... 0.4388812 -1.1982012 0.7674936 ] Sparsity at: 0.3020553916309013 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1888e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43904704 -1.198101 0.76766443] Sparsity at: 0.3020553916309013 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2187e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4392217 -1.1980063 0.7678803 ] Sparsity at: 0.3020553916309013 Epoch 361/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2465e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.43938953 -1.1979202 0.7680611 ] Sparsity at: 0.3020553916309013 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1749e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4395592 -1.1978035 0.7682446 ] Sparsity at: 0.3020553916309013 Epoch 363/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2206e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43974194 -1.1976755 0.76841307] Sparsity at: 0.3020553916309013 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1928e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.43991554 -1.1975871 0.76861924] Sparsity at: 0.3020553916309013 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1590e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4400882 -1.197485 0.7687996 ] Sparsity at: 0.3020553916309013 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1650e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44028583 -1.197361 0.76899344] Sparsity at: 0.3020553916309013 Epoch 367/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1630e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4404426 -1.1972709 0.76919097] Sparsity at: 0.3020553916309013 Epoch 368/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1332e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44061518 -1.1971676 0.7693669 ] Sparsity at: 0.3020553916309013 Epoch 369/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1412e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4407775 -1.1970512 0.7695454 ] Sparsity at: 0.3020553916309013 Epoch 370/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1153e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44095668 -1.1969222 0.7697713 ] Sparsity at: 0.3020553916309013 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1372e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44114006 -1.196836 0.76995057] Sparsity at: 0.3020553916309013 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1094e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44129962 -1.1967254 0.7701492 ] Sparsity at: 0.3020553916309013 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1352e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44148386 -1.1966279 0.77036554] Sparsity at: 0.3020553916309013 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1014e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44165543 -1.1965252 0.77054507] Sparsity at: 0.3020553916309013 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0875e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44183984 -1.1964163 0.77074057] Sparsity at: 0.3020553916309013 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0518e-09 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4420131 -1.1962901 0.7709345 ] Sparsity at: 0.3020553916309013 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0875e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4421901 -1.1961501 0.7711171 ] Sparsity at: 0.3020553916309013 Epoch 378/500 235/235 [==============================] - 2s 10ms/step - loss: 3.0776e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4423621 -1.196039 0.77131116] Sparsity at: 0.3020553916309013 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0498e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44252345 -1.1959181 0.77150357] Sparsity at: 0.3020553916309013 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0458e-09 - accuracy: 1.0000 - val_loss: 0.2992 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44269204 -1.1958115 0.7716891 ] Sparsity at: 0.3020553916309013 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0696e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44286236 -1.1956986 0.77189344] Sparsity at: 0.3020553916309013 Epoch 382/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0577e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44303998 -1.195598 0.77208185] Sparsity at: 0.3020553916309013 Epoch 383/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0061e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4432085 -1.1954632 0.77227503] Sparsity at: 0.3020553916309013 Epoch 384/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0339e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44337362 -1.1953372 0.77246135] Sparsity at: 0.3020553916309013 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0438e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4435464 -1.1952161 0.7726563 ] Sparsity at: 0.3020553916309013 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0478e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44370872 -1.1951019 0.77284473] Sparsity at: 0.3020553916309013 Epoch 387/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9981e-09 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44391263 -1.1949868 0.77302736] Sparsity at: 0.3020553916309013 Epoch 388/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0359e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44407594 -1.194874 0.7731918 ] Sparsity at: 0.3020553916309013 Epoch 389/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0180e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44422042 -1.1947584 0.77340823] Sparsity at: 0.3020553916309013 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9782e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.4444093 -1.1946319 0.7736053 ] Sparsity at: 0.3020553916309013 Epoch 391/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0001e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44458893 -1.1944842 0.77379614] Sparsity at: 0.3020553916309013 Epoch 392/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0239e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4447755 -1.194372 0.77400494] Sparsity at: 0.3020553916309013 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0617e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44496253 -1.1942661 0.77418697] Sparsity at: 0.3020553916309013 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44516352 -1.1941184 0.7743932 ] Sparsity at: 0.3020553916309013 Epoch 395/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9723e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44534633 -1.1939843 0.7745614 ] Sparsity at: 0.3020553916309013 Epoch 396/500 235/235 [==============================] - 2s 9ms/step - loss: 2.9445e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44553906 -1.1938394 0.7747833 ] Sparsity at: 0.3020553916309013 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 3.0001e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4457185 -1.193709 0.7749944 ] Sparsity at: 0.3020553916309013 Epoch 398/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9147e-09 - accuracy: 1.0000 - val_loss: 0.2994 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44591537 -1.1935756 0.77520996] Sparsity at: 0.3020553916309013 Epoch 399/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9345e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44609883 -1.1934432 0.7754135 ] Sparsity at: 0.3020553916309013 Epoch 400/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9862e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44627932 -1.1932997 0.77559716] Sparsity at: 0.3020553916309013 Epoch 401/500 Wanted sparsity 0.989328 Upper percentile 0.6257745814666933 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.25362724 tf.Tensor( [[1. 0. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 0. 1.] [0. 1. 0. ... 1. 1. 1.] ... [0. 1. 0. ... 1. 1. 1.] [1. 1. 0. ... 1. 1. 0.] [1. 0. 0. ... 1. 1. 1.]], shape=(784, 64), dtype=float32) Wanted sparsity 0.989328 Upper percentile 0.7970864249869294 Thresholhold 0.0 Using suggest threshold. Applying new mask Percentage zeros 0.645874 tf.Tensor( [[1. 0. 0. ... 0. 1. 0.] [1. 0. 0. ... 0. 1. 1.] [0. 0. 0. ... 0. 0. 0.] ... [1. 0. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 0. 1.] [1. 1. 0. ... 0. 1. 0.]], shape=(64, 128), dtype=float32) Wanted sparsity 0.989328 Upper percentile 2.02338800751275 Thresholhold -0.0 Using suggest threshold. Applying new mask Percentage zeros 0.0 tf.Tensor( [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]], shape=(128, 10), dtype=float32) 235/235 [==============================] - 43s 7ms/step - loss: 2.9643e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4464653 -1.1931634 0.77580684] Sparsity at: 0.3020553916309013 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44666585 -1.193022 0.77600807] Sparsity at: 0.3020553916309013 Epoch 403/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9763e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44685015 -1.1928682 0.77620107] Sparsity at: 0.3020553916309013 Epoch 404/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9306e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.44704214 -1.1927228 0.7764128 ] Sparsity at: 0.3020553916309013 Epoch 405/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44722834 -1.1925759 0.7766009 ] Sparsity at: 0.3020553916309013 Epoch 406/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9425e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.44739285 -1.1924493 0.7768046 ] Sparsity at: 0.3020553916309013 Epoch 407/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9107e-09 - accuracy: 1.0000 - val_loss: 0.2995 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.447587 -1.1923018 0.77698785] Sparsity at: 0.3020553916309013 Epoch 408/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9286e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44777647 -1.1921719 0.77718914] Sparsity at: 0.3020553916309013 Epoch 409/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8948e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4479641 -1.192013 0.7773983 ] Sparsity at: 0.3020553916309013 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9087e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44814655 -1.1918768 0.777601 ] Sparsity at: 0.3020553916309013 Epoch 411/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8849e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44832662 -1.1917466 0.7777895 ] Sparsity at: 0.3020553916309013 Epoch 412/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8869e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44851652 -1.1915841 0.77799135] Sparsity at: 0.3020553916309013 Epoch 413/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8988e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4486965 -1.1914351 0.7781718 ] Sparsity at: 0.3020553916309013 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8491e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44886136 -1.1912953 0.77836585] Sparsity at: 0.3020553916309013 Epoch 415/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9047e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4490435 -1.1911824 0.7785585 ] Sparsity at: 0.3020553916309013 Epoch 416/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8968e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44922873 -1.1910223 0.77873755] Sparsity at: 0.3020553916309013 Epoch 417/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8372e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.44943118 -1.1908526 0.7789373 ] Sparsity at: 0.3020553916309013 Epoch 418/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8968e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4495905 -1.1906978 0.7791303 ] Sparsity at: 0.3020553916309013 Epoch 419/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9745 [ 0.02126332 0. 0.04348067 ... 0.44978005 -1.1905614 0.7793366 ] Sparsity at: 0.3020553916309013 Epoch 420/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8431e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.44995713 -1.1904019 0.7795473 ] Sparsity at: 0.3020553916309013 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8491e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45017117 -1.1902528 0.77974164] Sparsity at: 0.3020553916309013 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8233e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45034805 -1.190086 0.7799482 ] Sparsity at: 0.3020553916309013 Epoch 423/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8531e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45054942 -1.1899395 0.78016704] Sparsity at: 0.3020553916309013 Epoch 424/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8590e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45073333 -1.1897812 0.78037673] Sparsity at: 0.3020553916309013 Epoch 425/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8888e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45089406 -1.1896098 0.7805784 ] Sparsity at: 0.3020553916309013 Epoch 426/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7855e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45110574 -1.1894555 0.7807969 ] Sparsity at: 0.3020553916309013 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8233e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45128655 -1.1893088 0.780993 ] Sparsity at: 0.3020553916309013 Epoch 428/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8551e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4514446 -1.1891822 0.7811973 ] Sparsity at: 0.3020553916309013 Epoch 429/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8173e-09 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4516546 -1.1890148 0.78139 ] Sparsity at: 0.3020553916309013 Epoch 430/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8253e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.4518591 -1.188885 0.78159463] Sparsity at: 0.3020553916309013 Epoch 431/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8074e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45200548 -1.1887137 0.78179383] Sparsity at: 0.3020553916309013 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8094e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45221624 -1.1885562 0.7819971 ] Sparsity at: 0.3020553916309013 Epoch 433/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8213e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45239007 -1.1884062 0.78220433] Sparsity at: 0.3020553916309013 Epoch 434/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7875e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45258963 -1.1882571 0.7824035 ] Sparsity at: 0.3020553916309013 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7895e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45276022 -1.1880857 0.7825916 ] Sparsity at: 0.3020553916309013 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8332e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45294923 -1.187932 0.7827864 ] Sparsity at: 0.3020553916309013 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8312e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4531503 -1.1877836 0.78298897] Sparsity at: 0.3020553916309013 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8054e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45332214 -1.1876085 0.783185 ] Sparsity at: 0.3020553916309013 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8372e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9744 [ 0.02126332 0. 0.04348067 ... 0.45350477 -1.1874613 0.7834121 ] Sparsity at: 0.3020553916309013 Epoch 440/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8133e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45370334 -1.187304 0.78361535] Sparsity at: 0.3020553916309013 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7895e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45388106 -1.1871562 0.783797 ] Sparsity at: 0.3020553916309013 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7935e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45408076 -1.1870052 0.7840216 ] Sparsity at: 0.3020553916309013 Epoch 443/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7736e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45426437 -1.1868759 0.78422976] Sparsity at: 0.3020553916309013 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8094e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45445177 -1.1867199 0.7844034 ] Sparsity at: 0.3020553916309013 Epoch 445/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7935e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45464307 -1.1865838 0.7845981 ] Sparsity at: 0.3020553916309013 Epoch 446/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7796e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45481792 -1.1864139 0.7848045 ] Sparsity at: 0.3020553916309013 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7537e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4550326 -1.186258 0.7850342 ] Sparsity at: 0.3020553916309013 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45521167 -1.1861291 0.7852498 ] Sparsity at: 0.3020553916309013 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.45540008 -1.1859567 0.7854605 ] Sparsity at: 0.3020553916309013 Epoch 450/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8034e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45559126 -1.1857984 0.78563416] Sparsity at: 0.3020553916309013 Epoch 451/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7796e-09 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.45577103 -1.1856498 0.78586733] Sparsity at: 0.3020553916309013 Epoch 452/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8014e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4559395 -1.1854857 0.786054 ] Sparsity at: 0.3020553916309013 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7259e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.4561282 -1.1853367 0.78625625] Sparsity at: 0.3020553916309013 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7458e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4563365 -1.1851479 0.78644043] Sparsity at: 0.3020553916309013 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 2.7696e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.45655072 -1.1849878 0.7866431 ] Sparsity at: 0.3020553916309013 Epoch 456/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7577e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.45673206 -1.1848509 0.78683716] Sparsity at: 0.3020553916309013 Epoch 457/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7597e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.45693666 -1.184674 0.7870373 ] Sparsity at: 0.3020553916309013 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7815e-09 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45710137 -1.1845218 0.7872344 ] Sparsity at: 0.3020553916309013 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7716e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4573101 -1.1843754 0.78742903] Sparsity at: 0.3020553916309013 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.4575042 -1.1842284 0.7876205 ] Sparsity at: 0.3020553916309013 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.45767817 -1.1840662 0.78782666] Sparsity at: 0.3020553916309013 Epoch 462/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7855e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.457872 -1.1839128 0.78804356] Sparsity at: 0.3020553916309013 Epoch 463/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.458048 -1.1837468 0.7882357 ] Sparsity at: 0.3020553916309013 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7815e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.4582476 -1.1836182 0.78843224] Sparsity at: 0.3020553916309013 Epoch 465/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45845062 -1.1834662 0.7886283 ] Sparsity at: 0.3020553916309013 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7696e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45864183 -1.1832778 0.7888266 ] Sparsity at: 0.3020553916309013 Epoch 467/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9742 [ 0.02126332 0. 0.04348067 ... 0.4588146 -1.1831131 0.7890437 ] Sparsity at: 0.3020553916309013 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7577e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45900393 -1.182952 0.78922045] Sparsity at: 0.3020553916309013 Epoch 469/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7617e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45919523 -1.1827818 0.7894301 ] Sparsity at: 0.3020553916309013 Epoch 470/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4593799 -1.1826422 0.78964746] Sparsity at: 0.3020553916309013 Epoch 471/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7517e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45956725 -1.1824613 0.78988355] Sparsity at: 0.3020553916309013 Epoch 472/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7319e-09 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45975965 -1.1822757 0.7900747 ] Sparsity at: 0.3020553916309013 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7458e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.45996156 -1.1821334 0.79028213] Sparsity at: 0.3020553916309013 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7478e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4601602 -1.1819777 0.79047465] Sparsity at: 0.3020553916309013 Epoch 475/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7378e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46035957 -1.1818355 0.7906642 ] Sparsity at: 0.3020553916309013 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46057132 -1.1816895 0.79084545] Sparsity at: 0.3020553916309013 Epoch 477/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6842e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46074736 -1.1815083 0.79105663] Sparsity at: 0.3020553916309013 Epoch 478/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7657e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46093693 -1.1813244 0.7912409 ] Sparsity at: 0.3020553916309013 Epoch 479/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7339e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46114072 -1.1811274 0.7914706 ] Sparsity at: 0.3020553916309013 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4613267 -1.1809651 0.79168445] Sparsity at: 0.3020553916309013 Epoch 481/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7041e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46152785 -1.1807996 0.7918849 ] Sparsity at: 0.3020553916309013 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4617256 -1.1806245 0.79206854] Sparsity at: 0.3020553916309013 Epoch 483/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7359e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46190038 -1.1804578 0.7922776 ] Sparsity at: 0.3020553916309013 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7478e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4620971 -1.180308 0.7924722 ] Sparsity at: 0.3020553916309013 Epoch 485/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7180e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46229157 -1.1801437 0.79266745] Sparsity at: 0.3020553916309013 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7259e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46247852 -1.1799914 0.79288936] Sparsity at: 0.3020553916309013 Epoch 487/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6981e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46268556 -1.179811 0.7931087 ] Sparsity at: 0.3020553916309013 Epoch 488/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7219e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4628794 -1.1796154 0.7932919 ] Sparsity at: 0.3020553916309013 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7438e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46305576 -1.1794428 0.7934727 ] Sparsity at: 0.3020553916309013 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7517e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46326023 -1.1793092 0.7936953 ] Sparsity at: 0.3020553916309013 Epoch 491/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7021e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46345156 -1.1791304 0.79389167] Sparsity at: 0.3020553916309013 Epoch 492/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6862e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46365586 -1.1789601 0.7940967 ] Sparsity at: 0.3020553916309013 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7041e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46385068 -1.1787962 0.7943191 ] Sparsity at: 0.3020553916309013 Epoch 494/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7378e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4640543 -1.1786457 0.7944923 ] Sparsity at: 0.3020553916309013 Epoch 495/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46425107 -1.1784854 0.7946855 ] Sparsity at: 0.3020553916309013 Epoch 496/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7498e-09 - accuracy: 1.0000 - val_loss: 0.3000 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46444574 -1.1783222 0.794889 ] Sparsity at: 0.3020553916309013 Epoch 497/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7478e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46466246 -1.178155 0.7950975 ] Sparsity at: 0.3020553916309013 Epoch 498/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6921e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46485028 -1.1779854 0.7953271 ] Sparsity at: 0.3020553916309013 Epoch 499/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7279e-09 - accuracy: 1.0000 - val_loss: 0.3001 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.46504995 -1.1778473 0.7954936 ] Sparsity at: 0.3020553916309013 Epoch 500/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7160e-09 - accuracy: 1.0000 - val_loss: 0.3002 - val_accuracy: 0.9743 [ 0.02126332 0. 0.04348067 ... 0.4652491 -1.1776524 0.79569346] Sparsity at: 0.3020553916309013 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 0.1401 - accuracy: 0.9780 - val_loss: 0.2065 - val_accuracy: 0.9596 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9785 - val_loss: 0.2130 - val_accuracy: 0.9584 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9796 - val_loss: 0.1976 - val_accuracy: 0.9622 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1380 - accuracy: 0.9793 - val_loss: 0.1975 - val_accuracy: 0.9633 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1397 - accuracy: 0.9791 - val_loss: 0.1915 - val_accuracy: 0.9636 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.2113 - val_accuracy: 0.9592 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1377 - accuracy: 0.9799 - val_loss: 0.2494 - val_accuracy: 0.9497 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9788 - val_loss: 0.1922 - val_accuracy: 0.9637 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9802 - val_loss: 0.2207 - val_accuracy: 0.9556 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9801 - val_loss: 0.1977 - val_accuracy: 0.9638 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9793 - val_loss: 0.2159 - val_accuracy: 0.9591 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9797 - val_loss: 0.2132 - val_accuracy: 0.9583 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1377 - accuracy: 0.9794 - val_loss: 0.2097 - val_accuracy: 0.9583 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9793 - val_loss: 0.2139 - val_accuracy: 0.9579 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9791 - val_loss: 0.2554 - val_accuracy: 0.9466 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9797 - val_loss: 0.2394 - val_accuracy: 0.9506 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9796 - val_loss: 0.1958 - val_accuracy: 0.9636 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1418 - accuracy: 0.9783 - val_loss: 0.2530 - val_accuracy: 0.9462 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1398 - accuracy: 0.9787 - val_loss: 0.2078 - val_accuracy: 0.9609 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9804 - val_loss: 0.2216 - val_accuracy: 0.9576 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9797 - val_loss: 0.2464 - val_accuracy: 0.9487 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9800 - val_loss: 0.2180 - val_accuracy: 0.9562 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1430 - accuracy: 0.9787 - val_loss: 0.2392 - val_accuracy: 0.9517 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1392 - accuracy: 0.9795 - val_loss: 0.2144 - val_accuracy: 0.9574 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9785 - val_loss: 0.2745 - val_accuracy: 0.9417 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.2215 - val_accuracy: 0.9546 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9793 - val_loss: 0.2714 - val_accuracy: 0.9444 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1417 - accuracy: 0.9782 - val_loss: 0.2698 - val_accuracy: 0.9430 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1388 - accuracy: 0.9796 - val_loss: 0.2208 - val_accuracy: 0.9583 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1393 - accuracy: 0.9793 - val_loss: 0.2264 - val_accuracy: 0.9555 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9799 - val_loss: 0.2182 - val_accuracy: 0.9584 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1393 - accuracy: 0.9797 - val_loss: 0.2449 - val_accuracy: 0.9502 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9793 - val_loss: 0.2364 - val_accuracy: 0.9529 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1384 - accuracy: 0.9793 - val_loss: 0.2251 - val_accuracy: 0.9546 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9793 - val_loss: 0.2399 - val_accuracy: 0.9527 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9797 - val_loss: 0.1995 - val_accuracy: 0.9622 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9790 - val_loss: 0.2337 - val_accuracy: 0.9521 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9794 - val_loss: 0.2439 - val_accuracy: 0.9523 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9786 - val_loss: 0.2179 - val_accuracy: 0.9557 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9798 - val_loss: 0.2103 - val_accuracy: 0.9605 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9798 - val_loss: 0.2033 - val_accuracy: 0.9623 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1400 - accuracy: 0.9783 - val_loss: 0.2246 - val_accuracy: 0.9561 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9801 - val_loss: 0.2241 - val_accuracy: 0.9556 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1390 - accuracy: 0.9786 - val_loss: 0.2306 - val_accuracy: 0.9541 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9789 - val_loss: 0.2016 - val_accuracy: 0.9626 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2437 - val_accuracy: 0.9520 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9791 - val_loss: 0.2127 - val_accuracy: 0.9582 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9797 - val_loss: 0.2123 - val_accuracy: 0.9608 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9794 - val_loss: 0.2293 - val_accuracy: 0.9537 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... 0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9794 - val_loss: 0.2470 - val_accuracy: 0.9504 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9804 - val_loss: 0.2161 - val_accuracy: 0.9566 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9796 - val_loss: 0.2300 - val_accuracy: 0.9519 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9791 - val_loss: 0.2104 - val_accuracy: 0.9594 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9793 - val_loss: 0.2224 - val_accuracy: 0.9558 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9793 - val_loss: 0.2497 - val_accuracy: 0.9489 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9798 - val_loss: 0.2232 - val_accuracy: 0.9540 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9787 - val_loss: 0.2255 - val_accuracy: 0.9535 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9802 - val_loss: 0.2120 - val_accuracy: 0.9595 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9792 - val_loss: 0.1988 - val_accuracy: 0.9630 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9803 - val_loss: 0.1931 - val_accuracy: 0.9643 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9795 - val_loss: 0.2089 - val_accuracy: 0.9608 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9792 - val_loss: 0.2216 - val_accuracy: 0.9573 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1347 - accuracy: 0.9797 - val_loss: 0.1922 - val_accuracy: 0.9619 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1404 - accuracy: 0.9786 - val_loss: 0.1959 - val_accuracy: 0.9641 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9794 - val_loss: 0.2167 - val_accuracy: 0.9572 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9797 - val_loss: 0.2070 - val_accuracy: 0.9598 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9788 - val_loss: 0.2018 - val_accuracy: 0.9611 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1410 - accuracy: 0.9781 - val_loss: 0.2150 - val_accuracy: 0.9578 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1415 - accuracy: 0.9781 - val_loss: 0.1931 - val_accuracy: 0.9648 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9800 - val_loss: 0.2036 - val_accuracy: 0.9593 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1334 - accuracy: 0.9801 - val_loss: 0.1914 - val_accuracy: 0.9613 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9792 - val_loss: 0.1978 - val_accuracy: 0.9620 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1331 - accuracy: 0.9799 - val_loss: 0.2292 - val_accuracy: 0.9561 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9782 - val_loss: 0.2042 - val_accuracy: 0.9619 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9798 - val_loss: 0.2066 - val_accuracy: 0.9627 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9789 - val_loss: 0.2095 - val_accuracy: 0.9609 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9792 - val_loss: 0.1966 - val_accuracy: 0.9629 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9795 - val_loss: 0.2043 - val_accuracy: 0.9604 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9794 - val_loss: 0.2204 - val_accuracy: 0.9547 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9791 - val_loss: 0.2195 - val_accuracy: 0.9555 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1356 - accuracy: 0.9797 - val_loss: 0.2028 - val_accuracy: 0.9622 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1339 - accuracy: 0.9798 - val_loss: 0.2102 - val_accuracy: 0.9597 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9793 - val_loss: 0.2086 - val_accuracy: 0.9610 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9781 - val_loss: 0.2051 - val_accuracy: 0.9619 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9797 - val_loss: 0.2299 - val_accuracy: 0.9525 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1387 - accuracy: 0.9782 - val_loss: 0.2004 - val_accuracy: 0.9626 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9797 - val_loss: 0.2376 - val_accuracy: 0.9507 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9801 - val_loss: 0.2145 - val_accuracy: 0.9584 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1355 - accuracy: 0.9798 - val_loss: 0.2123 - val_accuracy: 0.9574 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9794 - val_loss: 0.2600 - val_accuracy: 0.9426 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9788 - val_loss: 0.2072 - val_accuracy: 0.9588 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9793 - val_loss: 0.2264 - val_accuracy: 0.9547 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1323 - accuracy: 0.9800 - val_loss: 0.2054 - val_accuracy: 0.9594 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9785 - val_loss: 0.2392 - val_accuracy: 0.9537 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9793 - val_loss: 0.2156 - val_accuracy: 0.9580 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9798 - val_loss: 0.2018 - val_accuracy: 0.9596 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.1876 - val_accuracy: 0.9674 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9790 - val_loss: 0.2342 - val_accuracy: 0.9520 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1383 - accuracy: 0.9786 - val_loss: 0.2103 - val_accuracy: 0.9579 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1336 - accuracy: 0.9801 - val_loss: 0.1970 - val_accuracy: 0.9637 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9780 - val_loss: 0.2047 - val_accuracy: 0.9604 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.1923 - val_accuracy: 0.9626 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2094 - val_accuracy: 0.9596 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1348 - accuracy: 0.9797 - val_loss: 0.1940 - val_accuracy: 0.9636 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9782 - val_loss: 0.2070 - val_accuracy: 0.9599 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... 0.000000e+00 0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9783 - val_loss: 0.2219 - val_accuracy: 0.9546 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9796 - val_loss: 0.2048 - val_accuracy: 0.9588 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9787 - val_loss: 0.1980 - val_accuracy: 0.9626 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9789 - val_loss: 0.1999 - val_accuracy: 0.9648 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... 0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9787 - val_loss: 0.1869 - val_accuracy: 0.9659 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9789 - val_loss: 0.2116 - val_accuracy: 0.9601 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1341 - accuracy: 0.9800 - val_loss: 0.2233 - val_accuracy: 0.9568 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9790 - val_loss: 0.1947 - val_accuracy: 0.9631 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9796 - val_loss: 0.2144 - val_accuracy: 0.9574 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1381 - accuracy: 0.9784 - val_loss: 0.2298 - val_accuracy: 0.9541 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9792 - val_loss: 0.1916 - val_accuracy: 0.9640 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9788 - val_loss: 0.2359 - val_accuracy: 0.9528 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9790 - val_loss: 0.1998 - val_accuracy: 0.9639 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9794 - val_loss: 0.2064 - val_accuracy: 0.9605 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9802 - val_loss: 0.2034 - val_accuracy: 0.9593 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9792 - val_loss: 0.1834 - val_accuracy: 0.9661 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9793 - val_loss: 0.2260 - val_accuracy: 0.9523 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9785 - val_loss: 0.2308 - val_accuracy: 0.9518 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1345 - accuracy: 0.9798 - val_loss: 0.1985 - val_accuracy: 0.9628 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9796 - val_loss: 0.2246 - val_accuracy: 0.9524 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2023 - val_accuracy: 0.9607 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9790 - val_loss: 0.1964 - val_accuracy: 0.9637 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.2175 - val_accuracy: 0.9575 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9791 - val_loss: 0.2318 - val_accuracy: 0.9531 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1391 - accuracy: 0.9785 - val_loss: 0.2077 - val_accuracy: 0.9608 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9786 - val_loss: 0.2569 - val_accuracy: 0.9448 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2332 - val_accuracy: 0.9513 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9787 - val_loss: 0.2629 - val_accuracy: 0.9458 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1350 - accuracy: 0.9797 - val_loss: 0.2189 - val_accuracy: 0.9568 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1354 - accuracy: 0.9791 - val_loss: 0.2435 - val_accuracy: 0.9494 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9794 - val_loss: 0.2161 - val_accuracy: 0.9576 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9796 - val_loss: 0.2385 - val_accuracy: 0.9519 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9792 - val_loss: 0.2290 - val_accuracy: 0.9530 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1340 - accuracy: 0.9792 - val_loss: 0.2236 - val_accuracy: 0.9549 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9791 - val_loss: 0.1935 - val_accuracy: 0.9651 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9778 - val_loss: 0.3444 - val_accuracy: 0.9281 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1359 - accuracy: 0.9797 - val_loss: 0.1938 - val_accuracy: 0.9625 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9790 - val_loss: 0.1955 - val_accuracy: 0.9629 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1361 - accuracy: 0.9792 - val_loss: 0.1988 - val_accuracy: 0.9599 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9794 - val_loss: 0.1867 - val_accuracy: 0.9651 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9797 - val_loss: 0.2083 - val_accuracy: 0.9599 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 -0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9789 - val_loss: 0.2351 - val_accuracy: 0.9510 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1312 - accuracy: 0.9805 - val_loss: 0.2125 - val_accuracy: 0.9552 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1338 - accuracy: 0.9795 - val_loss: 0.2413 - val_accuracy: 0.9498 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9783 - val_loss: 0.2332 - val_accuracy: 0.9507 [ 0.000000e+00 4.959844e-34 0.000000e+00 ... -0.000000e+00 -0.000000e+00 0.000000e+00] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1428 - accuracy: 0.9778 - val_loss: 0.2033 - val_accuracy: 0.9611 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9784 - val_loss: 0.1820 - val_accuracy: 0.9664 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9794 - val_loss: 0.2079 - val_accuracy: 0.9579 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1385 - accuracy: 0.9793 - val_loss: 0.1837 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1348 - accuracy: 0.9793 - val_loss: 0.1971 - val_accuracy: 0.9615 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1413 - accuracy: 0.9773 - val_loss: 0.2044 - val_accuracy: 0.9606 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9786 - val_loss: 0.2568 - val_accuracy: 0.9445 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1389 - accuracy: 0.9782 - val_loss: 0.2040 - val_accuracy: 0.9601 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1342 - accuracy: 0.9793 - val_loss: 0.2419 - val_accuracy: 0.9506 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1376 - accuracy: 0.9793 - val_loss: 0.2334 - val_accuracy: 0.9533 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9785 - val_loss: 0.2011 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9782 - val_loss: 0.2106 - val_accuracy: 0.9573 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1358 - accuracy: 0.9796 - val_loss: 0.2322 - val_accuracy: 0.9518 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1367 - accuracy: 0.9786 - val_loss: 0.2090 - val_accuracy: 0.9588 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9789 - val_loss: 0.1974 - val_accuracy: 0.9623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9793 - val_loss: 0.2428 - val_accuracy: 0.9511 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9789 - val_loss: 0.2391 - val_accuracy: 0.9505 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1332 - accuracy: 0.9798 - val_loss: 0.2189 - val_accuracy: 0.9557 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9785 - val_loss: 0.1919 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9790 - val_loss: 0.2303 - val_accuracy: 0.9514 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9790 - val_loss: 0.2127 - val_accuracy: 0.9578 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9785 - val_loss: 0.2441 - val_accuracy: 0.9494 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9790 - val_loss: 0.2100 - val_accuracy: 0.9581 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9790 - val_loss: 0.2044 - val_accuracy: 0.9600 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1364 - accuracy: 0.9790 - val_loss: 0.2156 - val_accuracy: 0.9567 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9787 - val_loss: 0.1957 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1352 - accuracy: 0.9790 - val_loss: 0.2121 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9787 - val_loss: 0.1967 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2129 - val_accuracy: 0.9587 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1374 - accuracy: 0.9787 - val_loss: 0.1845 - val_accuracy: 0.9661 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9791 - val_loss: 0.2376 - val_accuracy: 0.9520 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1368 - accuracy: 0.9787 - val_loss: 0.2191 - val_accuracy: 0.9576 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1362 - accuracy: 0.9795 - val_loss: 0.2074 - val_accuracy: 0.9608 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9782 - val_loss: 0.2007 - val_accuracy: 0.9612 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1322 - accuracy: 0.9799 - val_loss: 0.2006 - val_accuracy: 0.9604 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.2475 - val_accuracy: 0.9514 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9791 - val_loss: 0.2229 - val_accuracy: 0.9560 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9781 - val_loss: 0.2093 - val_accuracy: 0.9578 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9795 - val_loss: 0.2842 - val_accuracy: 0.9390 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1370 - accuracy: 0.9790 - val_loss: 0.2087 - val_accuracy: 0.9588 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1365 - accuracy: 0.9787 - val_loss: 0.1854 - val_accuracy: 0.9650 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9793 - val_loss: 0.2111 - val_accuracy: 0.9574 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9790 - val_loss: 0.1872 - val_accuracy: 0.9645 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9785 - val_loss: 0.1969 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9788 - val_loss: 0.2143 - val_accuracy: 0.9571 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9794 - val_loss: 0.2162 - val_accuracy: 0.9572 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9788 - val_loss: 0.1886 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9791 - val_loss: 0.1936 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1380 - accuracy: 0.9779 - val_loss: 0.1982 - val_accuracy: 0.9613 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9794 - val_loss: 0.2208 - val_accuracy: 0.9565 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9769 - val_loss: 0.1969 - val_accuracy: 0.9625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1407 - accuracy: 0.9767 - val_loss: 0.2018 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1371 - accuracy: 0.9785 - val_loss: 0.2265 - val_accuracy: 0.9529 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9785 - val_loss: 0.2078 - val_accuracy: 0.9599 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9778 - val_loss: 0.2362 - val_accuracy: 0.9514 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1372 - accuracy: 0.9792 - val_loss: 0.1896 - val_accuracy: 0.9618 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1386 - accuracy: 0.9779 - val_loss: 0.2511 - val_accuracy: 0.9485 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1373 - accuracy: 0.9791 - val_loss: 0.2220 - val_accuracy: 0.9557 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1323 - accuracy: 0.9794 - val_loss: 0.2070 - val_accuracy: 0.9600 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1409 - accuracy: 0.9773 - val_loss: 0.2001 - val_accuracy: 0.9606 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1378 - accuracy: 0.9789 - val_loss: 0.2279 - val_accuracy: 0.9506 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1400 - accuracy: 0.9776 - val_loss: 0.2071 - val_accuracy: 0.9609 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9788 - val_loss: 0.1930 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1379 - accuracy: 0.9778 - val_loss: 0.2154 - val_accuracy: 0.9580 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1404 - accuracy: 0.9778 - val_loss: 0.2028 - val_accuracy: 0.9597 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1375 - accuracy: 0.9784 - val_loss: 0.1996 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1369 - accuracy: 0.9791 - val_loss: 0.2119 - val_accuracy: 0.9580 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1421 - accuracy: 0.9767 - val_loss: 0.1901 - val_accuracy: 0.9661 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1356 - accuracy: 0.9788 - val_loss: 0.1973 - val_accuracy: 0.9606 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1384 - accuracy: 0.9789 - val_loss: 0.2200 - val_accuracy: 0.9521 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1337 - accuracy: 0.9790 - val_loss: 0.2093 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1383 - accuracy: 0.9779 - val_loss: 0.1948 - val_accuracy: 0.9625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1389 - accuracy: 0.9784 - val_loss: 0.1943 - val_accuracy: 0.9621 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1349 - accuracy: 0.9783 - val_loss: 0.2476 - val_accuracy: 0.9496 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1394 - accuracy: 0.9773 - val_loss: 0.1763 - val_accuracy: 0.9697 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9786 - val_loss: 0.2130 - val_accuracy: 0.9570 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1327 - accuracy: 0.9796 - val_loss: 0.2019 - val_accuracy: 0.9624 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9798 - val_loss: 0.2137 - val_accuracy: 0.9577 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1346 - accuracy: 0.9794 - val_loss: 0.2088 - val_accuracy: 0.9592 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1414 - accuracy: 0.9767 - val_loss: 0.2022 - val_accuracy: 0.9635 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1351 - accuracy: 0.9796 - val_loss: 0.1891 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1362 - accuracy: 0.9783 - val_loss: 0.1870 - val_accuracy: 0.9663 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1375 - accuracy: 0.9790 - val_loss: 0.2036 - val_accuracy: 0.9604 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9788 - val_loss: 0.2638 - val_accuracy: 0.9439 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1412 - accuracy: 0.9773 - val_loss: 0.2368 - val_accuracy: 0.9524 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9788 - val_loss: 0.2061 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9791 - val_loss: 0.1954 - val_accuracy: 0.9599 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1379 - accuracy: 0.9782 - val_loss: 0.2340 - val_accuracy: 0.9505 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9786 - val_loss: 0.2044 - val_accuracy: 0.9600 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 0.2382 - val_accuracy: 0.9500 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9797 - val_loss: 0.2104 - val_accuracy: 0.9573 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1366 - accuracy: 0.9785 - val_loss: 0.2074 - val_accuracy: 0.9620 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1375 - accuracy: 0.9779 - val_loss: 0.1875 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9804 - val_loss: 0.2207 - val_accuracy: 0.9550 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1382 - accuracy: 0.9782 - val_loss: 0.2341 - val_accuracy: 0.9523 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9785 - val_loss: 0.2124 - val_accuracy: 0.9580 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1318 - accuracy: 0.9800 - val_loss: 0.2386 - val_accuracy: 0.9488 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1350 - accuracy: 0.9793 - val_loss: 0.2044 - val_accuracy: 0.9617 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1359 - accuracy: 0.9782 - val_loss: 0.1935 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1347 - accuracy: 0.9789 - val_loss: 0.2173 - val_accuracy: 0.9581 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1344 - accuracy: 0.9780 - val_loss: 0.1939 - val_accuracy: 0.9616 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1291 - accuracy: 0.9785 - val_loss: 0.1725 - val_accuracy: 0.9660 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1289 - accuracy: 0.9788 - val_loss: 0.1798 - val_accuracy: 0.9664 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9801 - val_loss: 0.1709 - val_accuracy: 0.9671 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1246 - accuracy: 0.9800 - val_loss: 0.1719 - val_accuracy: 0.9689 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1264 - accuracy: 0.9791 - val_loss: 0.1871 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9799 - val_loss: 0.1820 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1229 - accuracy: 0.9804 - val_loss: 0.1782 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1240 - accuracy: 0.9801 - val_loss: 0.1816 - val_accuracy: 0.9623 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1234 - accuracy: 0.9801 - val_loss: 0.1778 - val_accuracy: 0.9653 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1216 - accuracy: 0.9804 - val_loss: 0.1733 - val_accuracy: 0.9670 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1247 - accuracy: 0.9793 - val_loss: 0.1760 - val_accuracy: 0.9679 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1221 - accuracy: 0.9803 - val_loss: 0.1821 - val_accuracy: 0.9658 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1238 - accuracy: 0.9801 - val_loss: 0.1779 - val_accuracy: 0.9657 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9805 - val_loss: 0.1819 - val_accuracy: 0.9644 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9801 - val_loss: 0.1679 - val_accuracy: 0.9692 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1211 - accuracy: 0.9804 - val_loss: 0.1812 - val_accuracy: 0.9655 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1232 - accuracy: 0.9800 - val_loss: 0.1694 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9805 - val_loss: 0.1739 - val_accuracy: 0.9658 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9806 - val_loss: 0.1581 - val_accuracy: 0.9710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1207 - accuracy: 0.9807 - val_loss: 0.1852 - val_accuracy: 0.9626 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9804 - val_loss: 0.2037 - val_accuracy: 0.9583 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1202 - accuracy: 0.9808 - val_loss: 0.1651 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1216 - accuracy: 0.9800 - val_loss: 0.1879 - val_accuracy: 0.9632 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1193 - accuracy: 0.9810 - val_loss: 0.1838 - val_accuracy: 0.9633 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1199 - accuracy: 0.9803 - val_loss: 0.1777 - val_accuracy: 0.9650 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1214 - accuracy: 0.9805 - val_loss: 0.1659 - val_accuracy: 0.9694 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9813 - val_loss: 0.1635 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1201 - accuracy: 0.9808 - val_loss: 0.1681 - val_accuracy: 0.9676 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1153 - accuracy: 0.9818 - val_loss: 0.2425 - val_accuracy: 0.9465 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9796 - val_loss: 0.1737 - val_accuracy: 0.9647 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9807 - val_loss: 0.1838 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1209 - accuracy: 0.9801 - val_loss: 0.1758 - val_accuracy: 0.9667 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1214 - accuracy: 0.9804 - val_loss: 0.1853 - val_accuracy: 0.9624 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1204 - accuracy: 0.9805 - val_loss: 0.1799 - val_accuracy: 0.9659 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1222 - accuracy: 0.9803 - val_loss: 0.1801 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1190 - accuracy: 0.9808 - val_loss: 0.1619 - val_accuracy: 0.9703 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9802 - val_loss: 0.1763 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9805 - val_loss: 0.1697 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1187 - accuracy: 0.9813 - val_loss: 0.2071 - val_accuracy: 0.9555 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1218 - accuracy: 0.9804 - val_loss: 0.1685 - val_accuracy: 0.9679 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9805 - val_loss: 0.1801 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1179 - accuracy: 0.9809 - val_loss: 0.1789 - val_accuracy: 0.9646 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9806 - val_loss: 0.1822 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1191 - accuracy: 0.9811 - val_loss: 0.1761 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1202 - accuracy: 0.9808 - val_loss: 0.1708 - val_accuracy: 0.9675 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1182 - accuracy: 0.9808 - val_loss: 0.1753 - val_accuracy: 0.9659 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1213 - accuracy: 0.9802 - val_loss: 0.1815 - val_accuracy: 0.9640 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1174 - accuracy: 0.9816 - val_loss: 0.1867 - val_accuracy: 0.9637 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1196 - accuracy: 0.9809 - val_loss: 0.1794 - val_accuracy: 0.9641 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1353 - accuracy: 0.9745 - val_loss: 0.1540 - val_accuracy: 0.9689 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9790 - val_loss: 0.1534 - val_accuracy: 0.9697 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1151 - accuracy: 0.9792 - val_loss: 0.1782 - val_accuracy: 0.9629 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1138 - accuracy: 0.9793 - val_loss: 0.1852 - val_accuracy: 0.9591 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1132 - accuracy: 0.9797 - val_loss: 0.1686 - val_accuracy: 0.9685 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1126 - accuracy: 0.9792 - val_loss: 0.1461 - val_accuracy: 0.9703 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1102 - accuracy: 0.9803 - val_loss: 0.1645 - val_accuracy: 0.9665 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1102 - accuracy: 0.9799 - val_loss: 0.1560 - val_accuracy: 0.9707 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1115 - accuracy: 0.9798 - val_loss: 0.1775 - val_accuracy: 0.9622 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1088 - accuracy: 0.9802 - val_loss: 0.1519 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1081 - accuracy: 0.9811 - val_loss: 0.1722 - val_accuracy: 0.9642 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1060 - accuracy: 0.9808 - val_loss: 0.1633 - val_accuracy: 0.9676 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1076 - accuracy: 0.9811 - val_loss: 0.1658 - val_accuracy: 0.9666 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1086 - accuracy: 0.9801 - val_loss: 0.1597 - val_accuracy: 0.9686 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9818 - val_loss: 0.1711 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1050 - accuracy: 0.9811 - val_loss: 0.1659 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1072 - accuracy: 0.9808 - val_loss: 0.1723 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1055 - accuracy: 0.9812 - val_loss: 0.1535 - val_accuracy: 0.9683 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1029 - accuracy: 0.9820 - val_loss: 0.1666 - val_accuracy: 0.9663 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1039 - accuracy: 0.9815 - val_loss: 0.1738 - val_accuracy: 0.9630 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1074 - accuracy: 0.9805 - val_loss: 0.1649 - val_accuracy: 0.9664 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9815 - val_loss: 0.1573 - val_accuracy: 0.9693 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9816 - val_loss: 0.1700 - val_accuracy: 0.9651 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1052 - accuracy: 0.9811 - val_loss: 0.1654 - val_accuracy: 0.9675 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9816 - val_loss: 0.1709 - val_accuracy: 0.9636 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1050 - accuracy: 0.9813 - val_loss: 0.1545 - val_accuracy: 0.9712 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9819 - val_loss: 0.1776 - val_accuracy: 0.9638 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - accuracy: 0.9811 - val_loss: 0.1567 - val_accuracy: 0.9670 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9817 - val_loss: 0.1644 - val_accuracy: 0.9660 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9821 - val_loss: 0.1997 - val_accuracy: 0.9577 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1071 - accuracy: 0.9804 - val_loss: 0.1656 - val_accuracy: 0.9674 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9811 - val_loss: 0.1561 - val_accuracy: 0.9692 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9811 - val_loss: 0.1532 - val_accuracy: 0.9693 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - accuracy: 0.9804 - val_loss: 0.1733 - val_accuracy: 0.9659 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9817 - val_loss: 0.1485 - val_accuracy: 0.9703 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9807 - val_loss: 0.1556 - val_accuracy: 0.9672 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9814 - val_loss: 0.1767 - val_accuracy: 0.9628 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1032 - accuracy: 0.9817 - val_loss: 0.1708 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1035 - accuracy: 0.9814 - val_loss: 0.1540 - val_accuracy: 0.9695 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1039 - accuracy: 0.9813 - val_loss: 0.1619 - val_accuracy: 0.9672 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1045 - accuracy: 0.9814 - val_loss: 0.1623 - val_accuracy: 0.9658 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1043 - accuracy: 0.9812 - val_loss: 0.1593 - val_accuracy: 0.9677 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1047 - accuracy: 0.9812 - val_loss: 0.1680 - val_accuracy: 0.9667 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1042 - accuracy: 0.9811 - val_loss: 0.1674 - val_accuracy: 0.9654 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1030 - accuracy: 0.9821 - val_loss: 0.1710 - val_accuracy: 0.9648 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1043 - accuracy: 0.9813 - val_loss: 0.1798 - val_accuracy: 0.9626 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1041 - accuracy: 0.9815 - val_loss: 0.1604 - val_accuracy: 0.9673 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9811 - val_loss: 0.1685 - val_accuracy: 0.9649 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1036 - accuracy: 0.9817 - val_loss: 0.1583 - val_accuracy: 0.9683 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1053 - accuracy: 0.9813 - val_loss: 0.1680 - val_accuracy: 0.9652 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 14ms/step - loss: 0.3376 - accuracy: 0.8644 - val_loss: 0.3164 - val_accuracy: 0.8685 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2828 - accuracy: 0.8747 - val_loss: 0.3200 - val_accuracy: 0.8629 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2746 - accuracy: 0.8759 - val_loss: 0.3120 - val_accuracy: 0.8654 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2700 - accuracy: 0.8768 - val_loss: 0.3056 - val_accuracy: 0.8670 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2667 - accuracy: 0.8778 - val_loss: 0.3121 - val_accuracy: 0.8655 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2647 - accuracy: 0.8778 - val_loss: 0.2978 - val_accuracy: 0.8738 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2610 - accuracy: 0.8791 - val_loss: 0.3033 - val_accuracy: 0.8714 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2617 - accuracy: 0.8791 - val_loss: 0.3104 - val_accuracy: 0.8706 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2587 - accuracy: 0.8795 - val_loss: 0.3041 - val_accuracy: 0.8710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2592 - accuracy: 0.8792 - val_loss: 0.3041 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2577 - accuracy: 0.8797 - val_loss: 0.3016 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2567 - accuracy: 0.8802 - val_loss: 0.3031 - val_accuracy: 0.8715 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2569 - accuracy: 0.8793 - val_loss: 0.2991 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2552 - accuracy: 0.8804 - val_loss: 0.3053 - val_accuracy: 0.8703 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2555 - accuracy: 0.8801 - val_loss: 0.3050 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2542 - accuracy: 0.8801 - val_loss: 0.3020 - val_accuracy: 0.8710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2561 - accuracy: 0.8793 - val_loss: 0.3124 - val_accuracy: 0.8717 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2552 - accuracy: 0.8803 - val_loss: 0.3100 - val_accuracy: 0.8700 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.8810 - val_loss: 0.3040 - val_accuracy: 0.8719 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2539 - accuracy: 0.8801 - val_loss: 0.2981 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2544 - accuracy: 0.8806 - val_loss: 0.3034 - val_accuracy: 0.8708 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2534 - accuracy: 0.8805 - val_loss: 0.3048 - val_accuracy: 0.8715 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2550 - accuracy: 0.8794 - val_loss: 0.3017 - val_accuracy: 0.8723 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2535 - accuracy: 0.8808 - val_loss: 0.3022 - val_accuracy: 0.8719 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2544 - accuracy: 0.8802 - val_loss: 0.2998 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2544 - accuracy: 0.8797 - val_loss: 0.3079 - val_accuracy: 0.8707 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2538 - accuracy: 0.8804 - val_loss: 0.3048 - val_accuracy: 0.8701 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2537 - accuracy: 0.8804 - val_loss: 0.3034 - val_accuracy: 0.8713 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.8806 - val_loss: 0.3079 - val_accuracy: 0.8725 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2531 - accuracy: 0.8811 - val_loss: 0.3145 - val_accuracy: 0.8689 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2529 - accuracy: 0.8802 - val_loss: 0.3071 - val_accuracy: 0.8701 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2540 - accuracy: 0.8804 - val_loss: 0.3083 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2521 - accuracy: 0.8801 - val_loss: 0.3104 - val_accuracy: 0.8702 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2521 - accuracy: 0.8805 - val_loss: 0.3056 - val_accuracy: 0.8710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2534 - accuracy: 0.8805 - val_loss: 0.3098 - val_accuracy: 0.8717 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2530 - accuracy: 0.8808 - val_loss: 0.3090 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2529 - accuracy: 0.8808 - val_loss: 0.3078 - val_accuracy: 0.8727 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2521 - accuracy: 0.8808 - val_loss: 0.2986 - val_accuracy: 0.8735 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2530 - accuracy: 0.8809 - val_loss: 0.3011 - val_accuracy: 0.8723 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2517 - accuracy: 0.8810 - val_loss: 0.3145 - val_accuracy: 0.8698 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2528 - accuracy: 0.8804 - val_loss: 0.3103 - val_accuracy: 0.8729 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2549 - accuracy: 0.8799 - val_loss: 0.3111 - val_accuracy: 0.8701 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8805 - val_loss: 0.3066 - val_accuracy: 0.8711 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8803 - val_loss: 0.2999 - val_accuracy: 0.8736 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8811 - val_loss: 0.2999 - val_accuracy: 0.8713 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8806 - val_loss: 0.3041 - val_accuracy: 0.8715 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8814 - val_loss: 0.3027 - val_accuracy: 0.8736 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2517 - accuracy: 0.8807 - val_loss: 0.3189 - val_accuracy: 0.8694 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2543 - accuracy: 0.8804 - val_loss: 0.3046 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8807 - val_loss: 0.3055 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2708 - accuracy: 0.8750 - val_loss: 0.3093 - val_accuracy: 0.8690 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2594 - accuracy: 0.8777 - val_loss: 0.2958 - val_accuracy: 0.8710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2578 - accuracy: 0.8785 - val_loss: 0.3025 - val_accuracy: 0.8690 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2576 - accuracy: 0.8785 - val_loss: 0.2971 - val_accuracy: 0.8700 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2547 - accuracy: 0.8786 - val_loss: 0.3055 - val_accuracy: 0.8698 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2564 - accuracy: 0.8784 - val_loss: 0.3059 - val_accuracy: 0.8692 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2561 - accuracy: 0.8783 - val_loss: 0.2958 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2539 - accuracy: 0.8793 - val_loss: 0.2962 - val_accuracy: 0.8723 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2536 - accuracy: 0.8790 - val_loss: 0.2947 - val_accuracy: 0.8720 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2545 - accuracy: 0.8786 - val_loss: 0.2905 - val_accuracy: 0.8729 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 4s 16ms/step - loss: 0.2542 - accuracy: 0.8784 - val_loss: 0.2963 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2535 - accuracy: 0.8792 - val_loss: 0.2910 - val_accuracy: 0.8725 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2529 - accuracy: 0.8791 - val_loss: 0.2960 - val_accuracy: 0.8713 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2519 - accuracy: 0.8793 - val_loss: 0.2981 - val_accuracy: 0.8717 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2533 - accuracy: 0.8793 - val_loss: 0.2949 - val_accuracy: 0.8726 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2533 - accuracy: 0.8789 - val_loss: 0.2991 - val_accuracy: 0.8715 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2525 - accuracy: 0.8788 - val_loss: 0.2980 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8795 - val_loss: 0.2986 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2520 - accuracy: 0.8798 - val_loss: 0.2928 - val_accuracy: 0.8726 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.8794 - val_loss: 0.2978 - val_accuracy: 0.8714 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2527 - accuracy: 0.8796 - val_loss: 0.2951 - val_accuracy: 0.8720 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2520 - accuracy: 0.8796 - val_loss: 0.2864 - val_accuracy: 0.8740 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2518 - accuracy: 0.8793 - val_loss: 0.3002 - val_accuracy: 0.8713 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8792 - val_loss: 0.2924 - val_accuracy: 0.8715 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8793 - val_loss: 0.2976 - val_accuracy: 0.8712 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8799 - val_loss: 0.2936 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.8795 - val_loss: 0.2920 - val_accuracy: 0.8735 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8794 - val_loss: 0.2919 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2524 - accuracy: 0.8787 - val_loss: 0.2948 - val_accuracy: 0.8717 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2513 - accuracy: 0.8789 - val_loss: 0.2894 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8793 - val_loss: 0.2949 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2522 - accuracy: 0.8789 - val_loss: 0.2901 - val_accuracy: 0.8725 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 12ms/step - loss: 0.2519 - accuracy: 0.8794 - val_loss: 0.2949 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2516 - accuracy: 0.8796 - val_loss: 0.2925 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2526 - accuracy: 0.8789 - val_loss: 0.2914 - val_accuracy: 0.8719 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8792 - val_loss: 0.2940 - val_accuracy: 0.8735 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8793 - val_loss: 0.2921 - val_accuracy: 0.8731 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2524 - accuracy: 0.8794 - val_loss: 0.2941 - val_accuracy: 0.8740 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2525 - accuracy: 0.8793 - val_loss: 0.2941 - val_accuracy: 0.8719 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2517 - accuracy: 0.8795 - val_loss: 0.2932 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2507 - accuracy: 0.8796 - val_loss: 0.2983 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8797 - val_loss: 0.2927 - val_accuracy: 0.8729 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.8796 - val_loss: 0.2931 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8794 - val_loss: 0.2922 - val_accuracy: 0.8728 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2507 - accuracy: 0.8800 - val_loss: 0.2963 - val_accuracy: 0.8732 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8792 - val_loss: 0.2939 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8790 - val_loss: 0.2887 - val_accuracy: 0.8731 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2508 - accuracy: 0.8796 - val_loss: 0.2912 - val_accuracy: 0.8725 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8796 - val_loss: 0.2923 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8791 - val_loss: 0.2934 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8793 - val_loss: 0.2923 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8798 - val_loss: 0.2969 - val_accuracy: 0.8710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2520 - accuracy: 0.8787 - val_loss: 0.2963 - val_accuracy: 0.8729 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2498 - accuracy: 0.8797 - val_loss: 0.2954 - val_accuracy: 0.8723 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2523 - accuracy: 0.8792 - val_loss: 0.2935 - val_accuracy: 0.8728 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8794 - val_loss: 0.2900 - val_accuracy: 0.8731 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8794 - val_loss: 0.2913 - val_accuracy: 0.8734 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2515 - accuracy: 0.8790 - val_loss: 0.2904 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8791 - val_loss: 0.2935 - val_accuracy: 0.8733 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2496 - accuracy: 0.8799 - val_loss: 0.3006 - val_accuracy: 0.8712 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2522 - accuracy: 0.8792 - val_loss: 0.2954 - val_accuracy: 0.8719 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2500 - accuracy: 0.8792 - val_loss: 0.2959 - val_accuracy: 0.8728 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2526 - accuracy: 0.8789 - val_loss: 0.2982 - val_accuracy: 0.8711 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8797 - val_loss: 0.2934 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2514 - accuracy: 0.8796 - val_loss: 0.2993 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2516 - accuracy: 0.8796 - val_loss: 0.2956 - val_accuracy: 0.8711 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2508 - accuracy: 0.8796 - val_loss: 0.2956 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8794 - val_loss: 0.3064 - val_accuracy: 0.8701 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 4s 15ms/step - loss: 0.2513 - accuracy: 0.8792 - val_loss: 0.2921 - val_accuracy: 0.8720 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2507 - accuracy: 0.8798 - val_loss: 0.2969 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8787 - val_loss: 0.2913 - val_accuracy: 0.8730 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2507 - accuracy: 0.8794 - val_loss: 0.2924 - val_accuracy: 0.8728 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8793 - val_loss: 0.2951 - val_accuracy: 0.8730 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2500 - accuracy: 0.8793 - val_loss: 0.2927 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2513 - accuracy: 0.8790 - val_loss: 0.2940 - val_accuracy: 0.8720 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2501 - accuracy: 0.8794 - val_loss: 0.2989 - val_accuracy: 0.8704 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2502 - accuracy: 0.8798 - val_loss: 0.2937 - val_accuracy: 0.8717 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8790 - val_loss: 0.2961 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8796 - val_loss: 0.2953 - val_accuracy: 0.8713 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2513 - accuracy: 0.8797 - val_loss: 0.2959 - val_accuracy: 0.8717 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2503 - accuracy: 0.8800 - val_loss: 0.2932 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2511 - accuracy: 0.8792 - val_loss: 0.2963 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8792 - val_loss: 0.2930 - val_accuracy: 0.8721 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8796 - val_loss: 0.2981 - val_accuracy: 0.8725 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8797 - val_loss: 0.2926 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8791 - val_loss: 0.2925 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2502 - accuracy: 0.8794 - val_loss: 0.2947 - val_accuracy: 0.8727 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2500 - accuracy: 0.8799 - val_loss: 0.2939 - val_accuracy: 0.8718 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8795 - val_loss: 0.2905 - val_accuracy: 0.8724 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2491 - accuracy: 0.8795 - val_loss: 0.2951 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2509 - accuracy: 0.8790 - val_loss: 0.2999 - val_accuracy: 0.8713 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2506 - accuracy: 0.8793 - val_loss: 0.2997 - val_accuracy: 0.8705 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2512 - accuracy: 0.8790 - val_loss: 0.2913 - val_accuracy: 0.8722 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2510 - accuracy: 0.8793 - val_loss: 0.2917 - val_accuracy: 0.8733 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2498 - accuracy: 0.8794 - val_loss: 0.2888 - val_accuracy: 0.8727 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2503 - accuracy: 0.8796 - val_loss: 0.2921 - val_accuracy: 0.8723 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2505 - accuracy: 0.8793 - val_loss: 0.2980 - val_accuracy: 0.8716 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2502 - accuracy: 0.8790 - val_loss: 0.2933 - val_accuracy: 0.8729 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2498 - accuracy: 0.8792 - val_loss: 0.2953 - val_accuracy: 0.8720 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2499 - accuracy: 0.8791 - val_loss: 0.2960 - val_accuracy: 0.8714 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 5s 15ms/step - loss: 9.3065e-04 - accuracy: 0.9998 - val_loss: 0.0961 - val_accuracy: 0.9842 [ 0. 0. 0. ... 0.3898531 -0.5158259 -0. ] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5145e-04 - accuracy: 1.0000 - val_loss: 0.0933 - val_accuracy: 0.9853 [ 0. 0. 0. ... 0.3905899 -0.5202818 0. ] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0105e-04 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9847 [ 0. 0. 0. ... 0.39188322 -0.5325534 0. ] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1370e-05 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.39256585 -0.53411955 0. ] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9492e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.3930106 -0.5353843 0. ] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 3s 14ms/step - loss: 9.3030e-05 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9846 [ 0. 0. 0. ... 0.39384645 -0.5486798 0. ] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 3s 14ms/step - loss: 5.3987e-05 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9838 [ 0. 0. 0. ... 0.39495772 -0.5329883 0. ] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0273e-04 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9855 [ 0. 0. 0. ... 0.3954649 -0.55066806 -0. ] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7853e-05 - accuracy: 1.0000 - val_loss: 0.0978 - val_accuracy: 0.9858 [ 0. 0. 0. ... 0.40370035 -0.55238694 0. ] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 4s 15ms/step - loss: 2.0408e-05 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9859 [ 0. 0. 0. ... 0.40298137 -0.5545618 -0. ] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5724e-05 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 0.9855 [ 0. 0. 0. ... 0.40389735 -0.5549969 0. ] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5162e-05 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9859 [ 0. 0. 0. ... 0.40254235 -0.557172 0. ] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2032e-05 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9864 [ 0. 0. 0. ... 0.4047065 -0.55779934 0. ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 3s 14ms/step - loss: 9.4705e-06 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9867 [ 0. 0. 0. ... 0.40486833 -0.5586599 -0. ] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 3s 14ms/step - loss: 9.0066e-06 - accuracy: 1.0000 - val_loss: 0.0974 - val_accuracy: 0.9867 [ 0. 0. 0. ... 0.40514544 -0.5611765 0. ] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 3s 14ms/step - loss: 7.1312e-06 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9866 [ 0. 0. 0. ... 0.40497485 -0.56159836 -0. ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 3s 14ms/step - loss: 7.0004e-06 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9866 [ 0. 0. 0. ... 0.40563738 -0.56294185 0. ] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4492e-06 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9863 [ 0. 0. 0. ... 0.40620402 -0.56633073 0. ] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6537e-06 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9860 [ 0. 0. 0. ... 0.40628505 -0.5641691 0. ] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 3s 14ms/step - loss: 4.8383e-06 - accuracy: 1.0000 - val_loss: 0.0986 - val_accuracy: 0.9861 [ 0. 0. 0. ... 0.40684065 -0.5670016 -0. ] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2661e-06 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.4069241 -0.56900525 0. ] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.1341 - val_accuracy: 0.9818 [ 0. 0. 0. ... 0.40220156 -0.5614891 -0. ] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 3s 14ms/step - loss: 9.0384e-04 - accuracy: 0.9997 - val_loss: 0.1113 - val_accuracy: 0.9837 [ 0. 0. 0. ... 0.43992987 -0.58493453 -0. ] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6319e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9841 [ 0. 0. 0. ... 0.42477137 -0.58696556 -0. ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5259e-05 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9845 [ 0. 0. 0. ... 0.42555785 -0.5861252 -0. ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 3s 14ms/step - loss: 4.1701e-05 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.42829758 -0.58598495 -0. ] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 3s 15ms/step - loss: 2.9270e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.4286086 -0.5854041 -0. ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6889e-05 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.42776722 -0.5870525 0. ] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1721e-05 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9853 [ 0. 0. 0. ... 0.42749962 -0.58762914 -0. ] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5659e-05 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9855 [ 0. 0. 0. ... 0.4256516 -0.5887176 -0. ] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8327e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.425942 -0.59087926 -0. ] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0313e-05 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9856 [ 0. 0. 0. ... 0.4334331 -0.59113204 -0. ] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 4s 15ms/step - loss: 1.5490e-05 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.43352455 -0.59114486 0. ] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 3s 14ms/step - loss: 8.1654e-06 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.433654 -0.5916816 0. ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 3s 14ms/step - loss: 8.7518e-06 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9847 [ 0. 0. 0. ... 0.43381184 -0.592908 0. ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3490e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9848 [ 0. 0. 0. ... 0.43437967 -0.59319997 -0. ] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7454e-06 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9847 [ 0. 0. 0. ... 0.43606937 -0.5939255 -0. ] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 3s 15ms/step - loss: 5.3795e-06 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.4362031 -0.5946545 0. ] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9587e-06 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.43685165 -0.5951368 -0. ] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 4s 15ms/step - loss: 7.6090e-06 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.4322403 -0.5967824 0. ] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0790e-06 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.4322308 -0.5963704 0. ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2555e-05 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9844 [ 0. 0. 0. ... 0.45027718 -0.5971051 0. ] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.1289 - val_accuracy: 0.9819 [ 0. 0. 0. ... 0.4082035 -0.6209362 -0. ] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 4s 15ms/step - loss: 9.4691e-04 - accuracy: 0.9997 - val_loss: 0.1201 - val_accuracy: 0.9824 [ 0. 0. 0. ... 0.4171047 -0.6409388 0. ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 3s 15ms/step - loss: 8.4172e-05 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9842 [ 0. 0. 0. ... 0.4198018 -0.648477 0. ] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 3s 15ms/step - loss: 2.7153e-05 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9845 [ 0. 0. 0. ... 0.42114583 -0.6493258 -0. ] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 3s 15ms/step - loss: 1.7241e-05 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9847 [ 0. 0. 0. ... 0.42167825 -0.64995426 -0. ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3804e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.4220452 -0.65079767 -0. ] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4137e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.4232686 -0.6508494 -0. ] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1011e-05 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.42367133 -0.6513359 0. ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0996 - val_accuracy: 0.9825 [ 0. 0. 0. ... 0.46597597 -0.6151356 -0. ] Sparsity at: 0.6458602554470323 Epoch 52/500 235/235 [==============================] - 3s 14ms/step - loss: 8.3775e-04 - accuracy: 0.9998 - val_loss: 0.0939 - val_accuracy: 0.9845 [ 0. 0. 0. ... 0.45949364 -0.6158515 -0. ] Sparsity at: 0.6458602554470323 Epoch 53/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5514e-04 - accuracy: 1.0000 - val_loss: 0.0923 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.45768076 -0.62133193 0. ] Sparsity at: 0.6458602554470323 Epoch 54/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3714e-04 - accuracy: 1.0000 - val_loss: 0.0907 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.45995775 -0.6151077 0. ] Sparsity at: 0.6458602554470323 Epoch 55/500 235/235 [==============================] - 3s 14ms/step - loss: 7.4072e-05 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9856 [ 0. 0. 0. ... 0.46029535 -0.6195626 0. ] Sparsity at: 0.6458602554470323 Epoch 56/500 235/235 [==============================] - 3s 15ms/step - loss: 7.7038e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.45911187 -0.61793983 0. ] Sparsity at: 0.6458602554470323 Epoch 57/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2050e-05 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.45968363 -0.6211928 0. ] Sparsity at: 0.6458602554470323 Epoch 58/500 235/235 [==============================] - 3s 15ms/step - loss: 4.5488e-05 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.45955315 -0.6232606 0. ] Sparsity at: 0.6458602554470323 Epoch 59/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5487e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.45922405 -0.6263509 0. ] Sparsity at: 0.6458602554470323 Epoch 60/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0614e-05 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9848 [ 0. 0. 0. ... 0.46245384 -0.629474 0. ] Sparsity at: 0.6458602554470323 Epoch 61/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0954e-05 - accuracy: 1.0000 - val_loss: 0.0916 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.46376142 -0.63020724 0. ] Sparsity at: 0.6458602554470323 Epoch 62/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6236e-05 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9848 [ 0. 0. 0. ... 0.46419 -0.6319635 0. ] Sparsity at: 0.6458602554470323 Epoch 63/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2754e-05 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.46525973 -0.6335181 0. ] Sparsity at: 0.6458602554470323 Epoch 64/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3744e-05 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.46918952 -0.6317435 0. ] Sparsity at: 0.6458602554470323 Epoch 65/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1919e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.46811703 -0.6332109 0. ] Sparsity at: 0.6458602554470323 Epoch 66/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1026e-05 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.46809447 -0.6363593 -0. ] Sparsity at: 0.6458602554470323 Epoch 67/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6383e-05 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.46822533 -0.63829225 0. ] Sparsity at: 0.6458602554470323 Epoch 68/500 235/235 [==============================] - 4s 15ms/step - loss: 1.4273e-05 - accuracy: 1.0000 - val_loss: 0.0935 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.4686195 -0.638418 0. ] Sparsity at: 0.6458602554470323 Epoch 69/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1813e-05 - accuracy: 1.0000 - val_loss: 0.0933 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.46934253 -0.63940203 0. ] Sparsity at: 0.6458602554470323 Epoch 70/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1235e-05 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.47278318 -0.64200133 -0. ] Sparsity at: 0.6458602554470323 Epoch 71/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3956e-05 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9847 [ 0. 0. 0. ... 0.47114527 -0.6465881 0. ] Sparsity at: 0.6458602554470323 Epoch 72/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0105e-05 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 0.9848 [ 0. 0. 0. ... 0.47417617 -0.6473242 0. ] Sparsity at: 0.6458602554470323 Epoch 73/500 235/235 [==============================] - 3s 15ms/step - loss: 8.0636e-06 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9849 [ 0. 0. 0. ... 0.4743592 -0.6472893 0. ] Sparsity at: 0.6458602554470323 Epoch 74/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7326e-05 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9835 [ 0. 0. 0. ... 0.4969626 -0.6483071 -0. ] Sparsity at: 0.6458602554470323 Epoch 75/500 235/235 [==============================] - 3s 14ms/step - loss: 3.8910e-04 - accuracy: 0.9999 - val_loss: 0.1076 - val_accuracy: 0.9835 [ 0. 0. 0. ... 0.50154227 -0.6594645 0. ] Sparsity at: 0.6458602554470323 Epoch 76/500 235/235 [==============================] - 3s 14ms/step - loss: 1.1671e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9843 [ 0. 0. 0. ... 0.4979294 -0.69436276 -0. ] Sparsity at: 0.6458602554470323 Epoch 77/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2297e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.49719995 -0.68884367 -0. ] Sparsity at: 0.6458602554470323 Epoch 78/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3438e-05 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.4970855 -0.6894904 -0. ] Sparsity at: 0.6458602554470323 Epoch 79/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2293e-05 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.49784008 -0.6892401 0. ] Sparsity at: 0.6458602554470323 Epoch 80/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9657e-05 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.49978006 -0.69144285 -0. ] Sparsity at: 0.6458602554470323 Epoch 81/500 235/235 [==============================] - 3s 14ms/step - loss: 7.6235e-06 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.49989307 -0.69287497 -0. ] Sparsity at: 0.6458602554470323 Epoch 82/500 235/235 [==============================] - 3s 14ms/step - loss: 6.4158e-06 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.5002293 -0.6932928 -0. ] Sparsity at: 0.6458602554470323 Epoch 83/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5203e-06 - accuracy: 1.0000 - val_loss: 0.1030 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.50026053 -0.6950041 0. ] Sparsity at: 0.6458602554470323 Epoch 84/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7740e-06 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.50018084 -0.6954591 -0. ] Sparsity at: 0.6458602554470323 Epoch 85/500 235/235 [==============================] - 3s 14ms/step - loss: 4.9754e-06 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.5000696 -0.6959325 -0. ] Sparsity at: 0.6458602554470323 Epoch 86/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6527e-06 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.50016165 -0.6957323 -0. ] Sparsity at: 0.6458602554470323 Epoch 87/500 235/235 [==============================] - 3s 14ms/step - loss: 5.9431e-06 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.5012834 -0.6963597 0. ] Sparsity at: 0.6458602554470323 Epoch 88/500 235/235 [==============================] - 3s 14ms/step - loss: 7.3387e-05 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9842 [ 0. 0. 0. ... 0.4997874 -0.6974817 0. ] Sparsity at: 0.6458602554470323 Epoch 89/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9232e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9847 [ 0. 0. 0. ... 0.50163746 -0.7015947 0. ] Sparsity at: 0.6458602554470323 Epoch 90/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9061e-06 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.50159854 -0.7011329 0. ] Sparsity at: 0.6458602554470323 Epoch 91/500 235/235 [==============================] - 3s 14ms/step - loss: 7.7169e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.50412345 -0.7056029 -0. ] Sparsity at: 0.6458602554470323 Epoch 92/500 235/235 [==============================] - 3s 14ms/step - loss: 4.0730e-06 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9853 [ 0. 0. 0. ... 0.50409067 -0.7064393 0. ] Sparsity at: 0.6458602554470323 Epoch 93/500 235/235 [==============================] - 3s 14ms/step - loss: 3.5732e-06 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.50450134 -0.70622724 0. ] Sparsity at: 0.6458602554470323 Epoch 94/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7936e-06 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.50526816 -0.70619774 0. ] Sparsity at: 0.6458602554470323 Epoch 95/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2695e-06 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9852 [ 0. 0. 0. ... 0.50399023 -0.70585406 0. ] Sparsity at: 0.6458602554470323 Epoch 96/500 235/235 [==============================] - 3s 14ms/step - loss: 2.2315e-06 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9854 [ 0. 0. 0. ... 0.50428575 -0.7084808 0. ] Sparsity at: 0.6458602554470323 Epoch 97/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0315e-06 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9855 [ 0. 0. 0. ... 0.5048268 -0.70761704 0. ] Sparsity at: 0.6458602554470323 Epoch 98/500 235/235 [==============================] - 3s 15ms/step - loss: 4.9889e-06 - accuracy: 1.0000 - val_loss: 0.1109 - val_accuracy: 0.9851 [ 0. 0. 0. ... 0.50511247 -0.7122094 0. ] Sparsity at: 0.6458602554470323 Epoch 99/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0651e-06 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9850 [ 0. 0. 0. ... 0.50402176 -0.71068704 -0. ] Sparsity at: 0.6458602554470323 Epoch 100/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4412e-06 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9853 [ 0. 0. 0. ... 0.50164115 -0.7095205 0. ] Sparsity at: 0.6458602554470323 Epoch 101/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0086 - accuracy: 0.9972 - val_loss: 0.1061 - val_accuracy: 0.9825 [ 0. 0. 0. ... 0. -0.6716096 -0. ] Sparsity at: 0.7594515401953419 Epoch 102/500 235/235 [==============================] - 3s 14ms/step - loss: 9.7591e-04 - accuracy: 0.9998 - val_loss: 0.1060 - val_accuracy: 0.9828 [ 0. 0. 0. ... 0. -0.6723734 0. ] Sparsity at: 0.7594515401953419 Epoch 103/500 235/235 [==============================] - 4s 15ms/step - loss: 2.8747e-04 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9825 [ 0. 0. 0. ... 0. -0.6713119 0. ] Sparsity at: 0.7594515401953419 Epoch 104/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9578e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9826 [ 0. 0. 0. ... 0. -0.67197144 0. ] Sparsity at: 0.7594515401953419 Epoch 105/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6361e-04 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9825 [ 0. 0. 0. ... 0. -0.67481494 0. ] Sparsity at: 0.7594515401953419 Epoch 106/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9178e-04 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9832 [ 0. 0. 0. ... 0. -0.6761899 0. ] Sparsity at: 0.7594515401953419 Epoch 107/500 235/235 [==============================] - 3s 14ms/step - loss: 1.4322e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9828 [ 0. 0. 0. ... 0. -0.6750394 0. ] Sparsity at: 0.7594515401953419 Epoch 108/500 235/235 [==============================] - 3s 14ms/step - loss: 8.9606e-05 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9830 [ 0. 0. 0. ... -0. -0.6754024 0. ] Sparsity at: 0.7594515401953419 Epoch 109/500 235/235 [==============================] - 3s 14ms/step - loss: 8.7648e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9827 [ 0. 0. 0. ... 0. -0.6736307 0. ] Sparsity at: 0.7594515401953419 Epoch 110/500 235/235 [==============================] - 3s 14ms/step - loss: 8.4442e-05 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9828 [ 0. 0. 0. ... 0. -0.6733476 0. ] Sparsity at: 0.7594515401953419 Epoch 111/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0343e-04 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9828 [ 0. 0. 0. ... -0. -0.67449695 0. ] Sparsity at: 0.7594515401953419 Epoch 112/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0751e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9826 [ 0. 0. 0. ... -0. -0.6736327 0. ] Sparsity at: 0.7594515401953419 Epoch 113/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4871e-05 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9830 [ 0. 0. 0. ... -0. -0.6721374 0. ] Sparsity at: 0.7594515401953419 Epoch 114/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2220e-05 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9834 [ 0. 0. 0. ... -0. -0.66964775 0. ] Sparsity at: 0.7594515401953419 Epoch 115/500 235/235 [==============================] - 4s 15ms/step - loss: 4.5787e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9829 [ 0. 0. 0. ... 0. -0.6710673 0. ] Sparsity at: 0.7594515401953419 Epoch 116/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9035e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9838 [ 0. 0. 0. ... 0. -0.6726765 0. ] Sparsity at: 0.7594515401953419 Epoch 117/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2739e-05 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9834 [ 0. 0. 0. ... 0. -0.6735285 0. ] Sparsity at: 0.7594515401953419 Epoch 118/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4427e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9836 [ 0. 0. 0. ... 0. -0.6752823 0. ] Sparsity at: 0.7594515401953419 Epoch 119/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8867e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9838 [ 0. 0. 0. ... 0. -0.677676 0. ] Sparsity at: 0.7594515401953419 Epoch 120/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5994e-05 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9831 [ 0. 0. 0. ... 0. -0.674391 -0. ] Sparsity at: 0.7594515401953419 Epoch 121/500 235/235 [==============================] - 3s 14ms/step - loss: 2.6212e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9831 [ 0. 0. 0. ... 0. -0.67842406 0. ] Sparsity at: 0.7594515401953419 Epoch 122/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5125e-05 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9834 [ 0. 0. 0. ... -0. -0.6812905 0. ] Sparsity at: 0.7594515401953419 Epoch 123/500 235/235 [==============================] - 3s 14ms/step - loss: 1.9176e-05 - accuracy: 1.0000 - val_loss: 0.1094 - val_accuracy: 0.9839 [ 0. 0. 0. ... -0. -0.6828214 0. ] Sparsity at: 0.7594515401953419 Epoch 124/500 235/235 [==============================] - 3s 15ms/step - loss: 2.2365e-05 - accuracy: 1.0000 - val_loss: 0.1114 - val_accuracy: 0.9831 [ 0. 0. 0. ... -0. -0.68508834 0. ] Sparsity at: 0.7594515401953419 Epoch 125/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0145e-05 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9838 [ 0. 0. 0. ... 0. -0.6870583 -0. ] Sparsity at: 0.7594515401953419 Epoch 126/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3480e-05 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9835 [ 0. 0. 0. ... 0. -0.6871926 0. ] Sparsity at: 0.7594515401953419 Epoch 127/500 235/235 [==============================] - 4s 16ms/step - loss: 1.3140e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9836 [ 0. 0. 0. ... 0. -0.6906433 -0. ] Sparsity at: 0.7594515401953419 Epoch 128/500 235/235 [==============================] - 4s 17ms/step - loss: 1.1276e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9839 [ 0. 0. 0. ... 0. -0.6931372 0. ] Sparsity at: 0.7594515401953419 Epoch 129/500 235/235 [==============================] - 4s 17ms/step - loss: 1.0294e-05 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9834 [ 0. 0. 0. ... 0. -0.6970223 0. ] Sparsity at: 0.7594515401953419 Epoch 130/500 235/235 [==============================] - 4s 17ms/step - loss: 8.1668e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9833 [ 0. 0. 0. ... 0. -0.69800645 0. ] Sparsity at: 0.7594515401953419 Epoch 131/500 235/235 [==============================] - 4s 15ms/step - loss: 7.9750e-06 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9835 [ 0. 0. 0. ... -0. -0.69682264 0. ] Sparsity at: 0.7594515401953419 Epoch 132/500 235/235 [==============================] - 4s 15ms/step - loss: 6.8324e-06 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9836 [ 0. 0. 0. ... 0. -0.6987585 0. ] Sparsity at: 0.7594515401953419 Epoch 133/500 235/235 [==============================] - 4s 16ms/step - loss: 5.6998e-06 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9832 [ 0. 0. 0. ... 0. -0.69986844 0. ] Sparsity at: 0.7594515401953419 Epoch 134/500 235/235 [==============================] - 4s 16ms/step - loss: 6.0706e-06 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9834 [ 0. 0. 0. ... 0. -0.7011575 0. ] Sparsity at: 0.7594515401953419 Epoch 135/500 235/235 [==============================] - 4s 16ms/step - loss: 5.3144e-06 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9832 [ 0. 0. 0. ... 0. -0.70320445 0. ] Sparsity at: 0.7594515401953419 Epoch 136/500 235/235 [==============================] - 4s 15ms/step - loss: 6.4171e-06 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9836 [ 0. 0. 0. ... 0. -0.7045949 0. ] Sparsity at: 0.7594515401953419 Epoch 137/500 235/235 [==============================] - 4s 16ms/step - loss: 5.5977e-06 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9835 [ 0. 0. 0. ... 0. -0.70330656 0. ] Sparsity at: 0.7594515401953419 Epoch 138/500 235/235 [==============================] - 4s 16ms/step - loss: 1.6211e-04 - accuracy: 0.9999 - val_loss: 0.1342 - val_accuracy: 0.9821 [ 0. 0. 0. ... 0. -0.69383526 -0. ] Sparsity at: 0.7594515401953419 Epoch 139/500 235/235 [==============================] - 4s 16ms/step - loss: 2.0013e-04 - accuracy: 0.9999 - val_loss: 0.1317 - val_accuracy: 0.9828 [ 0. 0. 0. ... 0. -0.69614774 0. ] Sparsity at: 0.7594515401953419 Epoch 140/500 235/235 [==============================] - 4s 16ms/step - loss: 3.1898e-04 - accuracy: 0.9999 - val_loss: 0.1262 - val_accuracy: 0.9825 [ 0. 0. 0. ... 0. -0.6919799 0. ] Sparsity at: 0.7594515401953419 Epoch 141/500 235/235 [==============================] - 4s 16ms/step - loss: 1.1368e-04 - accuracy: 1.0000 - val_loss: 0.1234 - val_accuracy: 0.9838 [ 0. 0. 0. ... 0. -0.6921864 -0. ] Sparsity at: 0.7594515401953419 Epoch 142/500 235/235 [==============================] - 4s 16ms/step - loss: 1.8096e-05 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9838 [ 0. 0. 0. ... 0. -0.6934519 0. ] Sparsity at: 0.7594515401953419 Epoch 143/500 235/235 [==============================] - 4s 16ms/step - loss: 4.2352e-05 - accuracy: 1.0000 - val_loss: 0.1288 - val_accuracy: 0.9833 [ 0. 0. 0. ... -0. -0.6941465 0. ] Sparsity at: 0.7594515401953419 Epoch 144/500 235/235 [==============================] - 4s 16ms/step - loss: 4.3826e-05 - accuracy: 1.0000 - val_loss: 0.1232 - val_accuracy: 0.9839 [ 0. 0. 0. ... 0. -0.7012039 0. ] Sparsity at: 0.7594515401953419 Epoch 145/500 235/235 [==============================] - 4s 16ms/step - loss: 1.6384e-05 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9835 [ 0. 0. 0. ... -0. -0.7015288 0. ] Sparsity at: 0.7594515401953419 Epoch 146/500 235/235 [==============================] - 4s 15ms/step - loss: 1.7483e-04 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9829 [ 0. 0. 0. ... -0. -0.70105296 -0. ] Sparsity at: 0.7594515401953419 Epoch 147/500 235/235 [==============================] - 5s 21ms/step - loss: 9.4821e-05 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9832 [ 0. 0. 0. ... -0. -0.7016805 0. ] Sparsity at: 0.7594515401953419 Epoch 148/500 235/235 [==============================] - 4s 18ms/step - loss: 1.2213e-05 - accuracy: 1.0000 - val_loss: 0.1256 - val_accuracy: 0.9835 [ 0. 0. 0. ... -0. -0.70687497 0. ] Sparsity at: 0.7594515401953419 Epoch 149/500 235/235 [==============================] - 4s 17ms/step - loss: 8.3476e-06 - accuracy: 1.0000 - val_loss: 0.1259 - val_accuracy: 0.9835 [ 0. 0. 0. ... -0. -0.7079566 -0. ] Sparsity at: 0.7594515401953419 Epoch 150/500 235/235 [==============================] - 4s 16ms/step - loss: 8.7716e-06 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9832 [ 0. 0. 0. ... 0. -0.7077201 0. ] Sparsity at: 0.7594515401953419 Epoch 151/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0236 - accuracy: 0.9929 - val_loss: 0.1132 - val_accuracy: 0.9802 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 152/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1126 - val_accuracy: 0.9815 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 153/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.1121 - val_accuracy: 0.9809 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 154/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1119 - val_accuracy: 0.9807 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 155/500 235/235 [==============================] - 3s 14ms/step - loss: 8.5730e-04 - accuracy: 0.9999 - val_loss: 0.1120 - val_accuracy: 0.9805 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 156/500 235/235 [==============================] - 3s 15ms/step - loss: 7.2073e-04 - accuracy: 0.9999 - val_loss: 0.1121 - val_accuracy: 0.9810 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 157/500 235/235 [==============================] - 3s 14ms/step - loss: 5.8185e-04 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9808 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 158/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0905e-04 - accuracy: 0.9999 - val_loss: 0.1127 - val_accuracy: 0.9817 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 159/500 235/235 [==============================] - 3s 14ms/step - loss: 4.6181e-04 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9812 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 160/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6286e-04 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9812 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 161/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2952e-04 - accuracy: 1.0000 - val_loss: 0.1153 - val_accuracy: 0.9811 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 162/500 235/235 [==============================] - 3s 14ms/step - loss: 2.8115e-04 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9813 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 163/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5485e-04 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9816 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 164/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5650e-04 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9815 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 165/500 235/235 [==============================] - 3s 15ms/step - loss: 2.1671e-04 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9816 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 166/500 235/235 [==============================] - 3s 14ms/step - loss: 1.7602e-04 - accuracy: 1.0000 - val_loss: 0.1168 - val_accuracy: 0.9818 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 167/500 235/235 [==============================] - 3s 14ms/step - loss: 1.6571e-04 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9816 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 168/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3040e-04 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9818 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 169/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3042e-04 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9817 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 170/500 235/235 [==============================] - 4s 15ms/step - loss: 1.1553e-04 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9821 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 171/500 235/235 [==============================] - 3s 15ms/step - loss: 1.2115e-04 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9820 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 172/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5756e-04 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9814 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 173/500 235/235 [==============================] - 4s 16ms/step - loss: 8.7537e-05 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9818 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 174/500 235/235 [==============================] - 3s 15ms/step - loss: 7.7734e-05 - accuracy: 1.0000 - val_loss: 0.1225 - val_accuracy: 0.9821 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 175/500 235/235 [==============================] - 3s 15ms/step - loss: 5.8022e-05 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9824 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 176/500 235/235 [==============================] - 3s 14ms/step - loss: 5.6830e-05 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9819 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 177/500 235/235 [==============================] - 3s 14ms/step - loss: 5.0371e-05 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9818 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 178/500 235/235 [==============================] - 3s 14ms/step - loss: 5.2323e-05 - accuracy: 1.0000 - val_loss: 0.1242 - val_accuracy: 0.9815 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 179/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3935e-04 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9821 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 180/500 235/235 [==============================] - 3s 14ms/step - loss: 5.5416e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9817 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 181/500 235/235 [==============================] - 4s 15ms/step - loss: 3.7010e-05 - accuracy: 1.0000 - val_loss: 0.1261 - val_accuracy: 0.9823 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 182/500 235/235 [==============================] - 3s 15ms/step - loss: 2.8320e-05 - accuracy: 1.0000 - val_loss: 0.1265 - val_accuracy: 0.9822 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 183/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2234e-05 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9821 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 184/500 235/235 [==============================] - 3s 14ms/step - loss: 2.5283e-05 - accuracy: 1.0000 - val_loss: 0.1277 - val_accuracy: 0.9819 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 185/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5624e-05 - accuracy: 1.0000 - val_loss: 0.1329 - val_accuracy: 0.9810 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 186/500 235/235 [==============================] - 3s 14ms/step - loss: 5.4727e-05 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9822 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 187/500 235/235 [==============================] - 3s 14ms/step - loss: 8.6398e-05 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9817 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 188/500 235/235 [==============================] - 3s 14ms/step - loss: 9.8241e-05 - accuracy: 1.0000 - val_loss: 0.1342 - val_accuracy: 0.9822 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 189/500 235/235 [==============================] - 3s 14ms/step - loss: 4.7776e-05 - accuracy: 1.0000 - val_loss: 0.1326 - val_accuracy: 0.9823 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 190/500 235/235 [==============================] - 3s 14ms/step - loss: 3.2814e-05 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9815 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 191/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4437e-05 - accuracy: 1.0000 - val_loss: 0.1335 - val_accuracy: 0.9818 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 192/500 235/235 [==============================] - 3s 14ms/step - loss: 9.6971e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9815 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 193/500 235/235 [==============================] - 3s 14ms/step - loss: 4.3672e-05 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9816 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.8448196844477837 Epoch 194/500 235/235 [==============================] - 3s 14ms/step - loss: 3.7415e-05 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9813 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 195/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3698e-05 - accuracy: 1.0000 - val_loss: 0.1365 - val_accuracy: 0.9819 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 196/500 235/235 [==============================] - 3s 15ms/step - loss: 1.3509e-05 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9821 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 197/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3267e-05 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9821 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.8448196844477837 Epoch 198/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5826e-05 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9822 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 199/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0832e-05 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9822 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.8448196844477837 Epoch 200/500 235/235 [==============================] - 3s 15ms/step - loss: 1.1472e-05 - accuracy: 1.0000 - val_loss: 0.1401 - val_accuracy: 0.9823 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.8448196844477837 Epoch 201/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0623 - accuracy: 0.9835 - val_loss: 0.1405 - val_accuracy: 0.9742 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 202/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0190 - accuracy: 0.9942 - val_loss: 0.1293 - val_accuracy: 0.9748 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 203/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0116 - accuracy: 0.9965 - val_loss: 0.1252 - val_accuracy: 0.9754 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 204/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0079 - accuracy: 0.9978 - val_loss: 0.1238 - val_accuracy: 0.9759 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 205/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0059 - accuracy: 0.9987 - val_loss: 0.1245 - val_accuracy: 0.9755 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 206/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0048 - accuracy: 0.9987 - val_loss: 0.1225 - val_accuracy: 0.9761 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 207/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1233 - val_accuracy: 0.9766 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 208/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.1228 - val_accuracy: 0.9766 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 209/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 0.1225 - val_accuracy: 0.9763 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 210/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0024 - accuracy: 0.9997 - val_loss: 0.1246 - val_accuracy: 0.9765 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 211/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0020 - accuracy: 0.9998 - val_loss: 0.1243 - val_accuracy: 0.9775 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 212/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.1249 - val_accuracy: 0.9775 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 213/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.1261 - val_accuracy: 0.9774 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 214/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.1270 - val_accuracy: 0.9776 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 215/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.1270 - val_accuracy: 0.9784 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 216/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.1291 - val_accuracy: 0.9776 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 217/500 235/235 [==============================] - 3s 15ms/step - loss: 9.6531e-04 - accuracy: 0.9999 - val_loss: 0.1308 - val_accuracy: 0.9772 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 218/500 235/235 [==============================] - 3s 14ms/step - loss: 7.9615e-04 - accuracy: 1.0000 - val_loss: 0.1321 - val_accuracy: 0.9776 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 219/500 235/235 [==============================] - 3s 14ms/step - loss: 7.2509e-04 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9775 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 220/500 235/235 [==============================] - 3s 14ms/step - loss: 6.5214e-04 - accuracy: 0.9999 - val_loss: 0.1331 - val_accuracy: 0.9775 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 221/500 235/235 [==============================] - 3s 14ms/step - loss: 5.7805e-04 - accuracy: 1.0000 - val_loss: 0.1344 - val_accuracy: 0.9779 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 222/500 235/235 [==============================] - 3s 14ms/step - loss: 6.0168e-04 - accuracy: 0.9999 - val_loss: 0.1349 - val_accuracy: 0.9775 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 223/500 235/235 [==============================] - 3s 14ms/step - loss: 4.5303e-04 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9784 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 224/500 235/235 [==============================] - 3s 14ms/step - loss: 4.2693e-04 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9777 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 225/500 235/235 [==============================] - 3s 14ms/step - loss: 3.4728e-04 - accuracy: 1.0000 - val_loss: 0.1392 - val_accuracy: 0.9782 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 226/500 235/235 [==============================] - 3s 14ms/step - loss: 3.6580e-04 - accuracy: 1.0000 - val_loss: 0.1399 - val_accuracy: 0.9782 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 227/500 235/235 [==============================] - 3s 14ms/step - loss: 3.9179e-04 - accuracy: 1.0000 - val_loss: 0.1414 - val_accuracy: 0.9783 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 228/500 235/235 [==============================] - 3s 14ms/step - loss: 3.1101e-04 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9775 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 229/500 235/235 [==============================] - 3s 14ms/step - loss: 2.7967e-04 - accuracy: 1.0000 - val_loss: 0.1430 - val_accuracy: 0.9784 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 230/500 235/235 [==============================] - 3s 14ms/step - loss: 2.3698e-04 - accuracy: 1.0000 - val_loss: 0.1448 - val_accuracy: 0.9788 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 231/500 235/235 [==============================] - 3s 14ms/step - loss: 2.4002e-04 - accuracy: 1.0000 - val_loss: 0.1460 - val_accuracy: 0.9786 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 232/500 235/235 [==============================] - 3s 14ms/step - loss: 2.1044e-04 - accuracy: 1.0000 - val_loss: 0.1453 - val_accuracy: 0.9778 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 233/500 235/235 [==============================] - 3s 15ms/step - loss: 2.2428e-04 - accuracy: 1.0000 - val_loss: 0.1492 - val_accuracy: 0.9778 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 234/500 235/235 [==============================] - 3s 14ms/step - loss: 3.0708e-04 - accuracy: 0.9999 - val_loss: 0.1482 - val_accuracy: 0.9783 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 235/500 235/235 [==============================] - 3s 14ms/step - loss: 2.0479e-04 - accuracy: 1.0000 - val_loss: 0.1477 - val_accuracy: 0.9783 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 236/500 235/235 [==============================] - 3s 15ms/step - loss: 1.6461e-04 - accuracy: 1.0000 - val_loss: 0.1507 - val_accuracy: 0.9789 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 237/500 235/235 [==============================] - 3s 14ms/step - loss: 1.3638e-04 - accuracy: 1.0000 - val_loss: 0.1516 - val_accuracy: 0.9784 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 238/500 235/235 [==============================] - 3s 14ms/step - loss: 1.8562e-04 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9777 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 239/500 235/235 [==============================] - 3s 14ms/step - loss: 1.5996e-04 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9782 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 240/500 235/235 [==============================] - 4s 15ms/step - loss: 1.2188e-04 - accuracy: 1.0000 - val_loss: 0.1558 - val_accuracy: 0.9780 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 241/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0143e-04 - accuracy: 1.0000 - val_loss: 0.1561 - val_accuracy: 0.9786 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 242/500 235/235 [==============================] - 4s 15ms/step - loss: 7.5947e-05 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9784 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9059804658151765 Epoch 243/500 235/235 [==============================] - 3s 14ms/step - loss: 8.2570e-05 - accuracy: 1.0000 - val_loss: 0.1581 - val_accuracy: 0.9784 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 244/500 235/235 [==============================] - 3s 14ms/step - loss: 9.0700e-05 - accuracy: 1.0000 - val_loss: 0.1577 - val_accuracy: 0.9783 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 245/500 235/235 [==============================] - 3s 15ms/step - loss: 8.6183e-05 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9786 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 246/500 235/235 [==============================] - 3s 14ms/step - loss: 1.2061e-04 - accuracy: 1.0000 - val_loss: 0.1618 - val_accuracy: 0.9785 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 247/500 235/235 [==============================] - 3s 14ms/step - loss: 6.2780e-05 - accuracy: 1.0000 - val_loss: 0.1612 - val_accuracy: 0.9785 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 248/500 235/235 [==============================] - 3s 14ms/step - loss: 6.3127e-05 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9782 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059804658151765 Epoch 249/500 235/235 [==============================] - 3s 14ms/step - loss: 7.5904e-05 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9783 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9059804658151765 Epoch 250/500 235/235 [==============================] - 3s 15ms/step - loss: 7.3875e-05 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 0.9781 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059804658151765 Epoch 251/500 235/235 [==============================] - 3s 15ms/step - loss: 0.2209 - accuracy: 0.9476 - val_loss: 0.2066 - val_accuracy: 0.9546 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 252/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0985 - accuracy: 0.9707 - val_loss: 0.1778 - val_accuracy: 0.9598 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 253/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0772 - accuracy: 0.9765 - val_loss: 0.1644 - val_accuracy: 0.9615 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 254/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0669 - accuracy: 0.9794 - val_loss: 0.1559 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 255/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0588 - accuracy: 0.9817 - val_loss: 0.1492 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 256/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0520 - accuracy: 0.9836 - val_loss: 0.1445 - val_accuracy: 0.9651 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 257/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0482 - accuracy: 0.9849 - val_loss: 0.1407 - val_accuracy: 0.9664 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 258/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0446 - accuracy: 0.9859 - val_loss: 0.1380 - val_accuracy: 0.9665 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 259/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0407 - accuracy: 0.9871 - val_loss: 0.1360 - val_accuracy: 0.9669 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 260/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0385 - accuracy: 0.9880 - val_loss: 0.1339 - val_accuracy: 0.9676 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 261/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0361 - accuracy: 0.9887 - val_loss: 0.1328 - val_accuracy: 0.9676 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 262/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0349 - accuracy: 0.9890 - val_loss: 0.1326 - val_accuracy: 0.9673 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 263/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0315 - accuracy: 0.9901 - val_loss: 0.1319 - val_accuracy: 0.9676 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 264/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0307 - accuracy: 0.9908 - val_loss: 0.1320 - val_accuracy: 0.9680 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 265/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0290 - accuracy: 0.9910 - val_loss: 0.1327 - val_accuracy: 0.9679 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 266/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0271 - accuracy: 0.9917 - val_loss: 0.1328 - val_accuracy: 0.9685 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 267/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0257 - accuracy: 0.9922 - val_loss: 0.1329 - val_accuracy: 0.9685 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 268/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0249 - accuracy: 0.9924 - val_loss: 0.1331 - val_accuracy: 0.9685 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 269/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0236 - accuracy: 0.9930 - val_loss: 0.1330 - val_accuracy: 0.9683 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 270/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0226 - accuracy: 0.9930 - val_loss: 0.1339 - val_accuracy: 0.9683 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 271/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0219 - accuracy: 0.9936 - val_loss: 0.1334 - val_accuracy: 0.9680 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 272/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0205 - accuracy: 0.9940 - val_loss: 0.1346 - val_accuracy: 0.9688 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 273/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0192 - accuracy: 0.9944 - val_loss: 0.1356 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 274/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0187 - accuracy: 0.9946 - val_loss: 0.1364 - val_accuracy: 0.9691 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 275/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0175 - accuracy: 0.9948 - val_loss: 0.1366 - val_accuracy: 0.9684 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 276/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0169 - accuracy: 0.9954 - val_loss: 0.1372 - val_accuracy: 0.9680 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 277/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0162 - accuracy: 0.9954 - val_loss: 0.1392 - val_accuracy: 0.9685 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 278/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0158 - accuracy: 0.9955 - val_loss: 0.1400 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 279/500 235/235 [==============================] - 4s 15ms/step - loss: 0.0140 - accuracy: 0.9964 - val_loss: 0.1394 - val_accuracy: 0.9690 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 280/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0138 - accuracy: 0.9964 - val_loss: 0.1404 - val_accuracy: 0.9690 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 281/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0135 - accuracy: 0.9963 - val_loss: 0.1426 - val_accuracy: 0.9689 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 282/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0128 - accuracy: 0.9967 - val_loss: 0.1424 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 283/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.1443 - val_accuracy: 0.9688 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 284/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0116 - accuracy: 0.9970 - val_loss: 0.1460 - val_accuracy: 0.9691 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 285/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9974 - val_loss: 0.1456 - val_accuracy: 0.9693 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 286/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0107 - accuracy: 0.9972 - val_loss: 0.1468 - val_accuracy: 0.9691 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 287/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0101 - accuracy: 0.9976 - val_loss: 0.1467 - val_accuracy: 0.9688 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 288/500 235/235 [==============================] - 3s 15ms/step - loss: 0.0102 - accuracy: 0.9972 - val_loss: 0.1497 - val_accuracy: 0.9687 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 289/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0092 - accuracy: 0.9981 - val_loss: 0.1506 - val_accuracy: 0.9685 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 290/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.1520 - val_accuracy: 0.9687 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 291/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0082 - accuracy: 0.9982 - val_loss: 0.1540 - val_accuracy: 0.9687 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 292/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0080 - accuracy: 0.9983 - val_loss: 0.1537 - val_accuracy: 0.9692 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 293/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0080 - accuracy: 0.9984 - val_loss: 0.1549 - val_accuracy: 0.9693 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 294/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.1559 - val_accuracy: 0.9684 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 295/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0071 - accuracy: 0.9985 - val_loss: 0.1587 - val_accuracy: 0.9697 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 296/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0066 - accuracy: 0.9988 - val_loss: 0.1590 - val_accuracy: 0.9697 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 297/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 0.1614 - val_accuracy: 0.9696 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469872276483847 Epoch 298/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0061 - accuracy: 0.9988 - val_loss: 0.1625 - val_accuracy: 0.9691 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 299/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0057 - accuracy: 0.9990 - val_loss: 0.1646 - val_accuracy: 0.9689 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 300/500 235/235 [==============================] - 3s 14ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 0.1653 - val_accuracy: 0.9688 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469872276483847 Epoch 301/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5164 - accuracy: 0.8768 - val_loss: 0.3727 - val_accuracy: 0.9041 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9718557475582269 Epoch 302/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2957 - accuracy: 0.9171 - val_loss: 0.3096 - val_accuracy: 0.9169 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 303/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.9267 - val_loss: 0.2797 - val_accuracy: 0.9248 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 304/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2309 - accuracy: 0.9312 - val_loss: 0.2618 - val_accuracy: 0.9279 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 305/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2155 - accuracy: 0.9353 - val_loss: 0.2493 - val_accuracy: 0.9297 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 306/500 235/235 [==============================] - 3s 14ms/step - loss: 0.2039 - accuracy: 0.9383 - val_loss: 0.2399 - val_accuracy: 0.9329 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 307/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1953 - accuracy: 0.9402 - val_loss: 0.2318 - val_accuracy: 0.9343 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 308/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1889 - accuracy: 0.9421 - val_loss: 0.2259 - val_accuracy: 0.9364 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 309/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1827 - accuracy: 0.9434 - val_loss: 0.2210 - val_accuracy: 0.9377 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 310/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1781 - accuracy: 0.9443 - val_loss: 0.2164 - val_accuracy: 0.9395 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 311/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1741 - accuracy: 0.9462 - val_loss: 0.2121 - val_accuracy: 0.9402 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 312/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1691 - accuracy: 0.9482 - val_loss: 0.2089 - val_accuracy: 0.9410 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 313/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1659 - accuracy: 0.9485 - val_loss: 0.2061 - val_accuracy: 0.9417 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 314/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1628 - accuracy: 0.9495 - val_loss: 0.2036 - val_accuracy: 0.9427 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 315/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1590 - accuracy: 0.9505 - val_loss: 0.2012 - val_accuracy: 0.9438 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 316/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1571 - accuracy: 0.9517 - val_loss: 0.1993 - val_accuracy: 0.9445 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 317/500 235/235 [==============================] - 4s 16ms/step - loss: 0.1548 - accuracy: 0.9521 - val_loss: 0.1978 - val_accuracy: 0.9448 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 318/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1519 - accuracy: 0.9523 - val_loss: 0.1966 - val_accuracy: 0.9450 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 319/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1493 - accuracy: 0.9539 - val_loss: 0.1951 - val_accuracy: 0.9452 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 320/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1479 - accuracy: 0.9540 - val_loss: 0.1934 - val_accuracy: 0.9456 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 321/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1450 - accuracy: 0.9552 - val_loss: 0.1928 - val_accuracy: 0.9461 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 322/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1444 - accuracy: 0.9553 - val_loss: 0.1915 - val_accuracy: 0.9464 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 323/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1423 - accuracy: 0.9560 - val_loss: 0.1903 - val_accuracy: 0.9461 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 324/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1405 - accuracy: 0.9561 - val_loss: 0.1894 - val_accuracy: 0.9467 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 325/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1396 - accuracy: 0.9565 - val_loss: 0.1884 - val_accuracy: 0.9466 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 326/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1381 - accuracy: 0.9568 - val_loss: 0.1874 - val_accuracy: 0.9464 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 327/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1363 - accuracy: 0.9582 - val_loss: 0.1868 - val_accuracy: 0.9458 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 328/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1357 - accuracy: 0.9580 - val_loss: 0.1860 - val_accuracy: 0.9464 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 329/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1360 - accuracy: 0.9584 - val_loss: 0.1852 - val_accuracy: 0.9464 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 330/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1335 - accuracy: 0.9585 - val_loss: 0.1842 - val_accuracy: 0.9462 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 331/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1330 - accuracy: 0.9588 - val_loss: 0.1836 - val_accuracy: 0.9469 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 332/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1313 - accuracy: 0.9595 - val_loss: 0.1834 - val_accuracy: 0.9471 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 333/500 235/235 [==============================] - 3s 15ms/step - loss: 0.1302 - accuracy: 0.9601 - val_loss: 0.1824 - val_accuracy: 0.9472 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 334/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1296 - accuracy: 0.9599 - val_loss: 0.1823 - val_accuracy: 0.9472 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 335/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1287 - accuracy: 0.9599 - val_loss: 0.1817 - val_accuracy: 0.9476 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 336/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1273 - accuracy: 0.9610 - val_loss: 0.1811 - val_accuracy: 0.9475 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 337/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1269 - accuracy: 0.9608 - val_loss: 0.1806 - val_accuracy: 0.9479 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 338/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.1802 - val_accuracy: 0.9484 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 339/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1253 - accuracy: 0.9616 - val_loss: 0.1799 - val_accuracy: 0.9492 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 340/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1249 - accuracy: 0.9618 - val_loss: 0.1793 - val_accuracy: 0.9488 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 341/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1248 - accuracy: 0.9617 - val_loss: 0.1790 - val_accuracy: 0.9496 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 342/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1235 - accuracy: 0.9620 - val_loss: 0.1790 - val_accuracy: 0.9494 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 343/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1236 - accuracy: 0.9616 - val_loss: 0.1787 - val_accuracy: 0.9498 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 344/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1219 - accuracy: 0.9627 - val_loss: 0.1779 - val_accuracy: 0.9502 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 345/500 235/235 [==============================] - 4s 15ms/step - loss: 0.1211 - accuracy: 0.9625 - val_loss: 0.1779 - val_accuracy: 0.9500 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 346/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1200 - accuracy: 0.9633 - val_loss: 0.1776 - val_accuracy: 0.9507 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 347/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - accuracy: 0.9633 - val_loss: 0.1775 - val_accuracy: 0.9507 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 348/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1198 - accuracy: 0.9636 - val_loss: 0.1775 - val_accuracy: 0.9506 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 349/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1186 - accuracy: 0.9638 - val_loss: 0.1768 - val_accuracy: 0.9508 [ 0. 0. 0. ... 0. 0. -0.] Sparsity at: 0.9718557475582269 Epoch 350/500 235/235 [==============================] - 3s 14ms/step - loss: 0.1181 - accuracy: 0.9638 - val_loss: 0.1769 - val_accuracy: 0.9511 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9718557475582269 Epoch 351/500 235/235 [==============================] - 3s 14ms/step - loss: 0.7794 - accuracy: 0.7436 - val_loss: 0.6255 - val_accuracy: 0.7861 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 352/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5830 - accuracy: 0.8131 - val_loss: 0.5424 - val_accuracy: 0.8350 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 353/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5435 - accuracy: 0.8322 - val_loss: 0.5166 - val_accuracy: 0.8435 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 354/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5245 - accuracy: 0.8393 - val_loss: 0.5008 - val_accuracy: 0.8482 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 355/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5131 - accuracy: 0.8425 - val_loss: 0.4896 - val_accuracy: 0.8509 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 356/500 235/235 [==============================] - 3s 14ms/step - loss: 0.5048 - accuracy: 0.8464 - val_loss: 0.4824 - val_accuracy: 0.8532 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 357/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4978 - accuracy: 0.8489 - val_loss: 0.4766 - val_accuracy: 0.8549 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 358/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4937 - accuracy: 0.8499 - val_loss: 0.4727 - val_accuracy: 0.8572 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 359/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4879 - accuracy: 0.8521 - val_loss: 0.4686 - val_accuracy: 0.8586 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 360/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4859 - accuracy: 0.8522 - val_loss: 0.4656 - val_accuracy: 0.8583 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 361/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4818 - accuracy: 0.8539 - val_loss: 0.4632 - val_accuracy: 0.8589 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 362/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4787 - accuracy: 0.8548 - val_loss: 0.4600 - val_accuracy: 0.8603 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 363/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4753 - accuracy: 0.8560 - val_loss: 0.4585 - val_accuracy: 0.8613 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 364/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4734 - accuracy: 0.8569 - val_loss: 0.4570 - val_accuracy: 0.8616 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 365/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4717 - accuracy: 0.8577 - val_loss: 0.4553 - val_accuracy: 0.8615 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 366/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4702 - accuracy: 0.8570 - val_loss: 0.4540 - val_accuracy: 0.8627 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 367/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4673 - accuracy: 0.8576 - val_loss: 0.4526 - val_accuracy: 0.8628 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 368/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4668 - accuracy: 0.8588 - val_loss: 0.4519 - val_accuracy: 0.8625 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 369/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4648 - accuracy: 0.8597 - val_loss: 0.4507 - val_accuracy: 0.8636 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 370/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4629 - accuracy: 0.8597 - val_loss: 0.4497 - val_accuracy: 0.8636 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 371/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4620 - accuracy: 0.8597 - val_loss: 0.4483 - val_accuracy: 0.8645 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 372/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4604 - accuracy: 0.8602 - val_loss: 0.4472 - val_accuracy: 0.8650 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 373/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4592 - accuracy: 0.8608 - val_loss: 0.4464 - val_accuracy: 0.8656 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 374/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4581 - accuracy: 0.8609 - val_loss: 0.4461 - val_accuracy: 0.8654 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 375/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4566 - accuracy: 0.8622 - val_loss: 0.4454 - val_accuracy: 0.8653 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 376/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4557 - accuracy: 0.8625 - val_loss: 0.4449 - val_accuracy: 0.8656 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 377/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4549 - accuracy: 0.8622 - val_loss: 0.4441 - val_accuracy: 0.8655 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 378/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4539 - accuracy: 0.8619 - val_loss: 0.4436 - val_accuracy: 0.8654 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 379/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4525 - accuracy: 0.8628 - val_loss: 0.4427 - val_accuracy: 0.8665 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 380/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4522 - accuracy: 0.8627 - val_loss: 0.4421 - val_accuracy: 0.8667 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 381/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4513 - accuracy: 0.8634 - val_loss: 0.4418 - val_accuracy: 0.8671 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 382/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4505 - accuracy: 0.8636 - val_loss: 0.4417 - val_accuracy: 0.8667 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 383/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4489 - accuracy: 0.8644 - val_loss: 0.4406 - val_accuracy: 0.8667 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 384/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4481 - accuracy: 0.8640 - val_loss: 0.4407 - val_accuracy: 0.8687 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 385/500 235/235 [==============================] - 3s 15ms/step - loss: 0.4475 - accuracy: 0.8642 - val_loss: 0.4403 - val_accuracy: 0.8689 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 386/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4471 - accuracy: 0.8642 - val_loss: 0.4401 - val_accuracy: 0.8680 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 387/500 235/235 [==============================] - 4s 15ms/step - loss: 0.4457 - accuracy: 0.8655 - val_loss: 0.4400 - val_accuracy: 0.8683 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 388/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4454 - accuracy: 0.8643 - val_loss: 0.4395 - val_accuracy: 0.8684 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 389/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4452 - accuracy: 0.8651 - val_loss: 0.4394 - val_accuracy: 0.8681 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 390/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4450 - accuracy: 0.8651 - val_loss: 0.4392 - val_accuracy: 0.8686 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 391/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4443 - accuracy: 0.8651 - val_loss: 0.4390 - val_accuracy: 0.8687 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 392/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4443 - accuracy: 0.8660 - val_loss: 0.4384 - val_accuracy: 0.8693 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 393/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4429 - accuracy: 0.8662 - val_loss: 0.4379 - val_accuracy: 0.8689 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 394/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4428 - accuracy: 0.8661 - val_loss: 0.4385 - val_accuracy: 0.8688 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9846243425995492 Epoch 395/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4427 - accuracy: 0.8665 - val_loss: 0.4379 - val_accuracy: 0.8686 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 396/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4424 - accuracy: 0.8655 - val_loss: 0.4378 - val_accuracy: 0.8685 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 397/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4424 - accuracy: 0.8663 - val_loss: 0.4378 - val_accuracy: 0.8689 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 398/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4414 - accuracy: 0.8668 - val_loss: 0.4372 - val_accuracy: 0.8691 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 399/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4407 - accuracy: 0.8670 - val_loss: 0.4371 - val_accuracy: 0.8692 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 400/500 235/235 [==============================] - 3s 14ms/step - loss: 0.4403 - accuracy: 0.8665 - val_loss: 0.4376 - val_accuracy: 0.8693 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9846243425995492 Epoch 401/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0932 - accuracy: 0.6444 - val_loss: 1.0447 - val_accuracy: 0.6236 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 402/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0273 - accuracy: 0.6478 - val_loss: 1.0126 - val_accuracy: 0.6531 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 403/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0194 - accuracy: 0.6477 - val_loss: 1.0073 - val_accuracy: 0.6527 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 404/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0164 - accuracy: 0.6484 - val_loss: 1.0054 - val_accuracy: 0.6536 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 405/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0145 - accuracy: 0.6482 - val_loss: 1.0046 - val_accuracy: 0.6534 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 406/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0138 - accuracy: 0.6489 - val_loss: 1.0034 - val_accuracy: 0.6536 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 407/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0118 - accuracy: 0.6490 - val_loss: 1.0015 - val_accuracy: 0.6554 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 408/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0117 - accuracy: 0.6494 - val_loss: 1.0008 - val_accuracy: 0.6543 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 409/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0090 - accuracy: 0.6496 - val_loss: 1.0017 - val_accuracy: 0.6549 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 410/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0088 - accuracy: 0.6496 - val_loss: 0.9998 - val_accuracy: 0.6554 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 411/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0067 - accuracy: 0.6505 - val_loss: 0.9982 - val_accuracy: 0.6554 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 412/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0061 - accuracy: 0.6502 - val_loss: 0.9971 - val_accuracy: 0.6555 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 413/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0044 - accuracy: 0.6500 - val_loss: 0.9961 - val_accuracy: 0.6556 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 414/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0050 - accuracy: 0.6503 - val_loss: 0.9949 - val_accuracy: 0.6553 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 415/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0041 - accuracy: 0.6499 - val_loss: 0.9947 - val_accuracy: 0.6555 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 416/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0025 - accuracy: 0.6512 - val_loss: 0.9940 - val_accuracy: 0.6565 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 417/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0017 - accuracy: 0.6505 - val_loss: 0.9932 - val_accuracy: 0.6555 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 418/500 235/235 [==============================] - 3s 15ms/step - loss: 1.0019 - accuracy: 0.6514 - val_loss: 0.9926 - val_accuracy: 0.6561 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 419/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0014 - accuracy: 0.6514 - val_loss: 0.9920 - val_accuracy: 0.6572 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 420/500 235/235 [==============================] - 3s 14ms/step - loss: 1.0012 - accuracy: 0.6518 - val_loss: 0.9917 - val_accuracy: 0.6562 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 421/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9995 - accuracy: 0.6519 - val_loss: 0.9914 - val_accuracy: 0.6571 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 422/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9995 - accuracy: 0.6511 - val_loss: 0.9909 - val_accuracy: 0.6564 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 423/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9995 - accuracy: 0.6519 - val_loss: 0.9903 - val_accuracy: 0.6565 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 424/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9986 - accuracy: 0.6522 - val_loss: 0.9899 - val_accuracy: 0.6560 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 425/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9981 - accuracy: 0.6519 - val_loss: 0.9895 - val_accuracy: 0.6554 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 426/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9983 - accuracy: 0.6521 - val_loss: 0.9888 - val_accuracy: 0.6558 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 427/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9967 - accuracy: 0.6524 - val_loss: 0.9885 - val_accuracy: 0.6552 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 428/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9963 - accuracy: 0.6526 - val_loss: 0.9882 - val_accuracy: 0.6553 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 429/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9954 - accuracy: 0.6527 - val_loss: 0.9883 - val_accuracy: 0.6549 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 430/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9952 - accuracy: 0.6527 - val_loss: 0.9876 - val_accuracy: 0.6556 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 431/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9956 - accuracy: 0.6530 - val_loss: 0.9877 - val_accuracy: 0.6556 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 432/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9950 - accuracy: 0.6536 - val_loss: 0.9884 - val_accuracy: 0.6556 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 433/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9944 - accuracy: 0.6533 - val_loss: 0.9879 - val_accuracy: 0.6559 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 434/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9941 - accuracy: 0.6526 - val_loss: 0.9871 - val_accuracy: 0.6559 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 435/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9942 - accuracy: 0.6538 - val_loss: 0.9873 - val_accuracy: 0.6562 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 436/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9948 - accuracy: 0.6536 - val_loss: 0.9876 - val_accuracy: 0.6563 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 437/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9948 - accuracy: 0.6532 - val_loss: 0.9874 - val_accuracy: 0.6561 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 438/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9929 - accuracy: 0.6541 - val_loss: 0.9877 - val_accuracy: 0.6559 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 439/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9930 - accuracy: 0.6535 - val_loss: 0.9878 - val_accuracy: 0.6568 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 440/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9937 - accuracy: 0.6540 - val_loss: 0.9874 - val_accuracy: 0.6565 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 441/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9932 - accuracy: 0.6542 - val_loss: 0.9862 - val_accuracy: 0.6568 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 442/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9922 - accuracy: 0.6547 - val_loss: 0.9864 - val_accuracy: 0.6567 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 443/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9924 - accuracy: 0.6546 - val_loss: 0.9859 - val_accuracy: 0.6573 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 444/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9917 - accuracy: 0.6545 - val_loss: 0.9864 - val_accuracy: 0.6562 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 445/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9920 - accuracy: 0.6540 - val_loss: 0.9855 - val_accuracy: 0.6580 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 446/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9915 - accuracy: 0.6543 - val_loss: 0.9862 - val_accuracy: 0.6563 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 447/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9925 - accuracy: 0.6540 - val_loss: 0.9848 - val_accuracy: 0.6576 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 448/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9910 - accuracy: 0.6544 - val_loss: 0.9850 - val_accuracy: 0.6576 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 449/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9905 - accuracy: 0.6544 - val_loss: 0.9843 - val_accuracy: 0.6580 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 450/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9905 - accuracy: 0.6536 - val_loss: 0.9860 - val_accuracy: 0.6569 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 451/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9910 - accuracy: 0.6539 - val_loss: 0.9847 - val_accuracy: 0.6576 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 452/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9902 - accuracy: 0.6524 - val_loss: 0.9837 - val_accuracy: 0.6577 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 453/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9898 - accuracy: 0.6538 - val_loss: 0.9832 - val_accuracy: 0.6573 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 454/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9896 - accuracy: 0.6546 - val_loss: 0.9822 - val_accuracy: 0.6587 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 455/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9898 - accuracy: 0.6537 - val_loss: 0.9828 - val_accuracy: 0.6586 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 456/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9898 - accuracy: 0.6522 - val_loss: 0.9822 - val_accuracy: 0.6590 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 457/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9891 - accuracy: 0.6527 - val_loss: 0.9819 - val_accuracy: 0.6590 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 458/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9892 - accuracy: 0.6525 - val_loss: 0.9820 - val_accuracy: 0.6581 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 459/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9888 - accuracy: 0.6526 - val_loss: 0.9809 - val_accuracy: 0.6582 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 460/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9877 - accuracy: 0.6526 - val_loss: 0.9811 - val_accuracy: 0.6587 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 461/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9889 - accuracy: 0.6525 - val_loss: 0.9812 - val_accuracy: 0.6587 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 462/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9885 - accuracy: 0.6514 - val_loss: 0.9810 - val_accuracy: 0.6577 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 463/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9894 - accuracy: 0.6524 - val_loss: 0.9810 - val_accuracy: 0.6584 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 464/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9884 - accuracy: 0.6526 - val_loss: 0.9811 - val_accuracy: 0.6583 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 465/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9886 - accuracy: 0.6519 - val_loss: 0.9801 - val_accuracy: 0.6598 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 466/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9874 - accuracy: 0.6523 - val_loss: 0.9798 - val_accuracy: 0.6594 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 467/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9874 - accuracy: 0.6528 - val_loss: 0.9803 - val_accuracy: 0.6594 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 468/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9874 - accuracy: 0.6529 - val_loss: 0.9794 - val_accuracy: 0.6596 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 469/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9876 - accuracy: 0.6523 - val_loss: 0.9806 - val_accuracy: 0.6585 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 470/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9873 - accuracy: 0.6521 - val_loss: 0.9800 - val_accuracy: 0.6589 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 471/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9864 - accuracy: 0.6527 - val_loss: 0.9798 - val_accuracy: 0.6587 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 472/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9872 - accuracy: 0.6512 - val_loss: 0.9795 - val_accuracy: 0.6595 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 473/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9865 - accuracy: 0.6521 - val_loss: 0.9801 - val_accuracy: 0.6590 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 474/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6524 - val_loss: 0.9794 - val_accuracy: 0.6584 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 475/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9867 - accuracy: 0.6520 - val_loss: 0.9799 - val_accuracy: 0.6591 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 476/500 235/235 [==============================] - 4s 16ms/step - loss: 0.9864 - accuracy: 0.6531 - val_loss: 0.9799 - val_accuracy: 0.6583 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 477/500 235/235 [==============================] - 4s 15ms/step - loss: 0.9870 - accuracy: 0.6525 - val_loss: 0.9803 - val_accuracy: 0.6583 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 478/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9868 - accuracy: 0.6523 - val_loss: 0.9792 - val_accuracy: 0.6590 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 479/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9867 - accuracy: 0.6531 - val_loss: 0.9796 - val_accuracy: 0.6592 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 480/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9864 - accuracy: 0.6531 - val_loss: 0.9793 - val_accuracy: 0.6585 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 481/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9867 - accuracy: 0.6519 - val_loss: 0.9793 - val_accuracy: 0.6585 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 482/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9866 - accuracy: 0.6525 - val_loss: 0.9800 - val_accuracy: 0.6594 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 483/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9864 - accuracy: 0.6524 - val_loss: 0.9799 - val_accuracy: 0.6591 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 484/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9869 - accuracy: 0.6514 - val_loss: 0.9790 - val_accuracy: 0.6590 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 485/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9863 - accuracy: 0.6525 - val_loss: 0.9793 - val_accuracy: 0.6586 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 486/500 235/235 [==============================] - 4s 16ms/step - loss: 0.9862 - accuracy: 0.6526 - val_loss: 0.9788 - val_accuracy: 0.6594 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 487/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9861 - accuracy: 0.6527 - val_loss: 0.9792 - val_accuracy: 0.6589 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 488/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9856 - accuracy: 0.6529 - val_loss: 0.9792 - val_accuracy: 0.6589 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 489/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9861 - accuracy: 0.6528 - val_loss: 0.9791 - val_accuracy: 0.6595 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 490/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6526 - val_loss: 0.9788 - val_accuracy: 0.6593 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 491/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9870 - accuracy: 0.6523 - val_loss: 0.9800 - val_accuracy: 0.6593 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 492/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6537 - val_loss: 0.9789 - val_accuracy: 0.6593 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 493/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9862 - accuracy: 0.6521 - val_loss: 0.9778 - val_accuracy: 0.6596 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 494/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9856 - accuracy: 0.6532 - val_loss: 0.9791 - val_accuracy: 0.6589 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 495/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9854 - accuracy: 0.6528 - val_loss: 0.9786 - val_accuracy: 0.6595 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 496/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9865 - accuracy: 0.6523 - val_loss: 0.9795 - val_accuracy: 0.6596 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893275732531931 Epoch 497/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9861 - accuracy: 0.6519 - val_loss: 0.9785 - val_accuracy: 0.6595 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 498/500 235/235 [==============================] - 3s 15ms/step - loss: 0.9874 - accuracy: 0.6520 - val_loss: 0.9788 - val_accuracy: 0.6585 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 499/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9858 - accuracy: 0.6528 - val_loss: 0.9784 - val_accuracy: 0.6598 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 500/500 235/235 [==============================] - 3s 14ms/step - loss: 0.9852 - accuracy: 0.6523 - val_loss: 0.9792 - val_accuracy: 0.6592 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893275732531931 Epoch 1/500 235/235 [==============================] - 3s 9ms/step - loss: 0.8513 - accuracy: 0.9007 - val_loss: 0.8256 - val_accuracy: 0.9034 [0. 0. 0. ... 0.15788244 0. 0.14081757] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8415 - accuracy: 0.9017 - val_loss: 0.8244 - val_accuracy: 0.9042 [0. 0. 0. ... 0.16220704 0. 0.13526146] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8404 - accuracy: 0.9020 - val_loss: 0.8236 - val_accuracy: 0.9044 [ 0. 0. 0. ... 0.16280665 -0. 0.13400939] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8399 - accuracy: 0.9018 - val_loss: 0.8226 - val_accuracy: 0.9049 [0. 0. 0. ... 0.16244288 0. 0.13373683] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8394 - accuracy: 0.9019 - val_loss: 0.8230 - val_accuracy: 0.9046 [0. 0. 0. ... 0.16208158 0. 0.13432963] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8392 - accuracy: 0.9017 - val_loss: 0.8229 - val_accuracy: 0.9049 [ 0. 0. 0. ... 0.16135864 -0. 0.13471374] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8391 - accuracy: 0.9018 - val_loss: 0.8230 - val_accuracy: 0.9047 [0. 0. 0. ... 0.16059455 0. 0.13496135] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9020 - val_loss: 0.8226 - val_accuracy: 0.9046 [ 0. 0. 0. ... 0.16012819 -0. 0.13517989] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9016 - val_loss: 0.8224 - val_accuracy: 0.9048 [0. 0. 0. ... 0.16003999 0. 0.13517018] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8390 - accuracy: 0.9020 - val_loss: 0.8230 - val_accuracy: 0.9046 [0. 0. 0. ... 0.15942231 0. 0.13536632] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8389 - accuracy: 0.9019 - val_loss: 0.8232 - val_accuracy: 0.9047 [0. 0. 0. ... 0.1592013 0. 0.13542442] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9021 - val_loss: 0.8228 - val_accuracy: 0.9047 [0. 0. 0. ... 0.15921855 0. 0.13534898] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9020 - val_loss: 0.8228 - val_accuracy: 0.9051 [ 0. 0. 0. ... 0.15913102 -0. 0.1354428 ] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8386 - accuracy: 0.9020 - val_loss: 0.8222 - val_accuracy: 0.9046 [ 0. 0. 0. ... 0.15884258 -0. 0.13563946] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8387 - accuracy: 0.9023 - val_loss: 0.8223 - val_accuracy: 0.9047 [0. 0. 0. ... 0.15885976 0. 0.13555853] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9019 - val_loss: 0.8225 - val_accuracy: 0.9046 [0. 0. 0. ... 0.15848298 0. 0.13537033] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9021 - val_loss: 0.8222 - val_accuracy: 0.9047 [0. 0. 0. ... 0.15849675 0. 0.13540642] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9018 - val_loss: 0.8222 - val_accuracy: 0.9047 [0. 0. 0. ... 0.15827875 0. 0.13552135] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9049 [0. 0. 0. ... 0.15845625 0. 0.13551594] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9020 - val_loss: 0.8220 - val_accuracy: 0.9049 [0. 0. 0. ... 0.1583716 0. 0.13615865] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9020 - val_loss: 0.8218 - val_accuracy: 0.9043 [0. 0. 0. ... 0.15836339 0. 0.13612577] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9020 - val_loss: 0.8224 - val_accuracy: 0.9046 [0. 0. 0. ... 0.15808155 0. 0.13636185] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8382 - accuracy: 0.9021 - val_loss: 0.8226 - val_accuracy: 0.9052 [0. 0. 0. ... 0.15804927 0. 0.13609779] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9020 - val_loss: 0.8215 - val_accuracy: 0.9046 [ 0. 0. 0. ... 0.158039 -0. 0.13599962] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8385 - accuracy: 0.9021 - val_loss: 0.8222 - val_accuracy: 0.9052 [0. 0. 0. ... 0.15787165 0. 0.1357117 ] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9021 - val_loss: 0.8224 - val_accuracy: 0.9047 [ 0. 0. 0. ... 0.15788928 -0. 0.13574402] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9018 - val_loss: 0.8212 - val_accuracy: 0.9050 [0. 0. 0. ... 0.15784638 0. 0.1357661 ] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9023 - val_loss: 0.8218 - val_accuracy: 0.9049 [0. 0. 0. ... 0.15775436 0. 0.13552889] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9019 - val_loss: 0.8217 - val_accuracy: 0.9049 [0. 0. 0. ... 0.15762708 0. 0.13496895] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9047 [0. 0. 0. ... 0.15731747 0. 0.13471764] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9020 - val_loss: 0.8222 - val_accuracy: 0.9047 [ 0. 0. 0. ... 0.15731616 -0. 0.13463095] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9022 - val_loss: 0.8223 - val_accuracy: 0.9048 [0. 0. 0. ... 0.15722875 0. 0.13452779] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9021 - val_loss: 0.8224 - val_accuracy: 0.9048 [0. 0. 0. ... 0.15719621 0. 0.13406663] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9018 - val_loss: 0.8220 - val_accuracy: 0.9041 [0. 0. 0. ... 0.15694052 0. 0.1337055 ] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9017 - val_loss: 0.8225 - val_accuracy: 0.9049 [0. 0. 0. ... 0.15687607 0. 0.1334594 ] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8384 - accuracy: 0.9021 - val_loss: 0.8224 - val_accuracy: 0.9048 [0. 0. 0. ... 0.15664308 0. 0.13329028] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9021 - val_loss: 0.8225 - val_accuracy: 0.9045 [ 0. 0. 0. ... 0.1565845 -0. 0.1331554] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8379 - accuracy: 0.9021 - val_loss: 0.8222 - val_accuracy: 0.9043 [0. 0. 0. ... 0.15663806 0. 0.13260451] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9022 - val_loss: 0.8230 - val_accuracy: 0.9050 [ 0. 0. 0. ... 0.15649739 -0. 0.13243528] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8382 - accuracy: 0.9018 - val_loss: 0.8223 - val_accuracy: 0.9043 [ 0. 0. 0. ... 0.15614049 -0. 0.13218908] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8379 - accuracy: 0.9019 - val_loss: 0.8224 - val_accuracy: 0.9043 [0. 0. 0. ... 0.15599233 0. 0.1317357 ] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8378 - accuracy: 0.9019 - val_loss: 0.8221 - val_accuracy: 0.9047 [0. 0. 0. ... 0.15586543 0. 0.13152066] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8383 - accuracy: 0.9015 - val_loss: 0.8222 - val_accuracy: 0.9041 [0. 0. 0. ... 0.1557162 0. 0.13125826] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8380 - accuracy: 0.9020 - val_loss: 0.8224 - val_accuracy: 0.9048 [0. 0. 0. ... 0.15577984 0. 0.1311198 ] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8380 - accuracy: 0.9020 - val_loss: 0.8225 - val_accuracy: 0.9049 [0. 0. 0. ... 0.15565813 0. 0.13085525] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8382 - accuracy: 0.9020 - val_loss: 0.8223 - val_accuracy: 0.9043 [0. 0. 0. ... 0.15530625 0. 0.13077942] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8381 - accuracy: 0.9022 - val_loss: 0.8209 - val_accuracy: 0.9045 [0. 0. 0. ... 0.15526164 0. 0.1305427 ] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8378 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9045 [0. 0. 0. ... 0.1548658 0. 0.13041286] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8379 - accuracy: 0.9020 - val_loss: 0.8222 - val_accuracy: 0.9045 [ 0. 0. 0. ... 0.15488583 -0. 0.13032916] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8378 - accuracy: 0.9017 - val_loss: 0.8228 - val_accuracy: 0.9040 [ 0. 0. 0. ... 0.15472884 -0. 0.1303137 ] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8639 - accuracy: 0.9017 - val_loss: 0.8408 - val_accuracy: 0.9073 [0. 0. 0. ... 0.13868643 0. 0.13391747] Sparsity at: 0.6457718615879828 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8584 - accuracy: 0.9028 - val_loss: 0.8397 - val_accuracy: 0.9070 [0. 0. 0. ... 0.12898542 0. 0.13097095] Sparsity at: 0.6457718615879828 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8578 - accuracy: 0.9029 - val_loss: 0.8392 - val_accuracy: 0.9074 [0. 0. 0. ... 0.12436873 0. 0.12697442] Sparsity at: 0.6457718615879828 Epoch 54/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8576 - accuracy: 0.9029 - val_loss: 0.8390 - val_accuracy: 0.9077 [0. 0. 0. ... 0.12196597 0. 0.12345385] Sparsity at: 0.6457718615879828 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9030 - val_loss: 0.8388 - val_accuracy: 0.9079 [ 0. 0. 0. ... 0.12046788 -0. 0.12060314] Sparsity at: 0.6457718615879828 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8574 - accuracy: 0.9032 - val_loss: 0.8385 - val_accuracy: 0.9080 [0. 0. 0. ... 0.11946367 0. 0.11828665] Sparsity at: 0.6457718615879828 Epoch 57/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8571 - accuracy: 0.9033 - val_loss: 0.8387 - val_accuracy: 0.9082 [ 0. 0. 0. ... 0.11877161 -0. 0.11631727] Sparsity at: 0.6457718615879828 Epoch 58/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8572 - accuracy: 0.9031 - val_loss: 0.8386 - val_accuracy: 0.9083 [0. 0. 0. ... 0.11838406 0. 0.11472747] Sparsity at: 0.6457718615879828 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8571 - accuracy: 0.9036 - val_loss: 0.8387 - val_accuracy: 0.9082 [0. 0. 0. ... 0.11800242 0. 0.11338073] Sparsity at: 0.6457718615879828 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8570 - accuracy: 0.9034 - val_loss: 0.8385 - val_accuracy: 0.9082 [0. 0. 0. ... 0.11756724 0. 0.11241508] Sparsity at: 0.6457718615879828 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9031 - val_loss: 0.8385 - val_accuracy: 0.9083 [0. 0. 0. ... 0.117554 0. 0.11156002] Sparsity at: 0.6457718615879828 Epoch 62/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8570 - accuracy: 0.9031 - val_loss: 0.8386 - val_accuracy: 0.9082 [0. 0. 0. ... 0.1173941 0. 0.11054305] Sparsity at: 0.6457718615879828 Epoch 63/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8570 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9083 [ 0. 0. 0. ... 0.11711521 -0. 0.11003958] Sparsity at: 0.6457718615879828 Epoch 64/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9084 [0. 0. 0. ... 0.11699417 0. 0.1093235 ] Sparsity at: 0.6457718615879828 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8381 - val_accuracy: 0.9078 [0. 0. 0. ... 0.11691038 0. 0.10889148] Sparsity at: 0.6457718615879828 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9029 - val_loss: 0.8381 - val_accuracy: 0.9078 [0. 0. 0. ... 0.11658615 0. 0.10839706] Sparsity at: 0.6457718615879828 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9033 - val_loss: 0.8385 - val_accuracy: 0.9082 [0. 0. 0. ... 0.11661546 0. 0.1080367 ] Sparsity at: 0.6457718615879828 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9031 - val_loss: 0.8384 - val_accuracy: 0.9087 [0. 0. 0. ... 0.1165145 0. 0.10772243] Sparsity at: 0.6457718615879828 Epoch 69/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9034 - val_loss: 0.8382 - val_accuracy: 0.9079 [0. 0. 0. ... 0.11651203 0. 0.10758363] Sparsity at: 0.6457718615879828 Epoch 70/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9081 [0. 0. 0. ... 0.11649998 0. 0.10697347] Sparsity at: 0.6457718615879828 Epoch 71/500 235/235 [==============================] - 2s 8ms/step - loss: 0.8568 - accuracy: 0.9033 - val_loss: 0.8384 - val_accuracy: 0.9081 [0. 0. 0. ... 0.11632838 0. 0.10688186] Sparsity at: 0.6457718615879828 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9032 - val_loss: 0.8382 - val_accuracy: 0.9083 [0. 0. 0. ... 0.11620827 0. 0.10663418] Sparsity at: 0.6457718615879828 Epoch 73/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9032 - val_loss: 0.8384 - val_accuracy: 0.9085 [0. 0. 0. ... 0.11600775 0. 0.10657653] Sparsity at: 0.6457718615879828 Epoch 74/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9035 - val_loss: 0.8383 - val_accuracy: 0.9079 [0. 0. 0. ... 0.11608796 0. 0.10634568] Sparsity at: 0.6457718615879828 Epoch 75/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8386 - val_accuracy: 0.9092 [0. 0. 0. ... 0.11618339 0. 0.1061451 ] Sparsity at: 0.6457718615879828 Epoch 76/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8384 - val_accuracy: 0.9086 [0. 0. 0. ... 0.11608247 0. 0.10617648] Sparsity at: 0.6457718615879828 Epoch 77/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9032 - val_loss: 0.8381 - val_accuracy: 0.9083 [0. 0. 0. ... 0.11605187 0. 0.10620727] Sparsity at: 0.6457718615879828 Epoch 78/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8569 - accuracy: 0.9031 - val_loss: 0.8385 - val_accuracy: 0.9090 [ 0. 0. 0. ... 0.1157997 -0. 0.1060439] Sparsity at: 0.6457718615879828 Epoch 79/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9029 - val_loss: 0.8385 - val_accuracy: 0.9084 [0. 0. 0. ... 0.11595087 0. 0.10599548] Sparsity at: 0.6457718615879828 Epoch 80/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9030 - val_loss: 0.8382 - val_accuracy: 0.9085 [0. 0. 0. ... 0.11594436 0. 0.10598035] Sparsity at: 0.6457718615879828 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8385 - val_accuracy: 0.9087 [ 0. 0. 0. ... 0.1158568 -0. 0.10581444] Sparsity at: 0.6457718615879828 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9029 - val_loss: 0.8383 - val_accuracy: 0.9085 [ 0. 0. 0. ... 0.11588375 -0. 0.10575263] Sparsity at: 0.6457718615879828 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8384 - val_accuracy: 0.9086 [0. 0. 0. ... 0.11580482 0. 0.10548916] Sparsity at: 0.6457718615879828 Epoch 84/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8381 - val_accuracy: 0.9086 [0. 0. 0. ... 0.11591887 0. 0.10546894] Sparsity at: 0.6457718615879828 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9030 - val_loss: 0.8384 - val_accuracy: 0.9080 [0. 0. 0. ... 0.11551363 0. 0.10553424] Sparsity at: 0.6457718615879828 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8379 - val_accuracy: 0.9085 [0. 0. 0. ... 0.11585085 0. 0.10542686] Sparsity at: 0.6457718615879828 Epoch 87/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8565 - accuracy: 0.9032 - val_loss: 0.8379 - val_accuracy: 0.9080 [ 0. 0. 0. ... 0.11561348 -0. 0.10540969] Sparsity at: 0.6457718615879828 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8385 - val_accuracy: 0.9084 [0. 0. 0. ... 0.11544598 0. 0.10519272] Sparsity at: 0.6457718615879828 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9033 - val_loss: 0.8385 - val_accuracy: 0.9084 [0. 0. 0. ... 0.11551893 0. 0.10559369] Sparsity at: 0.6457718615879828 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8386 - val_accuracy: 0.9091 [0. 0. 0. ... 0.11554836 0. 0.10530648] Sparsity at: 0.6457718615879828 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9032 - val_loss: 0.8381 - val_accuracy: 0.9083 [0. 0. 0. ... 0.11572839 0. 0.10527035] Sparsity at: 0.6457718615879828 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9028 - val_loss: 0.8380 - val_accuracy: 0.9081 [0. 0. 0. ... 0.11558791 0. 0.10552419] Sparsity at: 0.6457718615879828 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8565 - accuracy: 0.9033 - val_loss: 0.8381 - val_accuracy: 0.9083 [ 0. 0. 0. ... 0.11559746 -0. 0.10545988] Sparsity at: 0.6457718615879828 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9028 - val_loss: 0.8384 - val_accuracy: 0.9086 [ 0. 0. 0. ... 0.1156354 -0. 0.10536512] Sparsity at: 0.6457718615879828 Epoch 95/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8382 - val_accuracy: 0.9088 [0. 0. 0. ... 0.11572543 0. 0.10520094] Sparsity at: 0.6457718615879828 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9032 - val_loss: 0.8386 - val_accuracy: 0.9083 [0. 0. 0. ... 0.1154372 0. 0.1053072] Sparsity at: 0.6457718615879828 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9030 - val_loss: 0.8384 - val_accuracy: 0.9088 [ 0. 0. 0. ... 0.1155977 -0. 0.10523844] Sparsity at: 0.6457718615879828 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9031 - val_loss: 0.8382 - val_accuracy: 0.9084 [ 0. 0. 0. ... 0.11549519 -0. 0.10525094] Sparsity at: 0.6457718615879828 Epoch 99/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8568 - accuracy: 0.9031 - val_loss: 0.8384 - val_accuracy: 0.9086 [0. 0. 0. ... 0.11548864 0. 0.10507528] Sparsity at: 0.6457718615879828 Epoch 100/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8567 - accuracy: 0.9028 - val_loss: 0.8383 - val_accuracy: 0.9086 [0. 0. 0. ... 0.11525078 0. 0.1050217 ] Sparsity at: 0.6457718615879828 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9045 - accuracy: 0.9006 - val_loss: 0.8844 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8967 - accuracy: 0.9023 - val_loss: 0.8828 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 103/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8959 - accuracy: 0.9026 - val_loss: 0.8825 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8956 - accuracy: 0.9025 - val_loss: 0.8823 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8953 - accuracy: 0.9028 - val_loss: 0.8817 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8951 - accuracy: 0.9032 - val_loss: 0.8818 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8950 - accuracy: 0.9026 - val_loss: 0.8816 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8949 - accuracy: 0.9028 - val_loss: 0.8818 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 109/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8948 - accuracy: 0.9026 - val_loss: 0.8817 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8948 - accuracy: 0.9028 - val_loss: 0.8815 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8947 - accuracy: 0.9028 - val_loss: 0.8816 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8946 - accuracy: 0.9028 - val_loss: 0.8815 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8947 - accuracy: 0.9025 - val_loss: 0.8814 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 114/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9026 - val_loss: 0.8813 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 115/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9025 - val_loss: 0.8818 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8812 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 117/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9029 - val_loss: 0.8812 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 118/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9030 - val_loss: 0.8810 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 121/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 122/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8813 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 123/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8945 - accuracy: 0.9028 - val_loss: 0.8813 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9041 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 125/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 126/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9031 - val_loss: 0.8809 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 127/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9032 - val_loss: 0.8814 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 129/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9028 - val_loss: 0.8810 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8811 - val_accuracy: 0.9046 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9027 - val_loss: 0.8811 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 132/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9027 - val_loss: 0.8810 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 133/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8813 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 134/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 135/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8813 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 136/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8811 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 138/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9048 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 139/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9030 - val_loss: 0.8810 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 140/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8809 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 141/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9030 - val_loss: 0.8810 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9028 - val_loss: 0.8809 - val_accuracy: 0.9043 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 143/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9029 - val_loss: 0.8809 - val_accuracy: 0.9045 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 144/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8812 - val_accuracy: 0.9042 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 146/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8815 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8944 - accuracy: 0.9029 - val_loss: 0.8810 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 148/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9031 - val_loss: 0.8808 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8942 - accuracy: 0.9029 - val_loss: 0.8810 - val_accuracy: 0.9047 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 150/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8943 - accuracy: 0.9028 - val_loss: 0.8811 - val_accuracy: 0.9044 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.7594051770386266 Epoch 151/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9944 - accuracy: 0.8967 - val_loss: 0.9566 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 152/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9693 - accuracy: 0.9014 - val_loss: 0.9535 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9670 - accuracy: 0.9020 - val_loss: 0.9521 - val_accuracy: 0.9038 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 154/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9658 - accuracy: 0.9018 - val_loss: 0.9513 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9650 - accuracy: 0.9017 - val_loss: 0.9509 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 156/500 235/235 [==============================] - 2s 10ms/step - loss: 0.9644 - accuracy: 0.9017 - val_loss: 0.9504 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9640 - accuracy: 0.9014 - val_loss: 0.9500 - val_accuracy: 0.9040 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 158/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9635 - accuracy: 0.9016 - val_loss: 0.9496 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 159/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9631 - accuracy: 0.9018 - val_loss: 0.9495 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9629 - accuracy: 0.9018 - val_loss: 0.9492 - val_accuracy: 0.9039 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9628 - accuracy: 0.9017 - val_loss: 0.9491 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9626 - accuracy: 0.9016 - val_loss: 0.9492 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9624 - accuracy: 0.9016 - val_loss: 0.9490 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9623 - accuracy: 0.9015 - val_loss: 0.9487 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 165/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9621 - accuracy: 0.9016 - val_loss: 0.9488 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9620 - accuracy: 0.9017 - val_loss: 0.9490 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9620 - accuracy: 0.9015 - val_loss: 0.9485 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9619 - accuracy: 0.9017 - val_loss: 0.9484 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9618 - accuracy: 0.9014 - val_loss: 0.9483 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9617 - accuracy: 0.9015 - val_loss: 0.9483 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9617 - accuracy: 0.9015 - val_loss: 0.9481 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 172/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9616 - accuracy: 0.9014 - val_loss: 0.9485 - val_accuracy: 0.9035 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9016 - val_loss: 0.9484 - val_accuracy: 0.9030 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9015 - val_loss: 0.9483 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9017 - val_loss: 0.9483 - val_accuracy: 0.9036 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 176/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9614 - accuracy: 0.9014 - val_loss: 0.9479 - val_accuracy: 0.9034 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9614 - accuracy: 0.9012 - val_loss: 0.9483 - val_accuracy: 0.9030 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9615 - accuracy: 0.9014 - val_loss: 0.9480 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9483 - val_accuracy: 0.9037 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 180/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9614 - accuracy: 0.9015 - val_loss: 0.9481 - val_accuracy: 0.9030 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 181/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9480 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9017 - val_loss: 0.9478 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 183/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9015 - val_loss: 0.9482 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9479 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9016 - val_loss: 0.9482 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 186/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9479 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 187/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9481 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9015 - val_loss: 0.9481 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9014 - val_loss: 0.9479 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 190/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9016 - val_loss: 0.9480 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9613 - accuracy: 0.9015 - val_loss: 0.9476 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 192/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9479 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9017 - val_loss: 0.9479 - val_accuracy: 0.9028 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 194/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9014 - val_loss: 0.9479 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 195/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9481 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9611 - accuracy: 0.9014 - val_loss: 0.9481 - val_accuracy: 0.9031 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9015 - val_loss: 0.9478 - val_accuracy: 0.9032 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9014 - val_loss: 0.9477 - val_accuracy: 0.9029 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9016 - val_loss: 0.9480 - val_accuracy: 0.9033 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 0.9612 - accuracy: 0.9013 - val_loss: 0.9480 - val_accuracy: 0.9026 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.8448061963519313 Epoch 201/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0908 - accuracy: 0.8862 - val_loss: 1.0428 - val_accuracy: 0.8967 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 202/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0568 - accuracy: 0.8956 - val_loss: 1.0376 - val_accuracy: 0.8990 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0535 - accuracy: 0.8967 - val_loss: 1.0361 - val_accuracy: 0.8994 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 204/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0523 - accuracy: 0.8968 - val_loss: 1.0353 - val_accuracy: 0.9002 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0516 - accuracy: 0.8970 - val_loss: 1.0348 - val_accuracy: 0.9003 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0512 - accuracy: 0.8972 - val_loss: 1.0344 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0509 - accuracy: 0.8972 - val_loss: 1.0343 - val_accuracy: 0.9005 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0506 - accuracy: 0.8972 - val_loss: 1.0340 - val_accuracy: 0.9006 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 209/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0504 - accuracy: 0.8974 - val_loss: 1.0339 - val_accuracy: 0.9005 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0502 - accuracy: 0.8976 - val_loss: 1.0337 - val_accuracy: 0.9006 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0501 - accuracy: 0.8976 - val_loss: 1.0336 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 212/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0500 - accuracy: 0.8978 - val_loss: 1.0335 - val_accuracy: 0.9005 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 213/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0500 - accuracy: 0.8977 - val_loss: 1.0334 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 214/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0499 - accuracy: 0.8979 - val_loss: 1.0333 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 215/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0498 - accuracy: 0.8979 - val_loss: 1.0332 - val_accuracy: 0.9011 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 216/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8978 - val_loss: 1.0331 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 217/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8980 - val_loss: 1.0330 - val_accuracy: 0.9011 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 218/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0497 - accuracy: 0.8977 - val_loss: 1.0331 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 219/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8977 - val_loss: 1.0330 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 220/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8979 - val_loss: 1.0331 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 221/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8979 - val_loss: 1.0330 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 222/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8980 - val_loss: 1.0329 - val_accuracy: 0.9011 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 223/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8979 - val_loss: 1.0330 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 224/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8980 - val_loss: 1.0329 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 225/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 226/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 227/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0496 - accuracy: 0.8978 - val_loss: 1.0330 - val_accuracy: 0.9007 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 228/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0330 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 229/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 230/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 231/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 232/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 233/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 234/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9006 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 235/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 236/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0329 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 237/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0327 - val_accuracy: 0.9007 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 238/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8977 - val_loss: 1.0328 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 239/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0328 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 240/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 241/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8977 - val_loss: 1.0329 - val_accuracy: 0.9007 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 242/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 243/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8976 - val_loss: 1.0328 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 244/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8980 - val_loss: 1.0328 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 245/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8978 - val_loss: 1.0329 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 246/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9009 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 247/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8980 - val_loss: 1.0327 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 248/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0495 - accuracy: 0.8978 - val_loss: 1.0328 - val_accuracy: 0.9008 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 249/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8979 - val_loss: 1.0328 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 250/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0494 - accuracy: 0.8978 - val_loss: 1.0328 - val_accuracy: 0.9010 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059649946351931 Epoch 251/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1677 - accuracy: 0.8843 - val_loss: 1.1221 - val_accuracy: 0.8967 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9469890021459227 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1374 - accuracy: 0.8920 - val_loss: 1.1163 - val_accuracy: 0.8970 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 253/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1336 - accuracy: 0.8921 - val_loss: 1.1141 - val_accuracy: 0.8968 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1317 - accuracy: 0.8923 - val_loss: 1.1127 - val_accuracy: 0.8966 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1306 - accuracy: 0.8921 - val_loss: 1.1119 - val_accuracy: 0.8964 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1299 - accuracy: 0.8923 - val_loss: 1.1115 - val_accuracy: 0.8963 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1295 - accuracy: 0.8921 - val_loss: 1.1110 - val_accuracy: 0.8966 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1291 - accuracy: 0.8921 - val_loss: 1.1108 - val_accuracy: 0.8965 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1288 - accuracy: 0.8921 - val_loss: 1.1106 - val_accuracy: 0.8966 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1286 - accuracy: 0.8922 - val_loss: 1.1105 - val_accuracy: 0.8966 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1285 - accuracy: 0.8921 - val_loss: 1.1103 - val_accuracy: 0.8970 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 262/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1283 - accuracy: 0.8924 - val_loss: 1.1102 - val_accuracy: 0.8972 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1282 - accuracy: 0.8923 - val_loss: 1.1101 - val_accuracy: 0.8972 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 264/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1282 - accuracy: 0.8924 - val_loss: 1.1101 - val_accuracy: 0.8970 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 265/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1281 - accuracy: 0.8925 - val_loss: 1.1099 - val_accuracy: 0.8970 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1281 - accuracy: 0.8926 - val_loss: 1.1099 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 267/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1280 - accuracy: 0.8926 - val_loss: 1.1098 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8927 - val_loss: 1.1097 - val_accuracy: 0.8970 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8926 - val_loss: 1.1098 - val_accuracy: 0.8972 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8925 - val_loss: 1.1097 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1279 - accuracy: 0.8926 - val_loss: 1.1097 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 272/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1098 - val_accuracy: 0.8969 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 273/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1097 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1097 - val_accuracy: 0.8971 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8974 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 276/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8928 - val_loss: 1.1096 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 279/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8926 - val_loss: 1.1097 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 282/500 235/235 [==============================] - 3s 11ms/step - loss: 1.1278 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1095 - val_accuracy: 0.8978 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 284/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1278 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8972 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8972 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1097 - val_accuracy: 0.8971 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1097 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1095 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1096 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 290/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8927 - val_loss: 1.1096 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1097 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 293/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1097 - val_accuracy: 0.8971 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 294/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 295/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8928 - val_loss: 1.1096 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8930 - val_loss: 1.1096 - val_accuracy: 0.8973 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 298/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8975 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8929 - val_loss: 1.1096 - val_accuracy: 0.8977 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1277 - accuracy: 0.8926 - val_loss: 1.1096 - val_accuracy: 0.8976 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 301/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2805 - accuracy: 0.8674 - val_loss: 1.2165 - val_accuracy: 0.8816 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716503487124464 Epoch 302/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2289 - accuracy: 0.8792 - val_loss: 1.2112 - val_accuracy: 0.8823 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2265 - accuracy: 0.8797 - val_loss: 1.2100 - val_accuracy: 0.8831 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2257 - accuracy: 0.8801 - val_loss: 1.2094 - val_accuracy: 0.8838 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716503487124464 Epoch 305/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2251 - accuracy: 0.8802 - val_loss: 1.2090 - val_accuracy: 0.8841 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 306/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2248 - accuracy: 0.8804 - val_loss: 1.2087 - val_accuracy: 0.8841 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716503487124464 Epoch 307/500 235/235 [==============================] - 2s 10ms/step - loss: 1.2245 - accuracy: 0.8803 - val_loss: 1.2084 - val_accuracy: 0.8840 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9716503487124464 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2242 - accuracy: 0.8801 - val_loss: 1.2082 - val_accuracy: 0.8836 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 309/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2240 - accuracy: 0.8799 - val_loss: 1.2080 - val_accuracy: 0.8836 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 310/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2238 - accuracy: 0.8800 - val_loss: 1.2077 - val_accuracy: 0.8838 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 311/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2234 - accuracy: 0.8800 - val_loss: 1.2071 - val_accuracy: 0.8839 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 312/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2230 - accuracy: 0.8802 - val_loss: 1.2066 - val_accuracy: 0.8842 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 313/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2227 - accuracy: 0.8804 - val_loss: 1.2063 - val_accuracy: 0.8846 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2224 - accuracy: 0.8806 - val_loss: 1.2061 - val_accuracy: 0.8848 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 315/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2223 - accuracy: 0.8805 - val_loss: 1.2059 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 316/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2222 - accuracy: 0.8805 - val_loss: 1.2058 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 317/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2221 - accuracy: 0.8806 - val_loss: 1.2057 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 318/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2221 - accuracy: 0.8805 - val_loss: 1.2056 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2220 - accuracy: 0.8806 - val_loss: 1.2056 - val_accuracy: 0.8851 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 320/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2220 - accuracy: 0.8804 - val_loss: 1.2055 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 321/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2220 - accuracy: 0.8806 - val_loss: 1.2055 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 322/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2055 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 323/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2055 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 324/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2055 - val_accuracy: 0.8853 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 325/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2055 - val_accuracy: 0.8854 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8807 - val_loss: 1.2055 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 327/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8807 - val_loss: 1.2054 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8807 - val_loss: 1.2054 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 330/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 332/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8852 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8803 - val_loss: 1.2054 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8846 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 335/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2053 - val_accuracy: 0.8849 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8851 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 337/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8849 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2053 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8846 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 341/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8846 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2053 - val_accuracy: 0.8847 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 343/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8847 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8847 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 345/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8806 - val_loss: 1.2054 - val_accuracy: 0.8849 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2053 - val_accuracy: 0.8846 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 347/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2053 - val_accuracy: 0.8849 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8806 - val_loss: 1.2053 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2219 - accuracy: 0.8804 - val_loss: 1.2053 - val_accuracy: 0.8851 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2218 - accuracy: 0.8805 - val_loss: 1.2054 - val_accuracy: 0.8850 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9716503487124464 Epoch 351/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5134 - accuracy: 0.7019 - val_loss: 1.4537 - val_accuracy: 0.7037 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4632 - accuracy: 0.7056 - val_loss: 1.4420 - val_accuracy: 0.7029 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 353/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4576 - accuracy: 0.7054 - val_loss: 1.4395 - val_accuracy: 0.7032 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 354/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4562 - accuracy: 0.7055 - val_loss: 1.4385 - val_accuracy: 0.7030 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 355/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4556 - accuracy: 0.7054 - val_loss: 1.4381 - val_accuracy: 0.7034 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 356/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4552 - accuracy: 0.7053 - val_loss: 1.4378 - val_accuracy: 0.7035 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 357/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4550 - accuracy: 0.7055 - val_loss: 1.4376 - val_accuracy: 0.7259 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4549 - accuracy: 0.7054 - val_loss: 1.4374 - val_accuracy: 0.7259 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 359/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4548 - accuracy: 0.7056 - val_loss: 1.4373 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 360/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4547 - accuracy: 0.7055 - val_loss: 1.4372 - val_accuracy: 0.7257 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4546 - accuracy: 0.7053 - val_loss: 1.4372 - val_accuracy: 0.7258 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 362/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4546 - accuracy: 0.7053 - val_loss: 1.4371 - val_accuracy: 0.7257 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4371 - val_accuracy: 0.7259 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 364/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4371 - val_accuracy: 0.7262 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 365/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4371 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 366/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 371/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7259 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 372/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 373/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 374/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 375/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 376/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 377/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 378/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 379/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 380/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7259 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 381/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7052 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 385/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 387/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7262 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7262 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 390/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 393/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9844923551502146 Epoch 394/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 395/500 235/235 [==============================] - 2s 10ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 396/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7262 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 397/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 398/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4545 - accuracy: 0.7053 - val_loss: 1.4370 - val_accuracy: 0.7261 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 399/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7054 - val_loss: 1.4370 - val_accuracy: 0.7259 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 400/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4544 - accuracy: 0.7056 - val_loss: 1.4370 - val_accuracy: 0.7260 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9844923551502146 Epoch 401/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6849 - accuracy: 0.5622 - val_loss: 1.6380 - val_accuracy: 0.5701 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6470 - accuracy: 0.5576 - val_loss: 1.6270 - val_accuracy: 0.5670 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6416 - accuracy: 0.5526 - val_loss: 1.6246 - val_accuracy: 0.5587 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6403 - accuracy: 0.5508 - val_loss: 1.6239 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6398 - accuracy: 0.5508 - val_loss: 1.6236 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6396 - accuracy: 0.5509 - val_loss: 1.6234 - val_accuracy: 0.5555 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6394 - accuracy: 0.5507 - val_loss: 1.6233 - val_accuracy: 0.5555 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6393 - accuracy: 0.5508 - val_loss: 1.6232 - val_accuracy: 0.5556 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6392 - accuracy: 0.5509 - val_loss: 1.6231 - val_accuracy: 0.5558 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6391 - accuracy: 0.5510 - val_loss: 1.6230 - val_accuracy: 0.5561 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6390 - accuracy: 0.5509 - val_loss: 1.6230 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6390 - accuracy: 0.5511 - val_loss: 1.6230 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5513 - val_loss: 1.6229 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5513 - val_loss: 1.6229 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5512 - val_loss: 1.6229 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5512 - val_loss: 1.6229 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6389 - accuracy: 0.5507 - val_loss: 1.6228 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5514 - val_loss: 1.6228 - val_accuracy: 0.5560 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5568 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6228 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6228 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5515 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6227 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 3s 11ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 10ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5568 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5514 - val_loss: 1.6227 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6227 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5508 - val_loss: 1.6227 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5515 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5562 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5568 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5509 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 7ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6387 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5568 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5568 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5566 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5514 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5510 - val_loss: 1.6227 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5513 - val_loss: 1.6227 - val_accuracy: 0.5563 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5567 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5511 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6228 - val_accuracy: 0.5564 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6388 - accuracy: 0.5512 - val_loss: 1.6227 - val_accuracy: 0.5565 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 1/500 235/235 [==============================] - 3s 9ms/step - loss: 0.0023 - accuracy: 0.9991 - val_loss: 0.2456 - val_accuracy: 0.9727 [ 0. 0. 0. ... 0. -1.0181915 0.88523495] Sparsity at: 0.5 Epoch 2/500 235/235 [==============================] - 2s 8ms/step - loss: 9.5415e-04 - accuracy: 0.9997 - val_loss: 0.2419 - val_accuracy: 0.9735 [ 0. 0. 0. ... 0. -1.0231206 0.88134694] Sparsity at: 0.5 Epoch 3/500 235/235 [==============================] - 2s 9ms/step - loss: 3.8316e-04 - accuracy: 0.9999 - val_loss: 0.2335 - val_accuracy: 0.9736 [ 0. 0. 0. ... -0. -1.0226798 0.8862854] Sparsity at: 0.5 Epoch 4/500 235/235 [==============================] - 2s 9ms/step - loss: 9.2704e-05 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9739 [ 0. 0. 0. ... 0. -1.0206683 0.88623744] Sparsity at: 0.5 Epoch 5/500 235/235 [==============================] - 2s 9ms/step - loss: 2.0538e-05 - accuracy: 1.0000 - val_loss: 0.2296 - val_accuracy: 0.9739 [ 0. 0. 0. ... 0. -1.0203844 0.88652563] Sparsity at: 0.5 Epoch 6/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4560e-05 - accuracy: 1.0000 - val_loss: 0.2290 - val_accuracy: 0.9737 [ 0. 0. 0. ... 0. -1.0202798 0.88662416] Sparsity at: 0.5 Epoch 7/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2441e-05 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9735 [ 0. 0. 0. ... 0. -1.0202796 0.88675714] Sparsity at: 0.5 Epoch 8/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0955e-05 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9736 [ 0. 0. 0. ... 0. -1.0203437 0.8869064] Sparsity at: 0.5 Epoch 9/500 235/235 [==============================] - 2s 9ms/step - loss: 9.8052e-06 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9735 [ 0. 0. 0. ... 0. -1.0204452 0.88705945] Sparsity at: 0.5 Epoch 10/500 235/235 [==============================] - 2s 9ms/step - loss: 8.8728e-06 - accuracy: 1.0000 - val_loss: 0.2279 - val_accuracy: 0.9736 [ 0. 0. 0. ... 0. -1.0205829 0.8872201] Sparsity at: 0.5 Epoch 11/500 235/235 [==============================] - 2s 8ms/step - loss: 8.0879e-06 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9735 [ 0. 0. 0. ... 0. -1.0207484 0.8873786] Sparsity at: 0.5 Epoch 12/500 235/235 [==============================] - 2s 9ms/step - loss: 7.4125e-06 - accuracy: 1.0000 - val_loss: 0.2276 - val_accuracy: 0.9736 [ 0. 0. 0. ... 0. -1.0209476 0.88753873] Sparsity at: 0.5 Epoch 13/500 235/235 [==============================] - 2s 8ms/step - loss: 6.8175e-06 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9738 [ 0. 0. 0. ... 0. -1.0211667 0.88770187] Sparsity at: 0.5 Epoch 14/500 235/235 [==============================] - 2s 9ms/step - loss: 6.2891e-06 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9738 [ 0. 0. 0. ... 0. -1.0214062 0.8878677] Sparsity at: 0.5 Epoch 15/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8141e-06 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.9738 [ 0. 0. 0. ... 0. -1.0216669 0.88803166] Sparsity at: 0.5 Epoch 16/500 235/235 [==============================] - 2s 9ms/step - loss: 5.3842e-06 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9739 [ 0. 0. 0. ... 0. -1.0219483 0.888198 ] Sparsity at: 0.5 Epoch 17/500 235/235 [==============================] - 2s 9ms/step - loss: 4.9949e-06 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9739 [ 0. 0. 0. ... 0. -1.0222465 0.88837135] Sparsity at: 0.5 Epoch 18/500 235/235 [==============================] - 2s 9ms/step - loss: 4.6368e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9740 [ 0. 0. 0. ... 0. -1.0225662 0.88855237] Sparsity at: 0.5 Epoch 19/500 235/235 [==============================] - 2s 9ms/step - loss: 4.3109e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9740 [ 0. 0. 0. ... 0. -1.0229051 0.8887399] Sparsity at: 0.5 Epoch 20/500 235/235 [==============================] - 2s 8ms/step - loss: 4.0075e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9742 [ 0. 0. 0. ... 0. -1.0232621 0.8889321] Sparsity at: 0.5 Epoch 21/500 235/235 [==============================] - 2s 8ms/step - loss: 3.7290e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9742 [ 0. 0. 0. ... 0. -1.0236423 0.8891367] Sparsity at: 0.5 Epoch 22/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4699e-06 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9743 [ 0. 0. 0. ... 0. -1.0240476 0.8893552] Sparsity at: 0.5 Epoch 23/500 235/235 [==============================] - 2s 9ms/step - loss: 3.2312e-06 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9743 [ 0. 0. 0. ... 0. -1.0244749 0.88957274] Sparsity at: 0.5 Epoch 24/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0097e-06 - accuracy: 1.0000 - val_loss: 0.2271 - val_accuracy: 0.9745 [ 0. 0. 0. ... 0. -1.0249312 0.889815 ] Sparsity at: 0.5 Epoch 25/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8032e-06 - accuracy: 1.0000 - val_loss: 0.2272 - val_accuracy: 0.9746 [ 0. 0. 0. ... 0. -1.0254152 0.8900664] Sparsity at: 0.5 Epoch 26/500 235/235 [==============================] - 2s 9ms/step - loss: 2.6122e-06 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.9746 [ 0. 0. 0. ... 0. -1.0259303 0.89034384] Sparsity at: 0.5 Epoch 27/500 235/235 [==============================] - 2s 9ms/step - loss: 2.4325e-06 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9747 [ 0. 0. 0. ... 0. -1.0264808 0.8906357] Sparsity at: 0.5 Epoch 28/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2656e-06 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.9746 [ 0. 0. 0. ... 0. -1.0270671 0.8909461] Sparsity at: 0.5 Epoch 29/500 235/235 [==============================] - 2s 9ms/step - loss: 2.1105e-06 - accuracy: 1.0000 - val_loss: 0.2278 - val_accuracy: 0.9746 [ 0. 0. 0. ... 0. -1.0276937 0.8912819] Sparsity at: 0.5 Epoch 30/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9665e-06 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9747 [ 0. 0. 0. ... 0. -1.0283669 0.8916432] Sparsity at: 0.5 Epoch 31/500 235/235 [==============================] - 2s 9ms/step - loss: 1.8303e-06 - accuracy: 1.0000 - val_loss: 0.2283 - val_accuracy: 0.9747 [ 0. 0. 0. ... 0. -1.0290956 0.89201945] Sparsity at: 0.5 Epoch 32/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7044e-06 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9747 [ 0. 0. 0. ... 0. -1.0298685 0.8924315] Sparsity at: 0.5 Epoch 33/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5860e-06 - accuracy: 1.0000 - val_loss: 0.2288 - val_accuracy: 0.9748 [ 0. 0. 0. ... 0. -1.0306965 0.8928702] Sparsity at: 0.5 Epoch 34/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4755e-06 - accuracy: 1.0000 - val_loss: 0.2291 - val_accuracy: 0.9748 [ 0. 0. 0. ... 0. -1.0315812 0.89333075] Sparsity at: 0.5 Epoch 35/500 235/235 [==============================] - 2s 10ms/step - loss: 1.3728e-06 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.0325135 0.8938218] Sparsity at: 0.5 Epoch 36/500 235/235 [==============================] - 2s 10ms/step - loss: 1.2766e-06 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.0335137 0.89435744] Sparsity at: 0.5 Epoch 37/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1870e-06 - accuracy: 1.0000 - val_loss: 0.2302 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.034576 0.89492154] Sparsity at: 0.5 Epoch 38/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1028e-06 - accuracy: 1.0000 - val_loss: 0.2306 - val_accuracy: 0.9748 [ 0. 0. 0. ... 0. -1.035703 0.8955361] Sparsity at: 0.5 Epoch 39/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0243e-06 - accuracy: 1.0000 - val_loss: 0.2310 - val_accuracy: 0.9748 [ 0. 0. 0. ... 0. -1.0369009 0.89618874] Sparsity at: 0.5 Epoch 40/500 235/235 [==============================] - 2s 9ms/step - loss: 9.5076e-07 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9747 [ 0. 0. 0. ... 0. -1.0381527 0.89689505] Sparsity at: 0.5 Epoch 41/500 235/235 [==============================] - 2s 9ms/step - loss: 8.8185e-07 - accuracy: 1.0000 - val_loss: 0.2320 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.039488 0.8976448] Sparsity at: 0.5 Epoch 42/500 235/235 [==============================] - 2s 9ms/step - loss: 8.1843e-07 - accuracy: 1.0000 - val_loss: 0.2325 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.0408934 0.89844894] Sparsity at: 0.5 Epoch 43/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5870e-07 - accuracy: 1.0000 - val_loss: 0.2330 - val_accuracy: 0.9748 [ 0. 0. 0. ... 0. -1.0423669 0.89930505] Sparsity at: 0.5 Epoch 44/500 235/235 [==============================] - 2s 9ms/step - loss: 7.0271e-07 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9748 [ 0. 0. 0. ... 0. -1.0439173 0.90020955] Sparsity at: 0.5 Epoch 45/500 235/235 [==============================] - 2s 9ms/step - loss: 6.5091e-07 - accuracy: 1.0000 - val_loss: 0.2341 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.0455459 0.90117425] Sparsity at: 0.5 Epoch 46/500 235/235 [==============================] - 2s 10ms/step - loss: 6.0229e-07 - accuracy: 1.0000 - val_loss: 0.2346 - val_accuracy: 0.9750 [ 0. 0. 0. ... 0. -1.0472581 0.90217096] Sparsity at: 0.5 Epoch 47/500 235/235 [==============================] - 2s 9ms/step - loss: 5.5735e-07 - accuracy: 1.0000 - val_loss: 0.2352 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.0490443 0.90325636] Sparsity at: 0.5 Epoch 48/500 235/235 [==============================] - 2s 10ms/step - loss: 5.1540e-07 - accuracy: 1.0000 - val_loss: 0.2358 - val_accuracy: 0.9749 [ 0. 0. 0. ... 0. -1.0509125 0.9043943] Sparsity at: 0.5 Epoch 49/500 235/235 [==============================] - 2s 10ms/step - loss: 4.7614e-07 - accuracy: 1.0000 - val_loss: 0.2365 - val_accuracy: 0.9750 [ 0. 0. 0. ... 0. -1.0528693 0.9055982] Sparsity at: 0.5 Epoch 50/500 235/235 [==============================] - 2s 10ms/step - loss: 4.3982e-07 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9751 [ 0. 0. 0. ... 0. -1.0549086 0.90686244] Sparsity at: 0.5 Epoch 51/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0183 - accuracy: 0.9945 - val_loss: 0.1800 - val_accuracy: 0.9736 [ 0. 0. 0. ... -0. -1.0044947 0.8258815] Sparsity at: 0.6458724517167382 Epoch 52/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1746 - val_accuracy: 0.9744 [ 0. 0. 0. ... -0. -1.0078081 0.8313586] Sparsity at: 0.6458724517167382 Epoch 53/500 235/235 [==============================] - 2s 9ms/step - loss: 6.2155e-04 - accuracy: 0.9999 - val_loss: 0.1727 - val_accuracy: 0.9747 [ 0. 0. 0. ... -0. -1.0144657 0.83406806] Sparsity at: 0.6458724517167382 Epoch 54/500 235/235 [==============================] - 2s 8ms/step - loss: 2.8440e-04 - accuracy: 1.0000 - val_loss: 0.1720 - val_accuracy: 0.9751 [ 0. 0. 0. ... -0. -1.0159569 0.83422184] Sparsity at: 0.6458724517167382 Epoch 55/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9494e-04 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9746 [ 0. 0. 0. ... -0. -1.0175234 0.8357566] Sparsity at: 0.6458724517167382 Epoch 56/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6738e-04 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.9748 [ 0. 0. 0. ... -0. -1.0191909 0.83756787] Sparsity at: 0.6458724517167382 Epoch 57/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4860e-04 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9746 [ 0. 0. 0. ... -0. -1.020996 0.83942026] Sparsity at: 0.6458724517167382 Epoch 58/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3377e-04 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.9743 [ 0. 0. 0. ... -0. -1.0229108 0.8414131] Sparsity at: 0.6458724517167382 Epoch 59/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2145e-04 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9742 [ 0. 0. 0. ... -0. -1.0249248 0.84347373] Sparsity at: 0.6458724517167382 Epoch 60/500 235/235 [==============================] - 2s 9ms/step - loss: 1.1089e-04 - accuracy: 1.0000 - val_loss: 0.1746 - val_accuracy: 0.9739 [ 0. 0. 0. ... -0. -1.0270368 0.8456093] Sparsity at: 0.6458724517167382 Epoch 61/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0156e-04 - accuracy: 1.0000 - val_loss: 0.1752 - val_accuracy: 0.9740 [ 0. 0. 0. ... -0. -1.0292325 0.84786755] Sparsity at: 0.6458724517167382 Epoch 62/500 235/235 [==============================] - 2s 8ms/step - loss: 9.3209e-05 - accuracy: 1.0000 - val_loss: 0.1757 - val_accuracy: 0.9740 [ 0. 0. 0. ... -0. -1.0315295 0.8502343] Sparsity at: 0.6458724517167382 Epoch 63/500 235/235 [==============================] - 2s 8ms/step - loss: 8.5712e-05 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9740 [ 0. 0. 0. ... -0. -1.0339124 0.8526186] Sparsity at: 0.6458724517167382 Epoch 64/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8945e-05 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9741 [ 0. 0. 0. ... -0. -1.0363885 0.8552584] Sparsity at: 0.6458724517167382 Epoch 65/500 235/235 [==============================] - 2s 9ms/step - loss: 7.2818e-05 - accuracy: 1.0000 - val_loss: 0.1775 - val_accuracy: 0.9742 [ 0. 0. 0. ... -0. -1.0389671 0.8580301] Sparsity at: 0.6458724517167382 Epoch 66/500 235/235 [==============================] - 2s 9ms/step - loss: 6.7124e-05 - accuracy: 1.0000 - val_loss: 0.1781 - val_accuracy: 0.9743 [ 0. 0. 0. ... -0. -1.0416406 0.86078215] Sparsity at: 0.6458724517167382 Epoch 67/500 235/235 [==============================] - 2s 9ms/step - loss: 6.1960e-05 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9744 [ 0. 0. 0. ... -0. -1.0444332 0.8637285] Sparsity at: 0.6458724517167382 Epoch 68/500 235/235 [==============================] - 2s 9ms/step - loss: 5.7130e-05 - accuracy: 1.0000 - val_loss: 0.1794 - val_accuracy: 0.9743 [ 0. 0. 0. ... -0. -1.0473466 0.8668981] Sparsity at: 0.6458724517167382 Epoch 69/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2735e-05 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9744 [ 0. 0. 0. ... -0. -1.0503454 0.8702127] Sparsity at: 0.6458724517167382 Epoch 70/500 235/235 [==============================] - 2s 8ms/step - loss: 4.8635e-05 - accuracy: 1.0000 - val_loss: 0.1808 - val_accuracy: 0.9746 [ 0. 0. 0. ... -0. -1.0534655 0.87350273] Sparsity at: 0.6458724517167382 Epoch 71/500 235/235 [==============================] - 2s 9ms/step - loss: 4.4853e-05 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.9748 [ 0. 0. 0. ... -0. -1.0566862 0.87715125] Sparsity at: 0.6458724517167382 Epoch 72/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1314e-05 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9747 [ 0. 0. 0. ... -0. -1.0600276 0.88086885] Sparsity at: 0.6458724517167382 Epoch 73/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8053e-05 - accuracy: 1.0000 - val_loss: 0.1830 - val_accuracy: 0.9746 [ 0. 0. 0. ... -0. -1.0634466 0.8847622] Sparsity at: 0.6458724517167382 Epoch 74/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5020e-05 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9748 [ 0. 0. 0. ... -0. -1.067005 0.88867897] Sparsity at: 0.6458724517167382 Epoch 75/500 235/235 [==============================] - 2s 8ms/step - loss: 3.2209e-05 - accuracy: 1.0000 - val_loss: 0.1845 - val_accuracy: 0.9749 [ 0. 0. 0. ... -0. -1.0706282 0.89282113] Sparsity at: 0.6458724517167382 Epoch 76/500 235/235 [==============================] - 2s 8ms/step - loss: 2.9621e-05 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9748 [ 0. 0. 0. ... -0. -1.0743974 0.89704335] Sparsity at: 0.6458724517167382 Epoch 77/500 235/235 [==============================] - 2s 8ms/step - loss: 2.7216e-05 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9750 [ 0. 0. 0. ... -0. -1.0782732 0.9014282] Sparsity at: 0.6458724517167382 Epoch 78/500 235/235 [==============================] - 2s 8ms/step - loss: 2.4975e-05 - accuracy: 1.0000 - val_loss: 0.1871 - val_accuracy: 0.9750 [ 0. 0. 0. ... -0. -1.0822431 0.90574473] Sparsity at: 0.6458724517167382 Epoch 79/500 235/235 [==============================] - 2s 8ms/step - loss: 2.2893e-05 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9751 [ 0. 0. 0. ... -0. -1.0863028 0.91035235] Sparsity at: 0.6458724517167382 Epoch 80/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0955e-05 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9751 [ 0. 0. 0. ... -0. -1.09045 0.9150701] Sparsity at: 0.6458724517167382 Epoch 81/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9207e-05 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9754 [ 0. 0. 0. ... -0. -1.0947087 0.91975933] Sparsity at: 0.6458724517167382 Epoch 82/500 235/235 [==============================] - 2s 9ms/step - loss: 1.7570e-05 - accuracy: 1.0000 - val_loss: 0.1907 - val_accuracy: 0.9755 [ 0. 0. 0. ... -0. -1.099098 0.92468375] Sparsity at: 0.6458724517167382 Epoch 83/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6058e-05 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9754 [ 0. 0. 0. ... 0. -1.1035762 0.92977226] Sparsity at: 0.6458724517167382 Epoch 84/500 235/235 [==============================] - 2s 8ms/step - loss: 1.4661e-05 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9753 [ 0. 0. 0. ... -0. -1.1081384 0.93481517] Sparsity at: 0.6458724517167382 Epoch 85/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3369e-05 - accuracy: 1.0000 - val_loss: 0.1938 - val_accuracy: 0.9755 [ 0. 0. 0. ... -0. -1.1128359 0.9401547] Sparsity at: 0.6458724517167382 Epoch 86/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2189e-05 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9754 [ 0. 0. 0. ... -0. -1.1176136 0.9453849] Sparsity at: 0.6458724517167382 Epoch 87/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1112e-05 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9755 [ 0. 0. 0. ... -0. -1.1224889 0.95076984] Sparsity at: 0.6458724517167382 Epoch 88/500 235/235 [==============================] - 2s 9ms/step - loss: 1.0102e-05 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9755 [ 0. 0. 0. ... 0. -1.127452 0.95645326] Sparsity at: 0.6458724517167382 Epoch 89/500 235/235 [==============================] - 2s 9ms/step - loss: 9.1733e-06 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9756 [ 0. 0. 0. ... -0. -1.132506 0.9621127] Sparsity at: 0.6458724517167382 Epoch 90/500 235/235 [==============================] - 2s 9ms/step - loss: 8.3432e-06 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9757 [ 0. 0. 0. ... -0. -1.1377124 0.968016 ] Sparsity at: 0.6458724517167382 Epoch 91/500 235/235 [==============================] - 2s 9ms/step - loss: 7.5675e-06 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9759 [ 0. 0. 0. ... 0. -1.1429793 0.973983 ] Sparsity at: 0.6458724517167382 Epoch 92/500 235/235 [==============================] - 2s 9ms/step - loss: 6.8538e-06 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9759 [ 0. 0. 0. ... 0. -1.1483622 0.97986007] Sparsity at: 0.6458724517167382 Epoch 93/500 235/235 [==============================] - 2s 9ms/step - loss: 6.2062e-06 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9760 [ 0. 0. 0. ... -0. -1.1538146 0.98587465] Sparsity at: 0.6458724517167382 Epoch 94/500 235/235 [==============================] - 2s 9ms/step - loss: 5.6216e-06 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9761 [ 0. 0. 0. ... -0. -1.1594172 0.99216473] Sparsity at: 0.6458724517167382 Epoch 95/500 235/235 [==============================] - 2s 8ms/step - loss: 5.0818e-06 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9762 [ 0. 0. 0. ... -0. -1.1650869 0.9984474] Sparsity at: 0.6458724517167382 Epoch 96/500 235/235 [==============================] - 2s 9ms/step - loss: 4.5906e-06 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9762 [ 0. 0. 0. ... 0. -1.1708474 1.0047092] Sparsity at: 0.6458724517167382 Epoch 97/500 235/235 [==============================] - 2s 9ms/step - loss: 4.1432e-06 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9761 [ 0. 0. 0. ... 0. -1.1766921 1.0110048] Sparsity at: 0.6458724517167382 Epoch 98/500 235/235 [==============================] - 2s 9ms/step - loss: 3.7402e-06 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9762 [ 0. 0. 0. ... 0. -1.18261 1.0173999] Sparsity at: 0.6458724517167382 Epoch 99/500 235/235 [==============================] - 2s 8ms/step - loss: 3.3735e-06 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9761 [ 0. 0. 0. ... 0. -1.1885993 1.0237564] Sparsity at: 0.6458724517167382 Epoch 100/500 235/235 [==============================] - 2s 8ms/step - loss: 3.0358e-06 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9761 [ 0. 0. 0. ... -0. -1.1947235 1.0302945] Sparsity at: 0.6458724517167382 Epoch 101/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0421 - accuracy: 0.9871 - val_loss: 0.1704 - val_accuracy: 0.9702 [ 0. 0. 0. ... 0. -1.1724231 0.9685865] Sparsity at: 0.759438707081545 Epoch 102/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0109 - accuracy: 0.9962 - val_loss: 0.1671 - val_accuracy: 0.9709 [ 0. 0. 0. ... 0. -1.1719555 0.9674918] Sparsity at: 0.759438707081545 Epoch 103/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.1663 - val_accuracy: 0.9713 [ 0. 0. 0. ... 0. -1.1666331 0.9704507] Sparsity at: 0.759438707081545 Epoch 104/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1645 - val_accuracy: 0.9720 [ 0. 0. 0. ... 0. -1.1633383 0.9720621] Sparsity at: 0.759438707081545 Epoch 105/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0032 - accuracy: 0.9997 - val_loss: 0.1641 - val_accuracy: 0.9719 [ 0. 0. 0. ... 0. -1.1623425 0.9759455] Sparsity at: 0.759438707081545 Epoch 106/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1643 - val_accuracy: 0.9720 [ 0. 0. 0. ... 0. -1.1621644 0.98052573] Sparsity at: 0.759438707081545 Epoch 107/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 0.9999 - val_loss: 0.1646 - val_accuracy: 0.9720 [ 0. 0. 0. ... 0. -1.1630228 0.9851188] Sparsity at: 0.759438707081545 Epoch 108/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1650 - val_accuracy: 0.9722 [ 0. 0. 0. ... 0. -1.1645243 0.9898876] Sparsity at: 0.759438707081545 Epoch 109/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1655 - val_accuracy: 0.9724 [ 0. 0. 0. ... 0. -1.166787 0.99414617] Sparsity at: 0.759438707081545 Epoch 110/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1661 - val_accuracy: 0.9723 [ 0. 0. 0. ... 0. -1.1695281 0.99826455] Sparsity at: 0.759438707081545 Epoch 111/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.1667 - val_accuracy: 0.9721 [ 0. 0. 0. ... 0. -1.1724857 1.002295 ] Sparsity at: 0.759438707081545 Epoch 112/500 235/235 [==============================] - 2s 9ms/step - loss: 9.7824e-04 - accuracy: 1.0000 - val_loss: 0.1675 - val_accuracy: 0.9721 [ 0. 0. 0. ... 0. -1.1757934 1.0063 ] Sparsity at: 0.759438707081545 Epoch 113/500 235/235 [==============================] - 2s 9ms/step - loss: 8.7433e-04 - accuracy: 1.0000 - val_loss: 0.1683 - val_accuracy: 0.9718 [ 0. 0. 0. ... 0. -1.1792421 1.0103333] Sparsity at: 0.759438707081545 Epoch 114/500 235/235 [==============================] - 2s 8ms/step - loss: 7.8218e-04 - accuracy: 1.0000 - val_loss: 0.1692 - val_accuracy: 0.9720 [ 0. 0. 0. ... 0. -1.182873 1.0146369] Sparsity at: 0.759438707081545 Epoch 115/500 235/235 [==============================] - 2s 8ms/step - loss: 7.0314e-04 - accuracy: 1.0000 - val_loss: 0.1701 - val_accuracy: 0.9724 [ 0. 0. 0. ... 0. -1.1867957 1.0192901] Sparsity at: 0.759438707081545 Epoch 116/500 235/235 [==============================] - 2s 9ms/step - loss: 6.3504e-04 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9723 [ 0. 0. 0. ... 0. -1.190718 1.0241282] Sparsity at: 0.759438707081545 Epoch 117/500 235/235 [==============================] - 2s 8ms/step - loss: 5.7357e-04 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9722 [ 0. 0. 0. ... 0. -1.1949095 1.0292276] Sparsity at: 0.759438707081545 Epoch 118/500 235/235 [==============================] - 2s 8ms/step - loss: 5.1924e-04 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.9723 [ 0. 0. 0. ... 0. -1.1993282 1.03442 ] Sparsity at: 0.759438707081545 Epoch 119/500 235/235 [==============================] - 2s 9ms/step - loss: 4.7045e-04 - accuracy: 1.0000 - val_loss: 0.1742 - val_accuracy: 0.9724 [ 0. 0. 0. ... 0. -1.2040023 1.0398817] Sparsity at: 0.759438707081545 Epoch 120/500 235/235 [==============================] - 2s 9ms/step - loss: 4.2618e-04 - accuracy: 1.0000 - val_loss: 0.1753 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2088557 1.0455652] Sparsity at: 0.759438707081545 Epoch 121/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8669e-04 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9724 [ 0. 0. 0. ... 0. -1.21384 1.0516487] Sparsity at: 0.759438707081545 Epoch 122/500 235/235 [==============================] - 2s 8ms/step - loss: 3.5113e-04 - accuracy: 1.0000 - val_loss: 0.1776 - val_accuracy: 0.9724 [ 0. 0. 0. ... 0. -1.2191674 1.0578794] Sparsity at: 0.759438707081545 Epoch 123/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1877e-04 - accuracy: 1.0000 - val_loss: 0.1788 - val_accuracy: 0.9724 [ 0. 0. 0. ... 0. -1.2246436 1.0643942] Sparsity at: 0.759438707081545 Epoch 124/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8929e-04 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2302443 1.0710657] Sparsity at: 0.759438707081545 Epoch 125/500 235/235 [==============================] - 2s 8ms/step - loss: 2.6259e-04 - accuracy: 1.0000 - val_loss: 0.1814 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2361274 1.0782167] Sparsity at: 0.759438707081545 Epoch 126/500 235/235 [==============================] - 2s 8ms/step - loss: 2.3885e-04 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2421038 1.0854352] Sparsity at: 0.759438707081545 Epoch 127/500 235/235 [==============================] - 2s 8ms/step - loss: 2.1638e-04 - accuracy: 1.0000 - val_loss: 0.1840 - val_accuracy: 0.9727 [ 0. 0. 0. ... 0. -1.2484224 1.0930289] Sparsity at: 0.759438707081545 Epoch 128/500 235/235 [==============================] - 2s 9ms/step - loss: 1.9629e-04 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2548465 1.1004783] Sparsity at: 0.759438707081545 Epoch 129/500 235/235 [==============================] - 2s 8ms/step - loss: 1.7807e-04 - accuracy: 1.0000 - val_loss: 0.1868 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2614312 1.1085973] Sparsity at: 0.759438707081545 Epoch 130/500 235/235 [==============================] - 2s 9ms/step - loss: 1.6126e-04 - accuracy: 1.0000 - val_loss: 0.1883 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2681916 1.116809 ] Sparsity at: 0.759438707081545 Epoch 131/500 235/235 [==============================] - 2s 9ms/step - loss: 1.4616e-04 - accuracy: 1.0000 - val_loss: 0.1898 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2751759 1.1250877] Sparsity at: 0.759438707081545 Epoch 132/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3241e-04 - accuracy: 1.0000 - val_loss: 0.1913 - val_accuracy: 0.9725 [ 0. 0. 0. ... 0. -1.2822132 1.1334398] Sparsity at: 0.759438707081545 Epoch 133/500 235/235 [==============================] - 2s 8ms/step - loss: 1.1988e-04 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9727 [ 0. 0. 0. ... 0. -1.2894782 1.142117 ] Sparsity at: 0.759438707081545 Epoch 134/500 235/235 [==============================] - 2s 8ms/step - loss: 1.0835e-04 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9729 [ 0. 0. 0. ... 0. -1.2968236 1.150963 ] Sparsity at: 0.759438707081545 Epoch 135/500 235/235 [==============================] - 2s 8ms/step - loss: 9.7970e-05 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9729 [ 0. 0. 0. ... 0. -1.304301 1.1598463] Sparsity at: 0.759438707081545 Epoch 136/500 235/235 [==============================] - 2s 8ms/step - loss: 8.8480e-05 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9730 [ 0. 0. 0. ... 0. -1.311966 1.1689874] Sparsity at: 0.759438707081545 Epoch 137/500 235/235 [==============================] - 2s 9ms/step - loss: 8.0059e-05 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9729 [ 0. 0. 0. ... 0. -1.3196768 1.1780622] Sparsity at: 0.759438707081545 Epoch 138/500 235/235 [==============================] - 2s 8ms/step - loss: 7.2113e-05 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9728 [ 0. 0. 0. ... 0. -1.3274646 1.1875334] Sparsity at: 0.759438707081545 Epoch 139/500 235/235 [==============================] - 2s 8ms/step - loss: 6.5082e-05 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9730 [ 0. 0. 0. ... 0. -1.3355262 1.1969401] Sparsity at: 0.759438707081545 Epoch 140/500 235/235 [==============================] - 2s 8ms/step - loss: 5.8765e-05 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9730 [ 0. 0. 0. ... 0. -1.3436683 1.2065746] Sparsity at: 0.759438707081545 Epoch 141/500 235/235 [==============================] - 2s 8ms/step - loss: 5.2940e-05 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9730 [ 0. 0. 0. ... 0. -1.3517257 1.2162542] Sparsity at: 0.759438707081545 Epoch 142/500 235/235 [==============================] - 2s 9ms/step - loss: 4.7707e-05 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9730 [ 0. 0. 0. ... 0. -1.360137 1.2257247] Sparsity at: 0.759438707081545 Epoch 143/500 235/235 [==============================] - 2s 8ms/step - loss: 4.2987e-05 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9732 [ 0. 0. 0. ... 0. -1.3685989 1.2358656] Sparsity at: 0.759438707081545 Epoch 144/500 235/235 [==============================] - 2s 8ms/step - loss: 3.8692e-05 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9733 [ 0. 0. 0. ... 0. -1.3769926 1.2454377] Sparsity at: 0.759438707081545 Epoch 145/500 235/235 [==============================] - 2s 9ms/step - loss: 3.4797e-05 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9733 [ 0. 0. 0. ... 0. -1.3856144 1.2556477] Sparsity at: 0.759438707081545 Epoch 146/500 235/235 [==============================] - 2s 8ms/step - loss: 3.1304e-05 - accuracy: 1.0000 - val_loss: 0.2160 - val_accuracy: 0.9730 [ 0. 0. 0. ... 0. -1.3941742 1.265625 ] Sparsity at: 0.759438707081545 Epoch 147/500 235/235 [==============================] - 2s 9ms/step - loss: 2.8109e-05 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9731 [ 0. 0. 0. ... 0. -1.4029249 1.2756073] Sparsity at: 0.759438707081545 Epoch 148/500 235/235 [==============================] - 2s 8ms/step - loss: 2.5272e-05 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9729 [ 0. 0. 0. ... 0. -1.4117149 1.2860515] Sparsity at: 0.759438707081545 Epoch 149/500 235/235 [==============================] - 2s 9ms/step - loss: 2.2729e-05 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9729 [ 0. 0. 0. ... 0. -1.420476 1.2957963] Sparsity at: 0.759438707081545 Epoch 150/500 235/235 [==============================] - 2s 8ms/step - loss: 2.0410e-05 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9729 [ 0. 0. 0. ... 0. -1.429346 1.3061253] Sparsity at: 0.759438707081545 Epoch 151/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0964 - accuracy: 0.9744 - val_loss: 0.1952 - val_accuracy: 0.9661 [ 0. 0. 0. ... 0. -1.2941321 0. ] Sparsity at: 0.8448229613733905 Epoch 152/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0401 - accuracy: 0.9872 - val_loss: 0.1885 - val_accuracy: 0.9672 [ 0. 0. 0. ... 0. -1.2721828 0. ] Sparsity at: 0.8448229613733905 Epoch 153/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0295 - accuracy: 0.9903 - val_loss: 0.1842 - val_accuracy: 0.9684 [ 0. 0. 0. ... 0. -1.2564533 0. ] Sparsity at: 0.8448229613733905 Epoch 154/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0239 - accuracy: 0.9920 - val_loss: 0.1808 - val_accuracy: 0.9692 [ 0. 0. 0. ... 0. -1.244729 0. ] Sparsity at: 0.8448229613733905 Epoch 155/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0203 - accuracy: 0.9932 - val_loss: 0.1786 - val_accuracy: 0.9700 [ 0. 0. 0. ... 0. -1.2358537 0. ] Sparsity at: 0.8448229613733905 Epoch 156/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0177 - accuracy: 0.9943 - val_loss: 0.1768 - val_accuracy: 0.9700 [ 0. 0. 0. ... 0. -1.2291857 0. ] Sparsity at: 0.8448229613733905 Epoch 157/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0157 - accuracy: 0.9950 - val_loss: 0.1755 - val_accuracy: 0.9701 [ 0. 0. 0. ... 0. -1.2241826 0. ] Sparsity at: 0.8448229613733905 Epoch 158/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0141 - accuracy: 0.9958 - val_loss: 0.1744 - val_accuracy: 0.9706 [ 0. 0. 0. ... 0. -1.2205951 0. ] Sparsity at: 0.8448229613733905 Epoch 159/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0127 - accuracy: 0.9964 - val_loss: 0.1739 - val_accuracy: 0.9704 [ 0. 0. 0. ... 0. -1.2179171 0. ] Sparsity at: 0.8448229613733905 Epoch 160/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0116 - accuracy: 0.9968 - val_loss: 0.1734 - val_accuracy: 0.9706 [ 0. 0. 0. ... 0. -1.2157714 0. ] Sparsity at: 0.8448229613733905 Epoch 161/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0106 - accuracy: 0.9973 - val_loss: 0.1731 - val_accuracy: 0.9706 [ 0. 0. 0. ... 0. -1.214289 0. ] Sparsity at: 0.8448229613733905 Epoch 162/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.1728 - val_accuracy: 0.9710 [ 0. 0. 0. ... 0. -1.2130989 0. ] Sparsity at: 0.8448229613733905 Epoch 163/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0090 - accuracy: 0.9980 - val_loss: 0.1728 - val_accuracy: 0.9712 [ 0. 0. 0. ... 0. -1.2123059 0. ] Sparsity at: 0.8448229613733905 Epoch 164/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.1730 - val_accuracy: 0.9709 [ 0. 0. 0. ... -0. -1.2114054 0. ] Sparsity at: 0.8448229613733905 Epoch 165/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0077 - accuracy: 0.9985 - val_loss: 0.1731 - val_accuracy: 0.9708 [ 0. 0. 0. ... 0. -1.2114593 0. ] Sparsity at: 0.8448229613733905 Epoch 166/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0071 - accuracy: 0.9987 - val_loss: 0.1736 - val_accuracy: 0.9709 [ 0. 0. 0. ... 0. -1.2115622 0. ] Sparsity at: 0.8448229613733905 Epoch 167/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0066 - accuracy: 0.9991 - val_loss: 0.1741 - val_accuracy: 0.9706 [ 0. 0. 0. ... 0. -1.2119647 0. ] Sparsity at: 0.8448229613733905 Epoch 168/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0062 - accuracy: 0.9992 - val_loss: 0.1745 - val_accuracy: 0.9704 [ 0. 0. 0. ... -0. -1.2125804 0. ] Sparsity at: 0.8448229613733905 Epoch 169/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0057 - accuracy: 0.9994 - val_loss: 0.1752 - val_accuracy: 0.9705 [ 0. 0. 0. ... 0. -1.2135597 0. ] Sparsity at: 0.8448229613733905 Epoch 170/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0054 - accuracy: 0.9995 - val_loss: 0.1756 - val_accuracy: 0.9703 [ 0. 0. 0. ... 0. -1.2143645 0. ] Sparsity at: 0.8448229613733905 Epoch 171/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0050 - accuracy: 0.9996 - val_loss: 0.1763 - val_accuracy: 0.9703 [ 0. 0. 0. ... -0. -1.2157342 0. ] Sparsity at: 0.8448229613733905 Epoch 172/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0047 - accuracy: 0.9997 - val_loss: 0.1771 - val_accuracy: 0.9702 [ 0. 0. 0. ... -0. -1.2175868 0. ] Sparsity at: 0.8448229613733905 Epoch 173/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9997 - val_loss: 0.1778 - val_accuracy: 0.9703 [ 0. 0. 0. ... -0. -1.219726 0. ] Sparsity at: 0.8448229613733905 Epoch 174/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0041 - accuracy: 0.9998 - val_loss: 0.1789 - val_accuracy: 0.9702 [ 0. 0. 0. ... -0. -1.2223516 0. ] Sparsity at: 0.8448229613733905 Epoch 175/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0039 - accuracy: 0.9998 - val_loss: 0.1797 - val_accuracy: 0.9701 [ 0. 0. 0. ... -0. -1.2254808 0. ] Sparsity at: 0.8448229613733905 Epoch 176/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0036 - accuracy: 0.9999 - val_loss: 0.1808 - val_accuracy: 0.9705 [ 0. 0. 0. ... 0. -1.2287586 0. ] Sparsity at: 0.8448229613733905 Epoch 177/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0034 - accuracy: 0.9999 - val_loss: 0.1815 - val_accuracy: 0.9705 [ 0. 0. 0. ... -0. -1.2325125 0. ] Sparsity at: 0.8448229613733905 Epoch 178/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0032 - accuracy: 0.9999 - val_loss: 0.1827 - val_accuracy: 0.9703 [ 0. 0. 0. ... -0. -1.2364184 0. ] Sparsity at: 0.8448229613733905 Epoch 179/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.1835 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -1.240545 0. ] Sparsity at: 0.8448229613733905 Epoch 180/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -1.2451948 0. ] Sparsity at: 0.8448229613733905 Epoch 181/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9708 [ 0. 0. 0. ... -0. -1.250121 0. ] Sparsity at: 0.8448229613733905 Epoch 182/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9709 [ 0. 0. 0. ... -0. -1.255553 0. ] Sparsity at: 0.8448229613733905 Epoch 183/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.1880 - val_accuracy: 0.9708 [ 0. 0. 0. ... -0. -1.2609473 0. ] Sparsity at: 0.8448229613733905 Epoch 184/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1893 - val_accuracy: 0.9709 [ 0. 0. 0. ... -0. -1.2667714 0. ] Sparsity at: 0.8448229613733905 Epoch 185/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9709 [ 0. 0. 0. ... -0. -1.2725538 0. ] Sparsity at: 0.8448229613733905 Epoch 186/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 0.9710 [ 0. 0. 0. ... -0. -1.2783074 0. ] Sparsity at: 0.8448229613733905 Epoch 187/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9708 [ 0. 0. 0. ... -0. -1.2848704 0. ] Sparsity at: 0.8448229613733905 Epoch 188/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9708 [ 0. 0. 0. ... -0. -1.2913005 0. ] Sparsity at: 0.8448229613733905 Epoch 189/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9707 [ 0. 0. 0. ... -0. -1.2981095 0. ] Sparsity at: 0.8448229613733905 Epoch 190/500 235/235 [==============================] - 2s 10ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -1.3049219 0. ] Sparsity at: 0.8448229613733905 Epoch 191/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -1.3121951 0. ] Sparsity at: 0.8448229613733905 Epoch 192/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9705 [ 0. 0. 0. ... -0. -1.3192942 0. ] Sparsity at: 0.8448229613733905 Epoch 193/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9705 [ 0. 0. 0. ... -0. -1.3271637 0. ] Sparsity at: 0.8448229613733905 Epoch 194/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9706 [ 0. 0. 0. ... 0. -1.3346442 0. ] Sparsity at: 0.8448229613733905 Epoch 195/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -1.34241 0. ] Sparsity at: 0.8448229613733905 Epoch 196/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9704 [ 0. 0. 0. ... 0. -1.3502862 0. ] Sparsity at: 0.8448229613733905 Epoch 197/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9704 [ 0. 0. 0. ... 0. -1.3579656 0. ] Sparsity at: 0.8448229613733905 Epoch 198/500 235/235 [==============================] - 2s 9ms/step - loss: 9.5605e-04 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9705 [ 0. 0. 0. ... -0. -1.3659614 0. ] Sparsity at: 0.8448229613733905 Epoch 199/500 235/235 [==============================] - 2s 9ms/step - loss: 8.9668e-04 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9706 [ 0. 0. 0. ... -0. -1.3740524 0. ] Sparsity at: 0.8448229613733905 Epoch 200/500 235/235 [==============================] - 2s 9ms/step - loss: 8.4497e-04 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9706 [ 0. 0. 0. ... 0. -1.3822023 0. ] Sparsity at: 0.8448229613733905 Epoch 201/500 235/235 [==============================] - 2s 8ms/step - loss: 0.2007 - accuracy: 0.9465 - val_loss: 0.2152 - val_accuracy: 0.9520 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 202/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1068 - accuracy: 0.9666 - val_loss: 0.1938 - val_accuracy: 0.9568 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 203/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0896 - accuracy: 0.9712 - val_loss: 0.1838 - val_accuracy: 0.9583 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 204/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0802 - accuracy: 0.9736 - val_loss: 0.1778 - val_accuracy: 0.9587 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 205/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0739 - accuracy: 0.9753 - val_loss: 0.1737 - val_accuracy: 0.9597 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 206/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0692 - accuracy: 0.9768 - val_loss: 0.1706 - val_accuracy: 0.9602 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 207/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0655 - accuracy: 0.9783 - val_loss: 0.1683 - val_accuracy: 0.9611 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 208/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0624 - accuracy: 0.9789 - val_loss: 0.1665 - val_accuracy: 0.9619 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 209/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0598 - accuracy: 0.9800 - val_loss: 0.1651 - val_accuracy: 0.9619 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 210/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0577 - accuracy: 0.9807 - val_loss: 0.1639 - val_accuracy: 0.9626 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 211/500 235/235 [==============================] - 2s 9ms/step - loss: 0.0557 - accuracy: 0.9812 - val_loss: 0.1629 - val_accuracy: 0.9627 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 212/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0541 - accuracy: 0.9818 - val_loss: 0.1620 - val_accuracy: 0.9624 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 213/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0525 - accuracy: 0.9823 - val_loss: 0.1613 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 214/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0512 - accuracy: 0.9829 - val_loss: 0.1608 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 215/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0499 - accuracy: 0.9834 - val_loss: 0.1603 - val_accuracy: 0.9628 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 216/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0488 - accuracy: 0.9837 - val_loss: 0.1599 - val_accuracy: 0.9631 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 217/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0478 - accuracy: 0.9842 - val_loss: 0.1595 - val_accuracy: 0.9632 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 218/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0469 - accuracy: 0.9846 - val_loss: 0.1592 - val_accuracy: 0.9631 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 219/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0459 - accuracy: 0.9849 - val_loss: 0.1590 - val_accuracy: 0.9630 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 220/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0451 - accuracy: 0.9851 - val_loss: 0.1588 - val_accuracy: 0.9631 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 221/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0444 - accuracy: 0.9854 - val_loss: 0.1587 - val_accuracy: 0.9633 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 222/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0437 - accuracy: 0.9856 - val_loss: 0.1586 - val_accuracy: 0.9638 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 223/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0430 - accuracy: 0.9859 - val_loss: 0.1586 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 224/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0423 - accuracy: 0.9861 - val_loss: 0.1586 - val_accuracy: 0.9638 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 225/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0417 - accuracy: 0.9862 - val_loss: 0.1585 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 226/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0411 - accuracy: 0.9865 - val_loss: 0.1585 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 227/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0406 - accuracy: 0.9868 - val_loss: 0.1587 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 228/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0401 - accuracy: 0.9871 - val_loss: 0.1587 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 229/500 235/235 [==============================] - 2s 8ms/step - loss: 0.0396 - accuracy: 0.9873 - val_loss: 0.1589 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 230/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0391 - accuracy: 0.9876 - val_loss: 0.1590 - val_accuracy: 0.9634 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 231/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0386 - accuracy: 0.9877 - val_loss: 0.1592 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 232/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0382 - accuracy: 0.9879 - val_loss: 0.1594 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 233/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0378 - accuracy: 0.9880 - val_loss: 0.1596 - val_accuracy: 0.9637 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 234/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0374 - accuracy: 0.9883 - val_loss: 0.1598 - val_accuracy: 0.9635 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 235/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0370 - accuracy: 0.9885 - val_loss: 0.1600 - val_accuracy: 0.9633 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 236/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0366 - accuracy: 0.9886 - val_loss: 0.1603 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 237/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0363 - accuracy: 0.9887 - val_loss: 0.1605 - val_accuracy: 0.9636 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 238/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0359 - accuracy: 0.9888 - val_loss: 0.1608 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 239/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0356 - accuracy: 0.9891 - val_loss: 0.1611 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 240/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0352 - accuracy: 0.9892 - val_loss: 0.1613 - val_accuracy: 0.9639 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9059985246781116 Epoch 241/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0349 - accuracy: 0.9894 - val_loss: 0.1617 - val_accuracy: 0.9640 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 242/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0346 - accuracy: 0.9894 - val_loss: 0.1620 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 243/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0343 - accuracy: 0.9896 - val_loss: 0.1623 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 244/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0340 - accuracy: 0.9897 - val_loss: 0.1627 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 245/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0337 - accuracy: 0.9898 - val_loss: 0.1630 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 246/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0334 - accuracy: 0.9899 - val_loss: 0.1634 - val_accuracy: 0.9643 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 247/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0331 - accuracy: 0.9901 - val_loss: 0.1637 - val_accuracy: 0.9641 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 248/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0329 - accuracy: 0.9902 - val_loss: 0.1641 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 249/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0326 - accuracy: 0.9903 - val_loss: 0.1645 - val_accuracy: 0.9642 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 250/500 235/235 [==============================] - 2s 7ms/step - loss: 0.0324 - accuracy: 0.9904 - val_loss: 0.1648 - val_accuracy: 0.9644 [ 0. 0. 0. ... 0. -0. -0.] Sparsity at: 0.9059985246781116 Epoch 251/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4951 - accuracy: 0.8515 - val_loss: 0.3678 - val_accuracy: 0.8900 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9469890021459227 Epoch 252/500 235/235 [==============================] - 2s 9ms/step - loss: 0.3094 - accuracy: 0.9025 - val_loss: 0.3142 - val_accuracy: 0.9058 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 253/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2702 - accuracy: 0.9153 - val_loss: 0.2883 - val_accuracy: 0.9150 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 254/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2489 - accuracy: 0.9221 - val_loss: 0.2725 - val_accuracy: 0.9195 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 255/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2352 - accuracy: 0.9266 - val_loss: 0.2615 - val_accuracy: 0.9228 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 256/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2252 - accuracy: 0.9297 - val_loss: 0.2532 - val_accuracy: 0.9256 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 257/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2176 - accuracy: 0.9323 - val_loss: 0.2468 - val_accuracy: 0.9276 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 258/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2114 - accuracy: 0.9346 - val_loss: 0.2414 - val_accuracy: 0.9289 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 259/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2063 - accuracy: 0.9359 - val_loss: 0.2370 - val_accuracy: 0.9304 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 260/500 235/235 [==============================] - 2s 9ms/step - loss: 0.2020 - accuracy: 0.9371 - val_loss: 0.2333 - val_accuracy: 0.9315 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 261/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1983 - accuracy: 0.9384 - val_loss: 0.2301 - val_accuracy: 0.9330 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 262/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1950 - accuracy: 0.9393 - val_loss: 0.2273 - val_accuracy: 0.9340 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 263/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1921 - accuracy: 0.9400 - val_loss: 0.2247 - val_accuracy: 0.9347 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 264/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1895 - accuracy: 0.9408 - val_loss: 0.2225 - val_accuracy: 0.9359 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 265/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1872 - accuracy: 0.9417 - val_loss: 0.2205 - val_accuracy: 0.9361 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 266/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1851 - accuracy: 0.9425 - val_loss: 0.2186 - val_accuracy: 0.9362 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 267/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1832 - accuracy: 0.9432 - val_loss: 0.2169 - val_accuracy: 0.9369 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 268/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1814 - accuracy: 0.9437 - val_loss: 0.2154 - val_accuracy: 0.9367 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 269/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1798 - accuracy: 0.9441 - val_loss: 0.2140 - val_accuracy: 0.9372 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 270/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1783 - accuracy: 0.9445 - val_loss: 0.2127 - val_accuracy: 0.9375 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 271/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1769 - accuracy: 0.9448 - val_loss: 0.2114 - val_accuracy: 0.9379 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 272/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1756 - accuracy: 0.9453 - val_loss: 0.2103 - val_accuracy: 0.9378 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 273/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1743 - accuracy: 0.9455 - val_loss: 0.2092 - val_accuracy: 0.9383 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 274/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1732 - accuracy: 0.9461 - val_loss: 0.2082 - val_accuracy: 0.9387 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 275/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1721 - accuracy: 0.9466 - val_loss: 0.2073 - val_accuracy: 0.9392 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 276/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1711 - accuracy: 0.9468 - val_loss: 0.2063 - val_accuracy: 0.9400 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 277/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1701 - accuracy: 0.9469 - val_loss: 0.2055 - val_accuracy: 0.9402 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 278/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1692 - accuracy: 0.9470 - val_loss: 0.2047 - val_accuracy: 0.9406 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 279/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1684 - accuracy: 0.9473 - val_loss: 0.2039 - val_accuracy: 0.9408 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 280/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1676 - accuracy: 0.9475 - val_loss: 0.2032 - val_accuracy: 0.9409 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 281/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1668 - accuracy: 0.9479 - val_loss: 0.2025 - val_accuracy: 0.9412 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 282/500 235/235 [==============================] - 2s 10ms/step - loss: 0.1660 - accuracy: 0.9479 - val_loss: 0.2018 - val_accuracy: 0.9415 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 283/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1653 - accuracy: 0.9482 - val_loss: 0.2012 - val_accuracy: 0.9422 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 284/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1646 - accuracy: 0.9486 - val_loss: 0.2006 - val_accuracy: 0.9425 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 285/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1640 - accuracy: 0.9487 - val_loss: 0.2001 - val_accuracy: 0.9425 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 286/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1634 - accuracy: 0.9486 - val_loss: 0.1995 - val_accuracy: 0.9427 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 287/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1628 - accuracy: 0.9490 - val_loss: 0.1990 - val_accuracy: 0.9433 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 288/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1622 - accuracy: 0.9491 - val_loss: 0.1985 - val_accuracy: 0.9436 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 289/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1616 - accuracy: 0.9492 - val_loss: 0.1980 - val_accuracy: 0.9436 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 290/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1610 - accuracy: 0.9496 - val_loss: 0.1976 - val_accuracy: 0.9435 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 291/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1605 - accuracy: 0.9496 - val_loss: 0.1971 - val_accuracy: 0.9437 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 292/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1600 - accuracy: 0.9498 - val_loss: 0.1967 - val_accuracy: 0.9441 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 293/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1595 - accuracy: 0.9500 - val_loss: 0.1963 - val_accuracy: 0.9444 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 294/500 235/235 [==============================] - 2s 8ms/step - loss: 0.1590 - accuracy: 0.9502 - val_loss: 0.1958 - val_accuracy: 0.9442 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 295/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1585 - accuracy: 0.9503 - val_loss: 0.1955 - val_accuracy: 0.9443 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 296/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1581 - accuracy: 0.9504 - val_loss: 0.1951 - val_accuracy: 0.9440 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 297/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1576 - accuracy: 0.9506 - val_loss: 0.1947 - val_accuracy: 0.9443 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 298/500 235/235 [==============================] - 2s 10ms/step - loss: 0.1572 - accuracy: 0.9507 - val_loss: 0.1943 - val_accuracy: 0.9445 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 299/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1568 - accuracy: 0.9508 - val_loss: 0.1940 - val_accuracy: 0.9447 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 300/500 235/235 [==============================] - 2s 9ms/step - loss: 0.1564 - accuracy: 0.9510 - val_loss: 0.1937 - val_accuracy: 0.9450 [0. 0. 0. ... 0. 0. 0.] Sparsity at: 0.9469890021459227 Epoch 301/500 235/235 [==============================] - 2s 9ms/step - loss: 0.8039 - accuracy: 0.7437 - val_loss: 0.6718 - val_accuracy: 0.7878 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 302/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6515 - accuracy: 0.7914 - val_loss: 0.6309 - val_accuracy: 0.8007 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 303/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6219 - accuracy: 0.8023 - val_loss: 0.6093 - val_accuracy: 0.8091 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 304/500 235/235 [==============================] - 2s 9ms/step - loss: 0.6035 - accuracy: 0.8079 - val_loss: 0.5937 - val_accuracy: 0.8158 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 305/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5895 - accuracy: 0.8123 - val_loss: 0.5815 - val_accuracy: 0.8194 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 306/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5780 - accuracy: 0.8160 - val_loss: 0.5716 - val_accuracy: 0.8223 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 307/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5683 - accuracy: 0.8192 - val_loss: 0.5636 - val_accuracy: 0.8238 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 308/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5603 - accuracy: 0.8212 - val_loss: 0.5569 - val_accuracy: 0.8245 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 309/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5533 - accuracy: 0.8227 - val_loss: 0.5510 - val_accuracy: 0.8264 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 310/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5466 - accuracy: 0.8247 - val_loss: 0.5455 - val_accuracy: 0.8283 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 311/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5402 - accuracy: 0.8269 - val_loss: 0.5405 - val_accuracy: 0.8297 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 312/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5342 - accuracy: 0.8290 - val_loss: 0.5361 - val_accuracy: 0.8302 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 313/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5289 - accuracy: 0.8303 - val_loss: 0.5321 - val_accuracy: 0.8316 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 314/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5245 - accuracy: 0.8309 - val_loss: 0.5287 - val_accuracy: 0.8338 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 315/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5208 - accuracy: 0.8320 - val_loss: 0.5256 - val_accuracy: 0.8353 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 316/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5174 - accuracy: 0.8338 - val_loss: 0.5227 - val_accuracy: 0.8365 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 317/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5141 - accuracy: 0.8349 - val_loss: 0.5198 - val_accuracy: 0.8371 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 318/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5112 - accuracy: 0.8360 - val_loss: 0.5171 - val_accuracy: 0.8383 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 319/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5085 - accuracy: 0.8371 - val_loss: 0.5144 - val_accuracy: 0.8391 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 320/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5061 - accuracy: 0.8376 - val_loss: 0.5119 - val_accuracy: 0.8403 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 321/500 235/235 [==============================] - 2s 8ms/step - loss: 0.5037 - accuracy: 0.8386 - val_loss: 0.5095 - val_accuracy: 0.8409 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 322/500 235/235 [==============================] - 2s 9ms/step - loss: 0.5016 - accuracy: 0.8393 - val_loss: 0.5073 - val_accuracy: 0.8414 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 323/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4995 - accuracy: 0.8401 - val_loss: 0.5053 - val_accuracy: 0.8415 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 324/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4977 - accuracy: 0.8410 - val_loss: 0.5035 - val_accuracy: 0.8421 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 325/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4959 - accuracy: 0.8416 - val_loss: 0.5018 - val_accuracy: 0.8426 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 326/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4943 - accuracy: 0.8420 - val_loss: 0.5004 - val_accuracy: 0.8439 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 327/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4929 - accuracy: 0.8429 - val_loss: 0.4990 - val_accuracy: 0.8444 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 328/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4915 - accuracy: 0.8431 - val_loss: 0.4978 - val_accuracy: 0.8450 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 329/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4904 - accuracy: 0.8437 - val_loss: 0.4967 - val_accuracy: 0.8458 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 330/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4892 - accuracy: 0.8440 - val_loss: 0.4957 - val_accuracy: 0.8464 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 331/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4882 - accuracy: 0.8442 - val_loss: 0.4947 - val_accuracy: 0.8470 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 332/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4872 - accuracy: 0.8445 - val_loss: 0.4938 - val_accuracy: 0.8475 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 333/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4863 - accuracy: 0.8446 - val_loss: 0.4930 - val_accuracy: 0.8475 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 334/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4854 - accuracy: 0.8450 - val_loss: 0.4922 - val_accuracy: 0.8475 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 335/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4846 - accuracy: 0.8452 - val_loss: 0.4915 - val_accuracy: 0.8475 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 336/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4839 - accuracy: 0.8454 - val_loss: 0.4909 - val_accuracy: 0.8477 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 337/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4832 - accuracy: 0.8457 - val_loss: 0.4902 - val_accuracy: 0.8480 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 338/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4825 - accuracy: 0.8458 - val_loss: 0.4896 - val_accuracy: 0.8483 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 339/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4818 - accuracy: 0.8460 - val_loss: 0.4891 - val_accuracy: 0.8479 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 340/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4812 - accuracy: 0.8462 - val_loss: 0.4885 - val_accuracy: 0.8479 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 341/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4806 - accuracy: 0.8465 - val_loss: 0.4880 - val_accuracy: 0.8478 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 342/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4801 - accuracy: 0.8469 - val_loss: 0.4874 - val_accuracy: 0.8482 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 343/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4795 - accuracy: 0.8469 - val_loss: 0.4870 - val_accuracy: 0.8482 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 344/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4790 - accuracy: 0.8471 - val_loss: 0.4865 - val_accuracy: 0.8480 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 345/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4785 - accuracy: 0.8472 - val_loss: 0.4861 - val_accuracy: 0.8479 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 346/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4780 - accuracy: 0.8474 - val_loss: 0.4857 - val_accuracy: 0.8478 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 347/500 235/235 [==============================] - 2s 8ms/step - loss: 0.4775 - accuracy: 0.8475 - val_loss: 0.4853 - val_accuracy: 0.8481 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 348/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4771 - accuracy: 0.8476 - val_loss: 0.4849 - val_accuracy: 0.8480 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 349/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4767 - accuracy: 0.8476 - val_loss: 0.4846 - val_accuracy: 0.8485 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 350/500 235/235 [==============================] - 2s 9ms/step - loss: 0.4762 - accuracy: 0.8478 - val_loss: 0.4843 - val_accuracy: 0.8488 [ 0. 0. 0. ... 0. -0. 0.] Sparsity at: 0.9718515289699571 Epoch 351/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5502 - accuracy: 0.5111 - val_loss: 1.3582 - val_accuracy: 0.5502 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 352/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3599 - accuracy: 0.5463 - val_loss: 1.3264 - val_accuracy: 0.5555 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 353/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3401 - accuracy: 0.5501 - val_loss: 1.3149 - val_accuracy: 0.5579 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9846097103004292 Epoch 354/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3309 - accuracy: 0.5516 - val_loss: 1.3087 - val_accuracy: 0.5598 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 355/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3253 - accuracy: 0.5521 - val_loss: 1.3047 - val_accuracy: 0.5613 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 356/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3213 - accuracy: 0.5533 - val_loss: 1.3017 - val_accuracy: 0.5623 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 357/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3182 - accuracy: 0.5538 - val_loss: 1.2992 - val_accuracy: 0.5625 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 358/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3155 - accuracy: 0.5547 - val_loss: 1.2971 - val_accuracy: 0.5637 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 359/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3132 - accuracy: 0.5550 - val_loss: 1.2954 - val_accuracy: 0.5638 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 360/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3113 - accuracy: 0.5558 - val_loss: 1.2939 - val_accuracy: 0.5644 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 361/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3096 - accuracy: 0.5566 - val_loss: 1.2927 - val_accuracy: 0.5648 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 362/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3082 - accuracy: 0.5575 - val_loss: 1.2916 - val_accuracy: 0.5650 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 363/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3069 - accuracy: 0.5575 - val_loss: 1.2907 - val_accuracy: 0.5654 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 364/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3058 - accuracy: 0.5580 - val_loss: 1.2898 - val_accuracy: 0.5658 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 365/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3047 - accuracy: 0.5584 - val_loss: 1.2891 - val_accuracy: 0.5660 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 366/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3038 - accuracy: 0.5590 - val_loss: 1.2884 - val_accuracy: 0.5669 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 367/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3029 - accuracy: 0.5595 - val_loss: 1.2878 - val_accuracy: 0.5678 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 368/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3021 - accuracy: 0.5598 - val_loss: 1.2872 - val_accuracy: 0.5679 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 369/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3014 - accuracy: 0.5602 - val_loss: 1.2867 - val_accuracy: 0.5683 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 370/500 235/235 [==============================] - 2s 9ms/step - loss: 1.3007 - accuracy: 0.5606 - val_loss: 1.2862 - val_accuracy: 0.5681 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 371/500 235/235 [==============================] - 2s 8ms/step - loss: 1.3001 - accuracy: 0.5610 - val_loss: 1.2858 - val_accuracy: 0.5685 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 372/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2996 - accuracy: 0.5611 - val_loss: 1.2854 - val_accuracy: 0.5685 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 373/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2991 - accuracy: 0.5616 - val_loss: 1.2851 - val_accuracy: 0.5686 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 374/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2986 - accuracy: 0.5619 - val_loss: 1.2847 - val_accuracy: 0.5691 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 375/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2982 - accuracy: 0.5619 - val_loss: 1.2844 - val_accuracy: 0.5692 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 376/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2978 - accuracy: 0.5620 - val_loss: 1.2841 - val_accuracy: 0.5691 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 377/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2975 - accuracy: 0.5622 - val_loss: 1.2839 - val_accuracy: 0.5693 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 378/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2971 - accuracy: 0.5623 - val_loss: 1.2836 - val_accuracy: 0.5692 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 379/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2967 - accuracy: 0.5624 - val_loss: 1.2833 - val_accuracy: 0.5691 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 380/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2964 - accuracy: 0.5627 - val_loss: 1.2831 - val_accuracy: 0.5695 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 381/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2961 - accuracy: 0.5628 - val_loss: 1.2828 - val_accuracy: 0.5699 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 382/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2957 - accuracy: 0.5630 - val_loss: 1.2826 - val_accuracy: 0.5700 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 383/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2954 - accuracy: 0.5631 - val_loss: 1.2824 - val_accuracy: 0.5699 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 384/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2951 - accuracy: 0.5630 - val_loss: 1.2822 - val_accuracy: 0.5701 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 385/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2948 - accuracy: 0.5629 - val_loss: 1.2820 - val_accuracy: 0.5701 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 386/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2945 - accuracy: 0.5630 - val_loss: 1.2818 - val_accuracy: 0.5704 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 387/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2942 - accuracy: 0.5631 - val_loss: 1.2816 - val_accuracy: 0.5703 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 388/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2939 - accuracy: 0.5632 - val_loss: 1.2814 - val_accuracy: 0.5706 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 389/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2936 - accuracy: 0.5634 - val_loss: 1.2812 - val_accuracy: 0.5707 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 390/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2934 - accuracy: 0.5634 - val_loss: 1.2810 - val_accuracy: 0.5707 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 391/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2931 - accuracy: 0.5636 - val_loss: 1.2808 - val_accuracy: 0.5709 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 392/500 235/235 [==============================] - 2s 9ms/step - loss: 1.2928 - accuracy: 0.5636 - val_loss: 1.2806 - val_accuracy: 0.5708 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 393/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2925 - accuracy: 0.5635 - val_loss: 1.2805 - val_accuracy: 0.5710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 394/500 235/235 [==============================] - 2s 8ms/step - loss: 1.2923 - accuracy: 0.5638 - val_loss: 1.2803 - val_accuracy: 0.5710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 395/500 235/235 [==============================] - 2s 7ms/step - loss: 1.2920 - accuracy: 0.5638 - val_loss: 1.2801 - val_accuracy: 0.5709 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 396/500 235/235 [==============================] - 2s 7ms/step - loss: 1.2918 - accuracy: 0.5638 - val_loss: 1.2800 - val_accuracy: 0.5709 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 397/500 235/235 [==============================] - 2s 7ms/step - loss: 1.2915 - accuracy: 0.5638 - val_loss: 1.2798 - val_accuracy: 0.5710 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 398/500 235/235 [==============================] - 2s 7ms/step - loss: 1.2913 - accuracy: 0.5639 - val_loss: 1.2796 - val_accuracy: 0.5711 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 399/500 235/235 [==============================] - 2s 7ms/step - loss: 1.2911 - accuracy: 0.5639 - val_loss: 1.2795 - val_accuracy: 0.5712 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9846097103004292 Epoch 400/500 235/235 [==============================] - 2s 7ms/step - loss: 1.2908 - accuracy: 0.5640 - val_loss: 1.2793 - val_accuracy: 0.5711 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9846097103004292 Epoch 401/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5670 - accuracy: 0.4518 - val_loss: 1.5330 - val_accuracy: 0.4625 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 402/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5355 - accuracy: 0.4424 - val_loss: 1.5285 - val_accuracy: 0.4410 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 403/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5328 - accuracy: 0.4365 - val_loss: 1.5270 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 404/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5316 - accuracy: 0.4367 - val_loss: 1.5262 - val_accuracy: 0.4402 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 405/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5308 - accuracy: 0.4368 - val_loss: 1.5256 - val_accuracy: 0.4398 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 406/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5302 - accuracy: 0.4370 - val_loss: 1.5251 - val_accuracy: 0.4399 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 407/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5297 - accuracy: 0.4373 - val_loss: 1.5246 - val_accuracy: 0.4401 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 408/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5292 - accuracy: 0.4374 - val_loss: 1.5243 - val_accuracy: 0.4403 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 409/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5288 - accuracy: 0.4376 - val_loss: 1.5239 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 410/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5284 - accuracy: 0.4375 - val_loss: 1.5237 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 411/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5281 - accuracy: 0.4377 - val_loss: 1.5234 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 412/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5277 - accuracy: 0.4376 - val_loss: 1.5231 - val_accuracy: 0.4410 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 413/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5274 - accuracy: 0.4375 - val_loss: 1.5228 - val_accuracy: 0.4404 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 414/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5270 - accuracy: 0.4378 - val_loss: 1.5225 - val_accuracy: 0.4404 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 415/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5267 - accuracy: 0.4378 - val_loss: 1.5222 - val_accuracy: 0.4426 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 416/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5264 - accuracy: 0.4381 - val_loss: 1.5220 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 417/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5261 - accuracy: 0.4375 - val_loss: 1.5218 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 418/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5259 - accuracy: 0.4377 - val_loss: 1.5216 - val_accuracy: 0.4425 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 419/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5257 - accuracy: 0.4376 - val_loss: 1.5215 - val_accuracy: 0.4424 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 420/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5255 - accuracy: 0.4376 - val_loss: 1.5212 - val_accuracy: 0.4403 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 421/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5253 - accuracy: 0.4377 - val_loss: 1.5211 - val_accuracy: 0.4403 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 422/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5252 - accuracy: 0.4378 - val_loss: 1.5209 - val_accuracy: 0.4401 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 423/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5250 - accuracy: 0.4380 - val_loss: 1.5208 - val_accuracy: 0.4403 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 424/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5249 - accuracy: 0.4376 - val_loss: 1.5208 - val_accuracy: 0.4404 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 425/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5248 - accuracy: 0.4378 - val_loss: 1.5206 - val_accuracy: 0.4404 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 426/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5247 - accuracy: 0.4377 - val_loss: 1.5205 - val_accuracy: 0.4403 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 427/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5246 - accuracy: 0.4379 - val_loss: 1.5204 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 428/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5244 - accuracy: 0.4375 - val_loss: 1.5203 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 429/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5243 - accuracy: 0.4373 - val_loss: 1.5201 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 430/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5242 - accuracy: 0.4377 - val_loss: 1.5201 - val_accuracy: 0.4407 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 431/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5241 - accuracy: 0.4379 - val_loss: 1.5200 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 432/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5240 - accuracy: 0.4380 - val_loss: 1.5199 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 433/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5239 - accuracy: 0.4378 - val_loss: 1.5198 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 434/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5238 - accuracy: 0.4377 - val_loss: 1.5198 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 435/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5237 - accuracy: 0.4378 - val_loss: 1.5197 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 436/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5237 - accuracy: 0.4379 - val_loss: 1.5196 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 437/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5236 - accuracy: 0.4379 - val_loss: 1.5195 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 438/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5235 - accuracy: 0.4380 - val_loss: 1.5194 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 439/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5234 - accuracy: 0.4378 - val_loss: 1.5193 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 440/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5233 - accuracy: 0.4381 - val_loss: 1.5193 - val_accuracy: 0.4405 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 441/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5232 - accuracy: 0.4382 - val_loss: 1.5192 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 442/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5232 - accuracy: 0.4380 - val_loss: 1.5192 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 443/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5231 - accuracy: 0.4380 - val_loss: 1.5191 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 444/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5230 - accuracy: 0.4379 - val_loss: 1.5190 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 445/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5230 - accuracy: 0.4381 - val_loss: 1.5190 - val_accuracy: 0.4407 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 446/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5229 - accuracy: 0.4385 - val_loss: 1.5190 - val_accuracy: 0.4426 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 447/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5228 - accuracy: 0.4385 - val_loss: 1.5189 - val_accuracy: 0.4406 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 448/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5227 - accuracy: 0.4385 - val_loss: 1.5188 - val_accuracy: 0.4407 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 449/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5227 - accuracy: 0.4380 - val_loss: 1.5188 - val_accuracy: 0.4407 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 450/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5226 - accuracy: 0.4383 - val_loss: 1.5187 - val_accuracy: 0.4409 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 451/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5226 - accuracy: 0.4386 - val_loss: 1.5187 - val_accuracy: 0.4410 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 452/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5225 - accuracy: 0.4381 - val_loss: 1.5186 - val_accuracy: 0.4413 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 453/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5225 - accuracy: 0.4381 - val_loss: 1.5185 - val_accuracy: 0.4409 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 454/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5224 - accuracy: 0.4383 - val_loss: 1.5185 - val_accuracy: 0.4413 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 455/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5224 - accuracy: 0.4384 - val_loss: 1.5185 - val_accuracy: 0.4413 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 456/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5223 - accuracy: 0.4383 - val_loss: 1.5184 - val_accuracy: 0.4413 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 457/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5222 - accuracy: 0.4388 - val_loss: 1.5183 - val_accuracy: 0.4413 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 458/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5222 - accuracy: 0.4385 - val_loss: 1.5183 - val_accuracy: 0.4414 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 459/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5222 - accuracy: 0.4385 - val_loss: 1.5182 - val_accuracy: 0.4414 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 460/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5221 - accuracy: 0.4385 - val_loss: 1.5182 - val_accuracy: 0.4414 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 461/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5221 - accuracy: 0.4383 - val_loss: 1.5182 - val_accuracy: 0.4417 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 462/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5220 - accuracy: 0.4385 - val_loss: 1.5181 - val_accuracy: 0.4415 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 463/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5220 - accuracy: 0.4382 - val_loss: 1.5181 - val_accuracy: 0.4416 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 464/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5219 - accuracy: 0.4388 - val_loss: 1.5181 - val_accuracy: 0.4417 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 465/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5219 - accuracy: 0.4385 - val_loss: 1.5181 - val_accuracy: 0.4417 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 466/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5219 - accuracy: 0.4387 - val_loss: 1.5180 - val_accuracy: 0.4416 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 467/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5218 - accuracy: 0.4385 - val_loss: 1.5180 - val_accuracy: 0.4416 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 468/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5218 - accuracy: 0.4385 - val_loss: 1.5179 - val_accuracy: 0.4419 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 469/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5217 - accuracy: 0.4383 - val_loss: 1.5179 - val_accuracy: 0.4419 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 470/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5217 - accuracy: 0.4387 - val_loss: 1.5178 - val_accuracy: 0.4418 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 471/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5217 - accuracy: 0.4385 - val_loss: 1.5179 - val_accuracy: 0.4419 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 472/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5216 - accuracy: 0.4385 - val_loss: 1.5179 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 473/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5216 - accuracy: 0.4388 - val_loss: 1.5178 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 474/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5216 - accuracy: 0.4386 - val_loss: 1.5178 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 475/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5215 - accuracy: 0.4387 - val_loss: 1.5178 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 476/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5215 - accuracy: 0.4388 - val_loss: 1.5177 - val_accuracy: 0.4419 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 477/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5215 - accuracy: 0.4385 - val_loss: 1.5177 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 478/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5215 - accuracy: 0.4387 - val_loss: 1.5177 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 479/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5214 - accuracy: 0.4388 - val_loss: 1.5177 - val_accuracy: 0.4436 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 480/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5214 - accuracy: 0.4387 - val_loss: 1.5177 - val_accuracy: 0.4418 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 481/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5214 - accuracy: 0.4385 - val_loss: 1.5176 - val_accuracy: 0.4418 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 482/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5213 - accuracy: 0.4389 - val_loss: 1.5176 - val_accuracy: 0.4436 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 483/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5213 - accuracy: 0.4390 - val_loss: 1.5176 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 484/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5213 - accuracy: 0.4389 - val_loss: 1.5176 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 485/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5213 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. 0.] Sparsity at: 0.9893374463519313 Epoch 486/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5213 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 487/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5212 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 488/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5212 - accuracy: 0.4387 - val_loss: 1.5176 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 489/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5212 - accuracy: 0.4391 - val_loss: 1.5175 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. -0. 0.] Sparsity at: 0.9893374463519313 Epoch 490/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5212 - accuracy: 0.4386 - val_loss: 1.5175 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 491/500 235/235 [==============================] - 3s 12ms/step - loss: 1.5211 - accuracy: 0.4387 - val_loss: 1.5175 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 492/500 235/235 [==============================] - 2s 7ms/step - loss: 1.5211 - accuracy: 0.4389 - val_loss: 1.5175 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 493/500 235/235 [==============================] - 2s 8ms/step - loss: 1.5211 - accuracy: 0.4389 - val_loss: 1.5175 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 494/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5211 - accuracy: 0.4390 - val_loss: 1.5175 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 495/500 235/235 [==============================] - 2s 10ms/step - loss: 1.5211 - accuracy: 0.4389 - val_loss: 1.5175 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 496/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4387 - val_loss: 1.5174 - val_accuracy: 0.4438 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 497/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4390 - val_loss: 1.5174 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 498/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4390 - val_loss: 1.5174 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. -0. -0.] Sparsity at: 0.9893374463519313 Epoch 499/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5210 - accuracy: 0.4388 - val_loss: 1.5175 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313 Epoch 500/500 235/235 [==============================] - 2s 9ms/step - loss: 1.5209 - accuracy: 0.4391 - val_loss: 1.5175 - val_accuracy: 0.4437 [ 0. 0. 0. ... -0. 0. -0.] Sparsity at: 0.9893374463519313
magnitude_histories
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with open('output/neural-network-pruning/pickle-jar/magnitude_histories'+str(j)+'.pickle', 'wb') as file:
pickle.dump(magnitude_histories, file)